Running head Multi-actor modelling system 1Multi-actor mod.docx

Running head: Multi-actor modelling system 1
Multi-actor modelling system3
Multi-actor modelling system
Yogesh Dagwale
University of the Cumberland’s
Ligtenberg, A., Wachowicz, M., Bregt, A. K., Beulens, A., &
Kettenis, D. L. (2004). A design and application of a multi-
agent system for simulation of multi-actor spatial planning.
Journal of environmental management, 72(1-2), 43-55.
They talk about the potential and restrictions of the MAS to
manufacture models that empower spatial organizers to
incorporate the 'actor factor' in their examination. Their
structure system contemplates actors who assume a functioning
job in the spatial planning. They included actors who can watch
and see a spatial domain. Using these perceptions and
discernment they produce an inclination for a preferred spatial

situation. Actors at that point present and discuss their
inclinations amid their exchanges with different actors.
The inclinations of the actor fill in as inputs for an official
choice making. Finally, ultimate conclusions are actualized in
the spatial framework. They found that MAS can produce space
utilization designs in light of a portrayal of a multi-actor
planning process. It additionally can clear up the impacts of
actors under the administration of various planning styles on the
space utilization and prove how the relations between actors
change amid a planning process and under different orders of
coming up with decisions. Unlike the work by Parker, Manson,
Janssen, Hoffman & Deadman,2003, cited below, this paper did
not include the various challenges associated with the use of
MAS.
Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J.,
& Deadman, P. (2003). Multi-agent systems for the simulation
of land-use and land-cover change: a review. Annals of the
association of American Geographers, 93(2), 314-337.
In this paper, they studied different models. These models,
however, were not thorough enough and therefore they took into
account the multi-actor system, dynamic spatial Simulation,
which has two components, that is, a cellular model that speaks
to biogeophysical and biological parts of a demonstrated
framework and an actor-based model to speak to human
conclusion making. Because of its nature and ability to model
complex situations, they highlighted some of the areas that
MAS can be applied where other models cannot be able to
deliver. Such areas are modeling of emergent phenomena
whereby MAS can model landscape plans, due to its flexibility,
MAS can represent complex land use/ cover systems, and they
can be used to model dynamic paths. They also outlined the
various challenges to Multi-actor systems. Such challenges
include an understanding of complexity, individual decision
making, empirical parameterization and model validation, and
communication.

Faber, N. R., & Jorna, R. J. (2011, June). The use of multi-actor
systems for studying social sustainability: Theoretical
backgrounds and pseudo-specifications. In Computer Supported
Cooperative Work in Design (CSCWD), 2011 15th International
Conference on (pp. 842-849). IEEE.
This paper addresses the need to use multi-actor simulations for
examining social sustainability. They contend that simulations
for maintainability should join conceivable psychological and
social agents to acquire knowledge on the issues identified with
manageability. Such agents should be furnished with elements
of observation, portability, learning, correspondence, and
coordination to frame a simulation demonstrate for social
maintainability. The actors impart their situations toward an
issue and coordinate each other's activities keeping in mind the
end goal to achieve. the actors confer their circumstances
toward an issue and coordinate each other's exercises
remembering the true objective to achieve. They argued that the
MAS should be nourished with at least one aptitude models that
mirror a specific learning space for the on-screen characters to
communicate with. For by definition undertakings and practices
of actors are formed by the ability these actors have.
They described that MAS can be used for local characteristics,
that is, recognition and natural thoughtfulness to achieve
versatility and investigation of neighborhood contrasts. It can
moreover be used for change of learning instruments and
correspondence parts to enhance data creation and basic
reasoning. In social components, MAS utilizes coordination
segments to oversee undertaking dissemination/reallocation.
Gazendam, H. W. (2005). Coordination mechanisms in multi-
actor systems. Planning: Its aspects, motivations, and methods.
New York: Wiley.
This paper explains that, in multi-actor frameworks, facilitated
activity is accomplished by procedures of shared alteration that
can appear as sorting out, planning, and impromptu creation. An
arrangement can be viewed as a social build, a moderately
persevering socially shared unit of information, fortified in its

reality by its day by day utilize. Keeping in mind the end goal
to have the capacity to comprehend multi-actor plan, the paper
researches the inquiries; what representation composes are
important in multi-actor participation, how actors can
accomplish coordinated activity utilizing social develops, how
the arranging exercises of numerous performing artists can be
made good with a specific end goal to come to a worthy,
perhaps circulated, organizing plan, and how actors can define
limits to the time and assets spent on planning in view of an
estimation of the expenses and advantages of making
arrangements. They concluded that multi-actor systems can be
used for the development of coherent theories.
Le, Q. B., Park, S. J., Vlek, P. L., & Cremers, A. B. (2008).
Land-Use Dynamic Simulator (LUDAS): A multi-agent system
model for simulating spatio-temporal dynamics of coupled
human–landscape system. I. Structure and theoretical
specification. Ecological Informatics, 3(2), 135-153.
This paper displays the idea and hypothetical determination of a
multi actor-based model for the spatial-transient recreation of a
coupled human– scene framework. The model falls into the
class, where the human populace and the scene condition are on
the whole self-sorted out intelligent specialists. The model
structure is spoken to by four segments: an arrangement of
human populace characterizing particular personal conduct
standards of ranch family units in arrive utilize basic leadership
as indicated by typological vocation gatherings, an arrangement
of scene condition portraying singular land patches with
different characteristics, speaking to the elements of harvest and
backwoods yields and also arrive utilize/cover advances in light
of family conduct and regular imperatives, an arrangement of
approach factors that are essential for arriving utilize decisions,
and a basic leadership technique incorporating family,
ecological and strategy data into arrive utilize choices of family
operators. In the model, the limited judicious methodology, in

view of utility boost utilizing spatial multi-ostensible strategic
capacities, is settled with heuristic lead-based strategies to
speak to basic leadership instruments of families in regard to
arriving utilize. Experimental confirmations of the model's
segments and the use of the model to a watershed in Vietnam
for coordinated evaluations of approach impacts on the scene
and network elements are subjects of a friend paper.
Public Administration and Information
Technology
Volume 10
Series Editor
Christopher G. Reddick
San Antonio, Texas, USA
[email protected]
More information about this series at
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[email protected]
Marijn Janssen • Maria A. Wimmer
Ameneh Deljoo

Editors
Policy Practice and Digital
Science
Integrating Complex Systems, Social
Simulation and Public Administration
in Policy Research
2123
[email protected]
Editors
Marijn Janssen Ameneh Deljoo
Faculty of Technology, Policy, and Faculty of Technology,
Policy, and
Management Management
Delft University of Technology Delft University of Technology
Delft Delft
The Netherlands The Netherlands
Maria A. Wimmer
Institute for Information Systems Research
University of Koblenz-Landau
Koblenz
Germany
ISBN 978-3-319-12783-5 ISBN 978-3-319-12784-2 (eBook)
Public Administration and Information Technology
DOI 10.1007/978-3-319-12784-2
Library of Congress Control Number: 2014956771

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Preface

The last economic and financial crisis has heavily threatened
European and other
economies around the globe. Also, the Eurozone crisis, the
energy and climate
change crises, challenges of demographic change with high
unemployment rates,
and the most recent conflicts in the Ukraine and the near East or
the Ebola virus
disease in Africa threaten the wealth of our societies in
different ways. The inability
to predict or rapidly deal with dramatic changes and negative
trends in our economies
and societies can seriously hamper the wealth and prosperity of
the European Union
and its Member States as well as the global networks. These
societal and economic
challenges demonstrate an urgent need for more effective and
efficient processes of
governance and policymaking, therewith specifically addressing
crisis management
and economic/welfare impact reduction.
Therefore, investing in the exploitation of innovative
information and commu-
nication technology (ICT) in the support of good governance
and policy modeling
has become a major effort of the European Union to position
itself and its Member
States well in the global digital economy. In this realm, the
European Union has
laid out clear strategic policy objectives for 2020 in the Europe
2020 strategy1: In
a changing world, we want the EU to become a smart,
sustainable, and inclusive
economy. These three mutually reinforcing priorities should
help the EU and the

Member States deliver high levels of employment, productivity,
and social cohesion.
Concretely, the Union has set five ambitious objectives—on
employment, innovation,
education, social inclusion, and climate/energy—to be reached
by 2020. Along with
this, Europe 2020 has established four priority areas—smart
growth, sustainable
growth, inclusive growth, and later added: A strong and
effective system of eco-
nomic governance—designed to help Europe emerge from the
crisis stronger and to
coordinate policy actions between the EU and national levels.
To specifically support European research in strengthening
capacities, in overcom-
ing fragmented research in the field of policymaking, and in
advancing solutions for
1 Europe 2020 http://ec.europa.eu/europe2020/index_en.htm
v
[email protected]
vi Preface
ICT supported governance and policy modeling, the European
Commission has co-
funded an international support action called eGovPoliNet2. The
overall objective
of eGovPoliNet was to create an international, cross-
disciplinary community of re-
searchers working on ICT solutions for governance and policy

modeling. In turn,
the aim of this community was to advance and sustain research
and to share the
insights gleaned from experiences in Europe and globally. To
achieve this, eGovPo-
liNet established a dialogue, brought together experts from
distinct disciplines, and
collected and analyzed knowledge assets (i.e., theories,
concepts, solutions, findings,
and lessons on ICT solutions in the field) from different
research disciplines. It built
on case material accumulated by leading actors coming from
distinct disciplinary
backgrounds and brought together the innovative knowledge in
the field. Tools, meth-
ods, and cases were drawn from the academic community, the
ICT sector, specialized
policy consulting firms as well as from policymakers and
governance experts. These
results were assembled in a knowledge base and analyzed in
order to produce com-
parative analyses and descriptions of cases, tools, and scientific
approaches to enrich
a common knowledge base accessible via www.policy-
community.eu.
This book, entitled “Policy Practice and Digital Science—
Integrating Complex
Systems, Social Simulation, and Public Administration in Policy
Research,” is one
of the exciting results of the activities of eGovPoliNet—fusing
community building
activities and activities of knowledge analysis. It documents
findings of comparative
analyses and brings in experiences of experts from academia
and from case descrip-

tions from all over the globe. Specifically, it demonstrates how
the explosive growth
in data, computational power, and social media creates new
opportunities for policy-
making and research. The book provides a first comprehensive
look on how to take
advantage of the development in the digital world with new
approaches, concepts,
instruments, and methods to deal with societal and
computational complexity. This
requires the knowledge traditionally found in different
disciplines including public
administration, policy analyses, information systems, complex
systems, and com-
puter science to work together in a multidisciplinary fashion
and to share approaches.
This book provides the foundation for strongly multidisciplinary
research, in which
the various developments and disciplines work together from a
comprehensive and
holistic policymaking perspective. A wide range of aspects for
social and professional
networking and multidisciplinary constituency building along
the axes of technol-
ogy, participative processes, governance, policy modeling,
social simulation, and
visualization are tackled in the 19 papers.
With this book, the project makes an effective contribution to
the overall objec-
tives of the Europe 2020 strategy by providing a better
understanding of different
approaches to ICT enabled governance and policy modeling, and
by overcoming the
fragmented research of the past. This book provides impressive
insights into various

theories, concepts, and solutions of ICT supported policy
modeling and how stake-
holders can be more actively engaged in public policymaking. It
draws conclusions
2 eGovPoliNet is cofunded under FP 7, Call identifier FP7-ICT-
2011-7, URL: www.policy-
community.eu
[email protected]
Preface vii
of how joint multidisciplinary research can bring more effective
and resilient find-
ings for better predicting dramatic changes and negative trends
in our economies and
societies.
It is my great pleasure to provide the preface to the book
resulting from the
eGovPoliNet project. This book presents stimulating research by
researchers coming
from all over Europe and beyond. Congratulations to the project
partners and to the
authors!—Enjoy reading!
Thanassis Chrissafis
Project officer of eGovPoliNet
European Commission
DG CNECT, Excellence in Science, Digital Science
[email protected]

Contents
1 Introduction to Policy-Making in the Digital Age . . . . . . . . . .
. . . . . . . 1
Marijn Janssen and Maria A. Wimmer
2 Educating Public Managers and Policy Analysts
in an Era of Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 15
Christopher Koliba and Asim Zia
3 The Quality of Social Simulation: An Example from Research
Policy Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 35
Petra Ahrweiler and Nigel Gilbert
4 Policy Making and Modelling in a Complex World . . . . . . . .
. . . . . . . . 57
Wander Jager and Bruce Edmonds
5 From Building a Model to Adaptive Robust Decision Making
Using Systems Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 75
Erik Pruyt
6 Features and Added Value of Simulation Models Using
Different
Modelling Approaches Supporting Policy-Making: A
Comparative
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 95
Dragana Majstorovic, Maria A.Wimmer, Roy Lay-Yee, Peter
Davis
and Petra Ahrweiler

7 A Comparative Analysis of Tools and Technologies
for Policy Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 125
Eleni Kamateri, Eleni Panopoulou, Efthimios Tambouris,
Konstantinos Tarabanis, Adegboyega Ojo, Deirdre Lee
and David Price
8 Value Sensitive Design of Complex Product Systems . . . . . . .
. . . . . . . . 157
Andreas Ligtvoet, Geerten van de Kaa, Theo Fens, Cees van
Beers,
Paulier Herder and Jeroen van den Hoven
ix
[email protected]
x Contents
9 Stakeholder Engagement in Policy Development: Observations
and Lessons from International Experience . . . . . . . . . . . . . . . .
. . . . . . 177
Natalie Helbig, Sharon Dawes, Zamira Dzhusupova, Bram
Klievink
and Catherine Gerald Mkude
10 Values in Computational Models Revalued . . . . . . . . . . . . .
. . . . . . . . . . 205
Rebecca Moody and Lasse Gerrits
11 The Psychological Drivers of Bureaucracy: Protecting
the Societal Goals of an Organization . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 221

Tjeerd C. Andringa
12 Active and Passive Crowdsourcing in Government . . . . . . . .
. . . . . . . . 261
Euripidis Loukis and Yannis Charalabidis
13 Management of Complex Systems: Toward Agent-Based
Gaming for Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 291
Wander Jager and Gerben van der Vegt
14 The Role of Microsimulation in the Development of Public
Policy . . . 305
Roy Lay-Yee and Gerry Cotterell
15 Visual Decision Support for Policy Making: Advancing
Policy
Analysis with Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 321
Tobias Ruppert, Jens Dambruch, Michel Krämer, Tina Balke,
Marco
Gavanelli, Stefano Bragaglia, Federico Chesani, Michela
Milano
and Jörn Kohlhammer
16 Analysis of Five Policy Cases in the Field of Energy Policy .
. . . . . . . . 355
Dominik Bär, Maria A.Wimmer, Jozef Glova, Anastasia
Papazafeiropoulou and Laurence Brooks
17 Challenges to Policy-Making in Developing Countries
and the Roles of Emerging Tools, Methods and Instruments:
Experiences from Saint Petersburg . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . 379
Dmitrii Trutnev, Lyudmila Vidyasova and Andrei Chugunov

18 Sustainable Urban Development, Governance and Policy:
A Comparative Overview of EU Policies and Projects . . . . . . . .
. . . . . 393
Diego Navarra and Simona Milio
19 eParticipation, Simulation Exercise and Leadership Training
in Nigeria: Bridging the Digital Divide . . . . . . . . . . . . . . . . . .
. . . . . . . . . 417
Tanko Ahmed
[email protected]
Contributors
Tanko Ahmed National Institute for Policy and Strategic Studies
(NIPSS), Jos,
Nigeria
Petra Ahrweiler EA European Academy of Technology and
Innovation Assess-
ment GmbH, Bad Neuenahr-Ahrweiler, Germany
Tjeerd C. Andringa University College Groningen, Institute of
Artificial In-
telligence and Cognitive Engineering (ALICE), University of
Groningen, AB,
Groningen, the Netherlands
Tina Balke University of Surrey, Surrey, UK
Dominik Bär University of Koblenz-Landau, Koblenz, Germany
Cees van Beers Faculty of Technology, Policy, and
Management, Delft University

of Technology, Delft, The Netherlands
Stefano Bragaglia University of Bologna, Bologna, Italy
Laurence Brooks Brunel University, Uxbridge, UK
Yannis Charalabidis University of the Aegean, Samos, Greece
Federico Chesani University of Bologna, Bologna, Italy
Andrei Chugunov ITMO University, St. Petersburg, Russia
Gerry Cotterell Centre of Methods and Policy Application in the
Social Sciences
(COMPASS Research Centre), University of Auckland,
Auckland, New Zealand
Jens Dambruch Fraunhofer Institute for Computer Graphics
Research, Darmstadt,
Germany
Peter Davis Centre of Methods and Policy Application in the
Social Sciences
Sharon Dawes Center for Technology in Government,
University at Albany,
Albany, New York, USA
xi
[email protected]

xii Contributors
Zamira Dzhusupova Department of Public Administration and
Development Man-
agement, United Nations Department of Economic and Social
Affairs (UNDESA),
NewYork, USA
Bruce Edmonds Manchester Metropolitan University,
Manchester, UK
Theo Fens Faculty of Technology, Policy, and Management,
Delft University of
Technology, Delft, The Netherlands
Marco Gavanelli University of Ferrara, Ferrara, Italy
Lasse Gerrits Department of Public Administration, Erasmus
University
Rotterdam, Rotterdam, The Netherlands
Nigel Gilbert University of Surrey, Guildford, UK
Jozef Glova Technical University Kosice, Kosice, Slovakia
Natalie Helbig Center for Technology in Government,
University at Albany,
Albany, New York, USA
Paulier Herder Faculty of Technology, Policy, and Management,
Delft University
Jeroen van den Hoven Faculty of Technology, Policy, and
Management, Delft
University of Technology, Delft, The Netherlands

Wander Jager Groningen Center of Social Complexity Studies,
University of
Groningen, Groningen, The Netherlands
Marijn Janssen Faculty of Technology, Policy, and
Management, Delft University
Geerten van de Kaa Faculty of Technology, Policy, and
Management, Delft
University of Technology, Delft, The Netherlands
Eleni Kamateri Information Technologies Institute, Centre for
Research &
Technology—Hellas, Thessaloniki, Greece
Bram Klievink Faculty of Technology, Policy and Management,
Delft University
Jörn Kohlhammer GRIS, TU Darmstadt & Fraunhofer IGD,
Darmstadt, Germany
Christopher Koliba University of Vermont, Burlington, VT,
USA
Michel Krämer Fraunhofer Institute for Computer Graphics
Germany
Roy Lay-Yee Centre of Methods and Policy Application in the
Social Sciences

Deirdre Lee INSIGHT Centre for Data Analytics, NUIG,
Galway, Ireland
[email protected]
Contributors xiii
Andreas Ligtvoet Faculty of Technology, Policy, and
Management, Delft Univer-
sity of Technology, Delft, The Netherlands
Euripidis Loukis University of the Aegean, Samos, Greece
Dragana Majstorovic University of Koblenz-Landau, Koblenz,
Germany
Michela Milano University of Bologna, Bologna, Italy
Simona Milio London School of Economics, Houghton Street,
London, UK
Catherine Gerald Mkude Institute for IS Research, University of
Koblenz-Landau,
Koblenz, Germany
Rebecca Moody Department of Public Administration, Erasmus
University
Rotterdam, Rotterdam, The Netherlands
Diego Navarra Studio Navarra, London, UK
Adegboyega Ojo INSIGHT Centre for Data Analytics, NUIG,
Galway, Ireland

Eleni Panopoulou Information Technologies Institute, Centre
for Research &
Technology—Hellas, Thessaloniki, Greece
Anastasia Papazafeiropoulou Brunel University, Uxbridge, UK
David Price Thoughtgraph Ltd, Somerset, UK
Erik Pruyt Faculty of Technology, Policy, and Management,
Delft University of
Technology, Delft, The Netherlands; Netherlands Institute for
Advanced Study,
Wassenaar, The Netherlands
Tobias Ruppert Fraunhofer Institute for Computer Graphics
Germany
Efthimios Tambouris Information Technologies Institute, Centre
for Research &
Technology—Hellas, Thessaloniki, Greece; University of
Macedonia, Thessaloniki,
Greece
Konstantinos Tarabanis Information Technologies Institute,
Centre for Research
& Technology—Hellas, Thessaloniki, Greece; University of
Macedonia, Thessa-
loniki, Greece
Dmitrii Trutnev ITMO University, St. Petersburg, Russia
Gerben van der Vegt Faculty of Economics and Business,
University of Groningen,
Groningen, The Netherlands

Lyudmila Vidyasova ITMO University, St. Petersburg, Russia
Maria A. Wimmer University of Koblenz-Landau, Koblenz,
Germany
Asim Zia University of Vermont, Burlington, VT, USA
[email protected]
Chapter 1
Introduction to Policy-Making in the Digital Age
Marijn Janssen and Maria A. Wimmer
We are running the 21st century using 20th century systems on
top of 19th century political structures. . . .
John Pollock, contributing editor MIT technology review
Abstract The explosive growth in data, computational power,
and social media
creates new opportunities for innovating governance and policy-
making. These in-
formation and communications technology (ICT) developments
affect all parts of
the policy-making cycle and result in drastic changes in the way
policies are devel-
oped. To take advantage of these developments in the digital
world, new approaches,
concepts, instruments, and methods are needed, which are able
to deal with so-
cietal complexity and uncertainty. This field of research is
sometimes depicted
as e-government policy, e-policy, policy informatics, or data
science. Advancing

our knowledge demands that different scientific communities
collaborate to create
practice-driven knowledge. For policy-making in the digital age
disciplines such as
complex systems, social simulation, and public administration
need to be combined.
1.1 Introduction
Policy-making and its subsequent implementation is necessary
to deal with societal
problems. Policy interventions can be costly, have long-term
implications, affect
groups of citizens or even the whole country and cannot be
easily undone or are even
irreversible. New information and communications technology
(ICT) and models
can help to improve the quality of policy-makers. In particular,
the explosive growth
in data, computational power, and social media creates new
opportunities for in-
novating the processes and solutions of ICT-based policy-
making and research. To
M. Janssen (�)
Faculty of Technology, Policy, and Management, Delft
University of Technology,
Delft, The Netherlands
e-mail: [email protected]
M. A. Wimmer
University of Koblenz-Landau, Koblenz, Germany
© Springer International Publishing Switzerland 2015 1
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI
10.1007/978-3-319-12784-2_1

[email protected]
2 M. Janssen and M. A. Wimmer
take advantage of these developments in the digital world, new
approaches, con-
cepts, instruments, and methods are needed, which are able to
deal with societal and
computational complexity. This requires the use of knowledge
which is traditionally
found in different disciplines, including (but not limited to)
public administration,
policy analyses, information systems, complex systems, and
computer science. All
these knowledge areas are needed for policy-making in the
digital age. The aim of
this book is to provide a foundation for this new
interdisciplinary field in which
various traditional disciplines are blended.
Both policy-makers and those in charge of policy
implementations acknowledge
that ICT is becoming more and more important and is changing
the policy-making
process, resulting in a next generation policy-making based on
ICT support. The field
of policy-making is changing driven by developments such as
open data, computa-
tional methods for processing data, opinion mining, simulation,
and visualization of
rich data sets, all combined with public engagement, social
media, and participatory
tools. In this respect Web 2.0 and even Web 3.0 point to the

specific applications of
social networks and semantically enriched and linked data
which are important for
policy-making. In policy-making vast amount of data are used
for making predictions
and forecasts. This should result in improving the outcomes of
policy-making.
Policy-making is confronted with an increasing complexity and
uncertainty of the
outcomes which results in a need for developing policy models
that are able to deal
with this. To improve the validity of the models policy-makers
are harvesting data to
generate evidence. Furthermore, they are improving their
models to capture complex
phenomena and dealing with uncertainty and limited and
incomplete information.
Despite all these efforts, there remains often uncertainty
concerning the outcomes of
policy interventions. Given the uncertainty, often multiple
scenarios are developed
to show alternative outcomes and impact. A condition for this is
the visualization of
policy alternatives and its impact. Visualization can ensure
involvement of nonexpert
and to communicate alternatives. Furthermore, games can be
used to let people gain
insight in what can happen, given a certain scenario. Games
allow persons to interact
and to experience what happens in the future based on their
interventions.
Policy-makers are often faced with conflicting solutions to
complex problems,
thus making it necessary for them to test out their assumptions,

interventions, and
resolutions. For this reason policy-making organizations
introduce platforms facili-
tating policy-making and citizens engagements and enabling the
processing of large
volumes of data. There are various participative platforms
developed by government
agencies (e.g., De Reuver et al. 2013; Slaviero et al. 2010;
Welch 2012). Platforms
can be viewed as a kind of regulated environment that enable
developers, users, and
others to interact with each other, share data, services, and
applications, enable gov-
ernments to more easily monitor what is happening and
facilitate the development
of innovative solutions (Janssen and Estevez 2013). Platforms
should provide not
only support for complex policy deliberations with citizens but
should also bring to-
gether policy-modelers, developers, policy-makers, and other
stakeholders involved
in policy-making. In this way platforms provide an information-
rich, interactive
[email protected]
1 Introduction to Policy-Making in the Digital Age 3
environment that brings together relevant stakeholders and in
which complex phe-
nomena can be modeled, simulated, visualized, discussed, and
even the playing of
games can be facilitated.

1.2 Complexity and Uncertainty in Policy-Making
Policy-making is driven by the need to solve societal problems
and should result in
interventions to solve these societal problems. Examples of
societal problems are
unemployment, pollution, water quality, safety, criminality,
well-being, health, and
immigration. Policy-making is an ongoing process in which
issues are recognized
as a problem, alternative courses of actions are formulated,
policies are affected,
implemented, executed, and evaluated (Stewart et al. 2007).
Figure 1.1 shows the
typical stages of policy formulation, implementation, execution,
enforcement, and
evaluation. This process should not be viewed as linear as many
interactions are
necessary as well as interactions with all kind of stakeholders.
In policy-making
processes a vast amount of stakeholders are always involved,
which makes policy-
making complex.
Once a societal need is identified, a policy has to be formulated.
Politicians,
members of parliament, executive branches, courts, and interest
groups may be
involved in these formulations. Often contradictory proposals
are made, and the
impact of a proposal is difficult to determine as data is missing,
models cannot
citizen
s

Policy formulation
Policy
implementation
Policy
execution
Policy
enforcement and
evaluation
politicians
Policy-
makers
Administrative
organizations
b
u
sin
esses
Inspection and
enforcement agencies
experts
Fig. 1.1 Overview of policy cycle and stakeholders
[email protected]

capture the complexity, and the results of policy models are
difficult to interpret and
even might be interpreted in an opposing way. This is further
complicated as some
proposals might be good but cannot be implemented or are too
costly to implement.
There is a large uncertainty concerning the outcomes.
Policy implementation is done by organizations other than those
that formulated
the policy. They often have to interpret the policy and have to
make implemen-
tation decisions. Sometimes IT can block quick implementation
as systems have
to be changed. Although policy-making is the domain of the
government, private
organizations can be involved to some extent, in particular in
the execution of policies.
Once all things are ready and decisions are made, policies need
to be executed.
During the execution small changes are typically made to fine
tune the policy formu-
lation, implementation decisions might be more difficult to
realize, policies might
bring other benefits than intended, execution costs might be
higher and so on. Typ-
ically, execution is continually changing. Evaluation is part of
the policy-making
process as it is necessary to ensure that the policy-execution
solved the initial so-
cietal problem. Policies might become obsolete, might not work,

have unintended
affects (like creating bureaucracy) or might lose its support
among elected officials,
or other alternatives might pop up that are better.
Policy-making is a complex process in which many stakeholders
play a role. In
the various phases of policy-making different actors are
dominant and play a role.
Figure 1.1 shows only some actors that might be involved, and
many of them are not
included in this figure. The involvement of so many actors
results in fragmentation
and often actors are even not aware of the decisions made by
other actors. This makes
it difficult to manage a policy-making process as each actor has
other goals and might
be self-interested.
Public values (PVs) are a way to try to manage complexity and
give some guidance.
Most policies are made to adhere to certain values. Public value
management (PVM)
represents the paradigm of achieving PVs as being the primary
objective (Stoker
2006). PVM refers to the continuous assessment of the actions
performed by public
officials to ensure that these actions result in the creation of PV
(Moore 1995). Public
servants are not only responsible for following the right
procedure, but they also have
to ensure that PVs are realized. For example, civil servants
should ensure that garbage
is collected. The procedure that one a week garbage is collected
is secondary. If it is
necessary to collect garbage more (or less) frequently to ensure

a healthy environment
then this should be done. The role of managers is not only to
ensure that procedures
are followed but they should be custodians of public assets and
maximize a PV.
There exist a wide variety of PVs (Jørgensen and Bozeman
2007). PVs can be
long-lasting or might be driven by contemporary politics. For
example, equal access
is a typical long-lasting value, whereas providing support for
students at universities
is contemporary, as politicians might give more, less, or no
support to students. PVs
differ over times, but also the emphasis on values is different in
the policy-making
cycle as shown in Fig. 1.2. In this figure some of the values
presented by Jørgensen
and Bozeman (2007) are mapped onto the four policy-making
stages. Dependent on
the problem at hand other values might play a role that is not
included in this figure.
[email protected]
Policy
formulation
Policy
implementation
Policy

execution
Policy
enforcement
and evaluation
efficiency
efficiency
accountability
transparancy
responsiveness
public interest
will of the people
listening
citizen involvement
evidence-based
protection of
individual rights
accountability
transparancy
evidence-based

equal access
balancing of interests
robust
honesty
fair
timelessness
reliable
flexible
fair
Fig. 1.2 Public values in the policy cycle
Policy is often formulated by politicians in consultation with
experts. In the PVM
paradigm, public administrations aim at creating PVs for society
and citizens. This
suggests a shift from talking about what citizens expect in
creating a PV. In this view
public officials should focus on collaborating and creating a
dialogue with citizens
in order to determine what constitutes a PV.
1.3 Developments
There is an infusion of technology that changes policy processes
at both the individual
and group level. There are a number of developments that
influence the traditional
way of policy-making, including social media as a means to

interact with the public
(Bertot et al. 2012), blogs (Coleman and Moss 2008), open data
(Janssen et al. 2012;
Zuiderwijk and Janssen 2013), freedom of information (Burt
2011), the wisdom
of the crowds (Surowiecki 2004), open collaboration and
transparency in policy
simulation (Wimmer et al. 2012a, b), agent-based simulation
and hybrid modeling
techniques (Koliba and Zia 2012) which open new ways of
innovative policy-making.
Whereas traditional policy-making is executed by experts, now
the public is involved
to fulfill requirements of good governance according to open
government principles.
[email protected]
Also, the skills and capabilities of crowds can be explored and
can lead to better and
more transparent democratic policy decisions. All these
developments can be used for
enhancing citizen’s engagement and to involve citizens better in
the policy-making
process. We want to emphasize three important developments.
1.3.1 The Availability of Big and Open Linked Data (BOLD)
Policy-making heavily depends on data about existing policies
and situations to
make decisions. Both public and private organizations are
opening their data for use

by others. Although information could be requested for in the
past, governments
have changed their strategy toward actively publishing open
data in formats that are
readily and easily accessible (for example,
European_Commission 2003; Obama
2009). Multiple perspectives are needed to make use of and
stimulate new practices
based on open data (Zuiderwijk et al. 2014). New applications
and innovations can
be based solely on open data, but often open data are enriched
with data from other
sources. As data can be generated and provided in huge
amounts, specific needs for
processing, curation, linking, visualization, and maintenance
appear. The latter is
often denoted with big data in which the value is generated by
combining different
datasets (Janssen et al. 2014). Current advances in processing
power and memory
allows for the processing of a huge amount of data. BOLD
allows for analyzing
policies and the use of these data in models to better predict the
effect of new policies.
1.3.2 Rise of Hybrid Simulation Approaches
In policy implementation and execution, many actors are
involved and there are a
huge number of factors influencing the outcomes; this
complicates the prediction
of the policy outcomes. Simulation models are capable of
capturing the interdepen-
dencies between the many factors and can include stochastic
elements to deal with
the variations and uncertainties. Simulation is often used in

policy-making as an
instrument to gain insight in the impact of possible policies
which often result in
new ideas for policies. Simulation allows decision-makers to
understand the essence
of a policy, to identify opportunities for change, and to evaluate
the effect of pro-
posed changes in key performance indicators (Banks 1998; Law
and Kelton 1991).
Simulation heavily depends on data and as such can benefit
from big and open data.
Simulation models should capture the essential aspects of
reality. Simulation
models do not rely heavily on mathematical abstraction and are
therefore suitable
for modeling complex systems (Pidd 1992). Already the
development of a model
can raise discussions about what to include and what factors are
of influence, in this
way contributing to a better understanding of the situation at
hand. Furthermore,
experimentation using models allows one to investigate
different settings and the
influence of different scenarios in time on the policy outcomes.
[email protected]
The effects of policies are hard to predict and dealing with
uncertainty is a key
aspect in policy modeling. Statistical representation of real-
world uncertainties is

an integral part of simulation models (Law and Kelton 1991).
The dynamics asso-
ciated with many factors affecting policy-making, the
complexity associated with
the interdependencies between individual parts, and the
stochastic elements asso-
ciated with the randomness and unpredictable behavior of
transactions complicates
the simulations. Computer simulations for examining,
explaining, and predicting so-
cial processes and relationships as well as measuring the
possible impact of policies
has become an important part of policy-making. Traditional
models are not able to
address all aspects of complex policy interactions, which
indicates the need for the
development of hybrid simulation models consisting of a
combinatory set of models
built on different modeling theories (Koliba and Zia 2012). In
policy-making it can
be that multiple models are developed, but it is also possible to
combine various
types of simulation in a single model. For this purpose agent-
based modeling and
simulation approaches can be used as these allow for combining
different type of
models in a single simulation.
1.3.3 Ubiquitous User Engagement
Efforts to design public policies are confronted with
considerable complexity, in
which (1) a large number of potentially relevant factors needs to
be considered, (2) a
vast amount of data needs to be processed, (3) a large degree of
uncertainty may exist,

and (4) rapidly changing circumstances need to be dealt with.
Utilizing computational
methods and various types of simulation and modeling methods
is often key to
solving these kinds of problems (Koliba and Zia 2012). The
open data and social
media movements are making large quantities of new data
available. At the same time
enhancements in computational power have expanded the
repertoire of instruments
and tools available for studying dynamic systems and their
interdependencies. In
addition, sophisticated techniques for data gathering,
visualization, and analysis have
expanded our ability to understand, display, and disseminate
complex, temporal, and
spatial information to diverse audiences. These problems can
only be addressed from
a complexity science perspective and with a multitude of views
and contributions
from different disciplines. Insights and methods of complexity
science should be
applied to assist policy-makers as they tackle societal problems
in policy areas such
as environmental protection, economics, energy, security, or
public safety and health.
This demands user involvement which is supported by
visualization techniques and
which can be actively involved by employing (serious) games.
These methods can
show what hypothetically will happen when certain policies are
implemented.
[email protected]

1.4 Combining Disciplines in E-government Policy-Making
This new field has been shaped using various names, including
e-policy-making,
digital policy science, computational intelligence, digital
sciences, data sciences,
and policy informatics (Dawes and Janssen 2013). The essence
of this field it that it
is
1. Practice-driven
2. Employs modeling techniques
3. Needs the knowledge coming from various disciplines
4. It focused on governance and policy-making
This field is practice-driven by taking as a starting point the
public policy problem and
defining what information is relevant for addressing the
problem under study. This
requires understanding of public administration and policy-
making processes. Next,
it is a key to determine how to obtain, store, retrieve, process,
model, and interpret the
results. This is the field of e-participation, policy-modeling,
social simulation, and
complex systems. Finally, it should be agreed upon how to
present and disseminate
the results so that other researchers, decision-makers, and
practitioners can use it.
This requires in-depth knowledge of practice, of structures of
public administration
and constitutions, political cultures, processes and culture and
policy-making.

Based on the ideas, the FP7 project EgovPoliNet project has
created an inter-
national community in ICT solutions for governance and policy-
modeling. The
“policy-making 2.0” LinkedIn community has a large number of
members from dif-
ferent disciplines and backgrounds representing practice and
academia. This book
is the product of this project in which a large number of persons
from various dis-
ciplines and representing a variety of communities were
involved. The book shows
experiences and advances in various areas of policy-making.
Furthermore, it contains
comparative analyses and descriptions of cases, tools, and
scientific approaches from
the knowledge base created in this project. Using this book,
practices and knowl-
edge in this field is shared among researchers. Furthermore, this
book provides the
foundations in this area. The covered expertise include a wide
range of aspects for so-
cial and professional networking and multidisciplinary
constituency building along
the axes of technology, participative processes, governance,
policy-modeling, social
simulation, and visualization. In this way eGovPoliNet has
advanced the way re-
search, development, and practice is performed worldwide in
using ICT solutions
for governance and policy-modeling.
Although in Europe the term “e-government policy” or “e-
policy,” for short, is
often used to refer to these types of phenomena, whereas in the

USA often the term
“policy informatics” is used. This is similar to that in the USA
the term digital
government is often used, whereas in Europe the term e-
government is preferred.
Policy informatics is defined as “the study of how information
is leveraged and efforts
are coordinated towards solving complex public policy
problems” (Krishnamurthy
et al. 2013, p. 367). These authors view policy informatics as an
emerging research
space to navigate through the challenges of complex layers of
uncertainty within
[email protected]
governance processes. Policy informatics community has
created Listserv called
Policy Informatics Network (PIN-L).
E-government policy-making is closely connected to “data
science.” Data science
is the ability to find answers from larger volumes of
(un)structured data (Davenport
and Patil 2012). Data scientists find and interpret rich data
sources, manage large
amounts of data, create visualizations to aid in understanding
data, build mathemat-
ical models using the data, present and communicate the data
insights/findings to
specialists and scientists in their team, and if required to a
nonexpert audience. These

are activities which are at the heart of policy-making.
1.5 Overview of Chapters
In total 54 different authors were involved in the creation of
this book. Some chapters
have a single author, but most of the chapters have multiple
authors. The authors rep-
resent a wide range of disciplines as shown in Fig. 1.2. The
focus has been on targeting
five communities that make up the core field for ICT-enabled
policy-making. These
communities include e-government/e-participation, information
systems, complex
systems, public administration, and policy research and social
simulation. The com-
bination of these disciplines and communities are necessary to
tackle policy problems
in new ways. A sixth category was added for authors not
belonging to any of these
communities, such as philosophy and economics. Figure 1.3
shows that the authors
are evenly distributed among the communities, although this is
less with the chapter.
Most of the authors can be classified as belonging to the e-
government/e-participation
community, which is by nature interdisciplinary.
Foundation The first part deals with the foundations of the
book. In their Chap. 2
Chris Koliba and Asim Zia start with a best practice to be
incorporated in public
administration educational programs to embrace the new
developments sketched in
EGOV

IS
Complex Systems
Public Administration and
Policy Research
Social Simulation
other (philosophy, energy,
economics, )
Fig. 1.3 Overview of the disciplinary background of the authors
[email protected]
this chapter. They identify two types of public servants that
need to be educated.
The policy informatics include the savvy public manager and
the policy informatics
analyst. This chapter can be used as a basis to adopt
interdisciplinary approaches and
include policy informatics in the public administration
curriculum.
Petra Ahrweiler and Nigel Gilbert discuss the need for the
quality of simulation
modeling in their Chap. 3. Developing simulation is always
based on certain as-
sumptions and a model is as good as the developer makes it.
The user community is

proposed to assess the quality of a policy-modeling exercise.
Communicative skills,
patience, willingness to compromise on both sides, and
motivation to bridge the
formal world of modelers and the narrative world of policy-
makers are suggested as
key competences. The authors argue that user involvement is
necessary in all stages
of model development.
Wander Jager and Bruce Edmonds argue that due to the
complexity that many
social systems are unpredictable by nature in their Chap. 4.
They discuss how some
insights and tools from complexity science can be used in
policy-making. In particular
they discuss the strengths and weaknesses of agent-based
modeling as a way to gain
insight in the complexity and uncertainty of policy-making.
In the Chap. 5, Erik Pruyt sketches the future in which different
systems modeling
schools and modeling methods are integrated. He shows that
elements from policy
analysis, data science, machine learning, and computer science
need to be combined
to deal with the uncertainty in policy-making. He demonstrates
the integration of
various modeling and simulation approaches and related
disciplines using three cases.
Modeling approaches are compared in the Chap. 6 authored by
Dragana Majs-
torovic, Maria A. Wimmer, Roy Lay-Yee, Peter Davis,and Petra
Ahrweiler. Like in
the previous chapter they argue that none of the theories on its

own is able to address
all aspects of complex policy interactions, and the need for
hybrid simulation models
is advocated.
The next chapter is complimentary to the previous chapter and
includes a com-
parison of ICT tools and technologies. The Chap. 7 is authored
by Eleni Kamateri,
Eleni Panopoulou, Efthimios Tambouris, Konstantinos
Tarabanis, Adegboyega Ojo,
Deirdre Lee, and David Price. This chapter can be used as a
basis for tool selecting
and includes visualization, argumentation, e-participation,
opinion mining, simula-
tion, persuasive, social network analysis, big data analytics,
semantics, linked data
tools, and serious games.
Social Aspects, Stakeholders and Values Although much
emphasis is put on mod-
eling efforts, the social aspects are key to effective policy-
making. The role of values
is discussed in the Chap. 8 authored by Andreas Ligtvoet,
Geerten van de Kaa, Theo
Fens, Cees van Beers, Paulien Herder, and Jeroen van den
Hoven. Using the case of
the design of smart meters in energy networks they argue that
policy-makers would
do well by not only addressing functional requirements but also
by taking individual
stakeholder and PVs into consideration.
In policy-making a wide range of stakeholders are involved in
various stages
of the policy-making process. Natalie Helbig, Sharon Dawes,

Zamira Dzhusupova,
Bram Klievink, and Catherine Gerald Mkude analyze five case
studies of stakeholder
[email protected]
engagement in policy-making in their Chap. 9. Various
engagement tools are dis-
cussed and factors identified which support the effective use of
particular tools and
technologies.
The Chap. 10 investigates the role of values and trust in
computational models in
the policy process. This chapter is authored by Rebecca Moody
and Lasse Gerrits. The
authors found that a large diversity exists in values within the
cases. By the authors
important explanatory factors were found including (1) the role
of the designer of
the model, (2) the number of different actors (3) the level of
trust already present,
and (4) and the limited control of decision-makers over the
models.
Bureaucratic organizations are often considered to be inefficient
and not customer
friendly. Tjeerd Andringa presents and discusses a
multidisciplinary framework con-
taining the drivers and causes of bureaucracy in the Chap. 11.
He concludes that the
reduction of the number of rules and regulations is important,

but that motivating
workers to understand their professional roles and to learn to
oversee the impact of
their activities is even more important.
Crowdsourcing has become an important policy instrument to
gain access to
expertise (“wisdom”) outside own boundaries. In the Chap. 12,
Euripids Loukis
and Yannis Charalabidis discuss Web 2.0 social media for
crowdsourcing. Passive
crowdsourcing exploits the content generated by users, whereas
active crowdsourcing
stimulates content postings and idea generation by users.
Synergy can be created by
combining both approaches. The results of passive
crowdsourcing can be used for
guiding active crowdsourcing to avoid asking users for similar
types of input.
Policy, Collaboration and Games Agent-based gaming (ABG) is
used as a tool
to explore the possibilities to manage complex systems in the
Chap. 13 by Wander
Jager and Gerben van der Vegt. ABG allows for modeling a
virtual and autonomous
population in a computer game setting to exploit various
management and leadership
styles. In this way ABG contribute to the development of the
required knowledge on
how to manage social complex behaving systems.
Micro simulation focuses on modeling individual units and the
micro-level pro-
cesses that affect their development. The concepts of micro
simulation are explained

by Roy Lay-Yee and Gerry Cotterell in the Chap. 14. Micro
simulation for pol-
icy development is useful to combine multiple sources of
information in a single
contextualized model to answer “what if” questions on complex
social phenomena.
Visualization is essential to communicate the model and the
results to a variety
of stakeholders. These aspects are discussed in the Chap. 15 by
Tobias Ruppert,
Jens Dambruch, Michel Krämer, Tina Balke, Marco Gavanelli,
Stefano Bragaglia,
Federico Chesani, Michela Milano, and Jörn Kohlhammer. They
argue that despite
the significance to use evidence in policy-making, this is
seldom realized. Three
case studies that have been conducted in two European research
projects for policy-
modeling are presented. In all the cases access for nonexperts to
the computational
models by information visualization technologies was realized.
[email protected]
Applications and Practices Different projects have been
initiated to study the best
suitable transition process towards renewable energy. In the
Chap. 16 by Dominik
Bär, Maria A. Wimmer, Jozef Glova, Anastasia
Papazafeiropoulou,and Laurence
Brooks five of these projects are analyzed and compared. They

please for transferring
models from one country to other countries to facilitate
learning.
Lyudmila Vidyasova, Andrei Chugunov, and Dmitrii Trutnev
present experiences
from Russia in their Chap. 17. They argue that informational,
analytical, and fore-
casting activities for the processes of socioeconomic
development are an important
element in policy-making. The authors provide a brief overview
of the history, the
current state of the implementation of information processing
techniques, and prac-
tices for the purpose of public administration in the Russian
Federation. Finally, they
provide a range of recommendations to proceed.
Urban policy for sustainability is another important area which
is directly linked
to the first chapter in this section. In the Chap. 18, Diego
Navarra and Simona Milio
demonstrate a system dynamics model to show how urban policy
and governance in
the future can support ICT projects in order to reduce energy
usage, rehabilitate the
housing stock, and promote sustainability in the urban
environment. This chapter
contains examples of sustainable urban development policies as
well as case studies.
In the Chap. 19, Tanko Ahmed discusses the digital divide
which is blocking
online participation in policy-making processes. Structuration,
institutional and
actor-network theories are used to analyze a case study of

political zoning. The
author recommends stronger institutionalization of ICT support
and legislation for
enhancing participation in policy-making and bridging the
digital divide.
1.6 Conclusions
This book is the first comprehensive book in which the various
development and disci-
plines are covered from the policy-making perspective driven by
ICT developments.
A wide range of aspects for social and professional networking
and multidisciplinary
constituency building along the axes of technology,
participative processes, gover-
nance, policy-modeling, social simulation, and visualization are
investigated. Policy-
making is a complex process in which many stakeholders are
involved. PVs can be
used to guide policy-making efforts and to ensure that the many
stakeholders have
an understanding of the societal value that needs to be created.
There is an infusion
of technology resulting in changing policy processes and
stakeholder involvement.
Technologies like social media provides a means to interact
with the public, blogs
can be used to express opinions, big and open data provide
input for evidence-based
policy-making, the integration of various types of modeling and
simulation tech-
niques (hybrid models) can provide much more insight and
reliable outcomes, gam-
ing in which all kind of stakeholders are involved open new
ways of innovative policy-

making. In addition trends like the freedom of information, the
wisdom of the crowds,
and open collaboration changes the landscape further. The
policy-making landscape
is clearly changing and this demands a strong need for
interdisciplinary research.
[email protected]
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[email protected]
Chapter 2
Educating Public Managers and Policy Analysts

in an Era of Informatics
Christopher Koliba and Asim Zia
Abstract In this chapter, two ideal types of practitioners who
may use or cre-
ate policy informatics projects, programs, or platforms are
introduced: the policy
informatics-savvy public manager and the policy informatics
analyst. Drawing from
our experiences in teaching an informatics-friendly graduate
curriculum, we dis-
cuss the range of learning competencies needed for traditional
public managers and
policy informatics-oriented analysts to thrive in an era of
informatics. The chapter
begins by describing the two different types of students who
are, or can be touched
by, policy informatics-friendly competencies, skills, and
attitudes. Competencies
ranging from those who may be users of policy informatics and
sponsors of policy
informatics projects and programs to those analysts designing
and executing policy
informatics projects and programs will be addressed. The
chapter concludes with
an illustration of how one Master of Public Administration
(MPA) program with a
policy informatics-friendly mission, a core curriculum that
touches on policy infor-
matics applications, and a series of program electives that
allows students to develop
analysis and modeling skills, designates its informatics-oriented
competencies.
2.1 Introduction

The range of policy informatics opportunities highlighted in this
volume will require
future generations of public managers and policy analysts to
adapt to the oppor-
tunities and challenges posed by big data and increasing
computational modeling
capacities afforded by the rapid growth in information
technologies. It will be up
to the field’s Master of Public Administration (MPA) and
Master of Public Policy
(MPP) programs to provide this next generation with the tools
needed to harness the
wealth of data, information, and knowledge increasingly at the
disposal of public
C. Koliba (�)
University of Vermont, 103 Morrill Hall, 05405 Burlington, VT,
USA
A. Zia
University of Vermont, 205 Morrill Hall, 05405 Burlington, VT,
USA
10.1007/978-3-319-12784-2_2
[email protected]
16 C. Koliba and A. Zia
administrators and policy analysts. In this chapter, we discuss

the role of policy infor-
matics in the development of present and future public
managers and policy analysts.
Drawing from our experiences in teaching an informatics-
friendly graduate curricu-
lum, we discuss the range of learning competencies needed for
traditional public
managers and policy informatics-oriented analysts to thrive in
an era of informatics.
The chapter begins by describing the two different types of
students who are, or can
be touched by, policy informatics-friendly competencies, skills,
and attitudes. Com-
petencies ranging from those who may be users of policy
informatics and sponsors of
policy informatics projects and programs to those analysts
designing and executing
policy informatics projects and programs will be addressed. The
chapter concludes
with an illustration of how one MPA program with a policy
informatics-friendly
mission, a core curriculum that touches on policy informatics
applications, and a
series of program electives that allows students to develop
analysis and modeling
skills, designates its informatics-oriented competencies.
2.2 Two Types of Practitioner Orientations to Policy
Informatics
Drawn from our experience, we find that there are two “ideal
types” of policy infor-
matics practitioner, each requiring greater and greater levels of
technical mastery of
analytics techniques and approaches. These ideal types are:
policy informatics-savvy

public managers and policy informatics analysts.
A policy informatics-savvy public manager may take on one of
two possible roles
relative to policy informatics projects, programs, or platforms.
They may play instru-
mental roles in catalyzing and implementing informatics
initiatives on behalf of their
organizations, agencies, or institutions. In the manner, they may
work with technical
experts (analysts) to envision possible uses for data,
visualizations, simulations, and
the like. Public managers may also be in the role of using policy
informatics projects,
programs, or platforms. They may be in positions to use these
initiatives to ground
decision making, allocate resources, and otherwise guide the
performance of their
organizations.
A policy informatics analyst is a person who is positioned to
actually execute
a policy informatics initiative. They may be referred to as
analysts, researchers,
modelers, or programmers and provide the technical assistance
needed to analyze
databases, build and run models, simulations, and otherwise
construct useful and
effective policy informatics projects, programs, or platforms.
To succeed in either and both roles, managers and analysts will
require a certain set
of skills, knowledge, or competencies. Drawing on some of the
prevailing literature
and our own experiences, we lay out an initial list of potential
competencies for

consideration.
[email protected]
2 Educating Public Managers and Policy Analysts in an Era of
Informatics 17
2.2.1 Policy Informatics-Savvy Public Managers
To successfully harness policy informatics, public managers
will likely not need to
know how to explicitly build models or manipulate big data.
Instead, they will need
to know what kinds of questions that policy informatics projects
or programs can
answer or not answer. They will need to know how to contract
with and/or manage
data managers, policy analysts, and modelers. They will need to
be savvy consumers
of data analysis and computational models, but not necessarily
need to know how to
technically execute them. Policy informatics projects, programs,
and platforms are
designed and executed in some ways, as any large-scale,
complex project.
In writing about the stages of informatics project development
using “big data,”
DeSouza lays out project development along three stages:
planning, execution, and
postimplementation. Throughout the project life cycle, he
emphasizes the role of
understanding the prevailing policy and legal environment, the
need to venture into

coalition building, the importance of communicating the broader
opportunities af-
forded by the project, the need to develop performance
indicators, and the importance
of lining up adequate financial and human resources (2014).
Framing what traditional public managers need to know and do
to effectively
interface with policy informatics projects and programs requires
an ability to be a
“systems thinker,” an effective evaluator, a capacity to integrate
informatics into
performance and financial management systems, effective
communication skills,
and a capacity to draw on social media, information technology,
and e-governance
approaches to achieve common objectives. We briefly review
each of these capacities
below.
Systems Thinking Knowing the right kinds of questions that
may be asked through
policy informatics projects and programs requires public
managers to possess a “sys-
tems” view. Much has been written about the importance of
“systems thinking” for
public managers (Katz and Kahn 1978; Stacey 2001; Senge
1990; Korton 2001).
Taking a systems perspective allows public managers to
understand the relationship
between the “whole” and the “parts.” Systems-oriented public
managers will possess
a level of situational awareness (Endsley 1995) that allows them
to see and under-
stand patterns of interaction and anticipate future events and
orientations. Situational

awareness allows public mangers to understand and evaluate
where data are coming
from, how best data are interpreted, and the kinds of
assumptions being used in
specific interpretations (Koliba et al. 2011). The concept of
system thinking laid out
here can be associated with the notion of transition management
(Loorbach 2007).
Process Orientations to Public Policy The capacity to view the
policy making and
implementation process as a process that involves certain levels
of coordination
and conflict between policy actors is of critical importance for
policy informatics-
savvy public managers and analysts. Understanding how data
are used to frame
problems and policy solutions, how complex governance
arrangements impact policy
implementation (Koliba et al. 2010), and how data visualization
can be used to
[email protected]
facilitate the setting of policy agendas and open policy windows
(Kingdon 1984) is
of critical importance for public management and policy
analysts alike.
Research Methodologies Another basic competency needed for
any public manager
using policy informatics is a foundational understanding of

research methods, par-
ticularly quantitative reasoning and methodologies. A
foundational understanding of
data validity, analytical rigor and relevance, statistical
significance, and the like are
needed to be effective consumers of informatics. That said,
traditional public man-
agers should also be exposed to qualitative methods as well,
refining their powers of
observation, understanding how symbols, stories, and numbers
are used to govern,
and how data and data visualization and computer simulations
play into these mental
models.
Performance Management A key feature of systems thinking as
applied to policy
informatics is the importance of understanding how data and
analysis are to be
used and who the intended users of the data are (Patton 2008).
The integration of
policy informatics into strategic planning (Bryson 2011),
performance management
systems (Moynihan 2008), and ultimately woven into an
organization’s capacity to
learn, adapt, and evolve (Argyis and Schön 1996) are critically
important in this
vein. As policy informatics trends evolve, public managers will
likely need to be
exposed to uses of decision support tools, dashboards, and other
computationally
driven models and visualizations to support organizational
performance.
Financial Management Since the first systemic budgeting
systems were put in place,

public managers have been urged to use the budgeting process
as a planning and eval-
uation tool (Willoughby 1918). This approach was formally
codified in the 1960s
with the planning–programming–budgeting (PPB) system with
its focus on plan-
ning, managerial, and operational control (Schick 1966) and
later adopted into more
contemporary approaches to budgeting (Caiden 1981). Using
informative projects,
programs, or platforms to make strategic resource allocation
decisions is a necessary
given and a capacity that effective public managers must
master. Likewise, the pol-
icy analyst will likely need to integrate financial resource flows
and costs into their
projects.
Collaborative and Cooperative Capacity Building The
development and use of pol-
icy informatics projects, programs, or platforms is rarely, if
ever, undertaken as
an individual, isolated endeavor. It is more likely that such
initiatives will require
interagency, interorganizational, or intergroup coordination. It
is also likely that
content experts will need to be partnered with analysts and
programmers to com-
plete tasks and execute designs. The public manager and policy
analyst must both
possess the capacity to facilitate collaborative management
functions (O’Leary and
Bingham 2009).
Basic Communication Skills This perhaps goes without saying,
but the heart of any

informatics project lies in the ability to effectively
communicate findings and ideas
through the analysis of data.
[email protected]
Informatics 19
Social Media, Information Technology, and e-Governance
Awareness A final com-
petency concerns public managers’ capacity to deepen their
understanding of how
social media, Web-based tools, and related information
technologies are being em-
ployed to foster various e-government, e-governance, and
related initiatives (Mergel
2013). Placing policy informatics projects and programs within
the context of these
larger trends and uses is something that public managers must
be exposed to.
Within our MPA program, we have operationalized these
capacities within a four-
point rubric that outlines what a student needs to do to
demonstrate meeting these
standards. The rubric below highlights 8 of our program’s 18
capacities. All 18 of
these capacities are situated under 1 of the 5 core competencies
tied to the accred-
itation standards of the Network of Schools of Public Affairs
and Administration
(NASPAA), the professional accrediting association in the USA,
and increasingly in

other countries as well, for MPA and MPP programs. A
complete list of these core
competencies and the 18 capacities nested under them are
provided in Appendix of
this chapter.
The eight capacities that we have singled out as being the most
salient to the role
of policy informatics in public administration are provided in
Table 2.1. The rubric
follows a four-point scale, ranging from “does not meet
standard,” “approaches
standard,” “meets standard,” and “exceeds standard.”
2.2.2 Policy Informatics Analysts
A second type of practitioner to be considered is what we are
referring to as a “policy
informatics analyst.” When considering the kinds of
competencies that policy infor-
matics analysts need to be successful, we first assume that the
basic competencies
outlined in the prior section apply here as well. In other words,
effective policy in-
formatics analysts must be systems thinkers in order to place
data and their analysis
into context, be cognizant of current uses of decision support
systems (and related
platforms) to enable organizational learning, performance, and
strategic planning,
and possess an awareness of e-governance and e-government
initiatives and how they
are transforming contemporary public management and policy
planning practices.
In addition, policy analysts must possess a capacity to
understand policy systems:

How policies are made and implemented? This baseline
understanding can then be
used to consider the placement, purpose, and design of policy
informatics projects
or programs. We lay out more specific analyst capacities below.
Advanced Research Methods of Information Technology
Applications In many in-
stances, policy informatics analysts will need to move beyond
meeting the standard.
This is particularly true in the area of exceeding the public
manager standards for re-
search methods and utilization of information technology. It is
assumed that effective
policy informatics analysts will have a strong foundation in
quantitative methodolo-
gies and applications. To obtain these skills, policy analysts
will need to move beyond
basic surveys of research methods into more advanced research
methods curriculum.
[email protected]
T
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ra
m
/p
ro
je
ct
C
an
pr
ov
id
e
a
pi
ec
e

of
or
ig
in
al
an
al
ys
is
of
an
ob
se
rv
ed
ph
en
om
en
on
em
pl
oy
in

g
on
e
qu
al
it
at
iv
e
or
qu
an
ti
ta
ti
ve
m
et
ho
do
lo
gy
ef
fe

ct
iv
el
y.
P
os
se
ss
es
ca
pa
ci
ty
to
co
m
m
is
si
on
a
pi
ec
e

of
or
ig
in
al
re
se
ar
ch
.C
an
pr
ov
id
e
a
de
ta
il
ed
ac
co
un
t

fo
r
ho
w
a
D
em
on
st
ra
te
s
th
e
ca
pa
ci
ty
to
un
de
rt
ak

e
an
in
de
pe
nd
en
t
re
se
ar
ch
ag
en
da
th
ro
ug
h
em
pl
oy
in
g

on
e
or
m
or
e
so
ci
al
re
se
ar
ch
m
et
ho
ds
ar
ou
nd
a
to
pi
c

of
st
ud
y
of
im
po
rt
an
ce
to
pu
bl
ic
ad
m
in
is
tr
at
io
n.
C
an

de
m
on
st
ra
te
th
e
su
cc
es
sf
ul
ex
ec
ut
io
n
of
a
pr
og
ra
m

or
[email protected]
Informatics 21
T
ab
le
2.
1
(c
on
ti
nu
ed
)
C
ap
ac
it
y
D
oe

s
no
t
m
ee
t
st
an
da
rd
A
pp
ro
ac
he
s
st
an
da
rd
M
ee
ts
st

an
da
rd
E
xc
ee
ds
st
an
da
rd
ev
al
ua
ti
on
an
d
ex
pl
ai
n
w
ha

t
th
e
po
ss
ib
le
go
al
s
an
d
ou
tc
om
es
of
su
ch
an
ev
al
ua
ti

on
m
ig
ht
be
pr
og
ra
m
or
pr
oj
ec
t
ev
al
ua
ti
on
pr
oj
ec
t
sh

ou
ld
be
st
ru
ct
ur
ed
w
it
hi
n
th
e
co
nt
ex
t
of
a
sp
ec
ifi
c

pr
og
ra
m
or
pr
oj
ec
t
pr
oj
ec
t
ev
al
ua
ti
on
or
th
e
su
cc
es

sf
ul
ut
il
iz
at
io
n
of
a
pr
og
ra
m
or
pr
oj
ec
t
ev
al
ua
ti
on

to
im
pr
ov
e
ad
m
in
is
tr
at
iv
e
pr
ac
ti
ce
C
a
p
a
ci
ty
to

a
p
p
ly
so
u
n
d
p
er
fo
rm
a
n
ce
m
ea
su
re
m
en
t
a
n

d
m
a
n
ag
em
en
t
p
ra
ct
ic
es
C
an
pr
ov
id
e
an
ex
pl
an
at

io
n
of
w
hy
pe
rf
or
m
an
ce
go
al
s
an
d
m
ea
su
re
s
ar
e
im

po
rt
an
t
in
pu
bl
ic
ad
m
in
is
tr
at
io
n,
bu
t
ca
nn
ot
ap
pl
y

th
is
re
as
on
in
g
to
sp
ec
ifi
c
co
nt
ex
ts
C
an
id
en
ti
fy
th
e

pe
rf
or
m
an
ce
m
an
ag
em
en
t
co
ns
id
er
at
io
ns
fo
r
a
pa
rt

ic
ul
ar
si
tu
at
io
n
or
co
nt
ex
t,
bu
t
ha
s
li
m
it
ed
ca
pa
ci

ty
to
ev
al
ua
te
th
e
ef
fe
ct
iv
en
es
s
of
pe
rf
or
m
an
ce
m
an

ag
em
en
t
sy
st
em
s
C
an
id
en
ti
fy
an
d
an
al
yz
e
pe
rf
or
m

an
ce
m
an
ag
em
en
t
sy
st
em
s,
ne
ed
s,
an
d
em
er
gi
ng
op
po
rt

un
it
ie
s
w
it
hi
n
a
sp
ec
ifi
c
or
ga
ni
za
ti
on
or
ne
tw
or
k

C
an
pr
ov
id
e
ne
w
in
si
gh
ts
in
to
th
e
pe
rf
or
m
an
ce
m
an

ag
em
en
t
ch
al
le
ng
es
fa
ci
ng
an
or
ga
ni
za
ti
on
or
ne
tw
or
k,

an
d
su
gg
es
t
al
te
rn
at
iv
e
de
si
gn
an
d
m
ea
su
re
m
en
t

sc
en
ar
io
s
C
a
p
a
ci
ty
to
a
p
p
ly
so
u
n
d
fi
n
a

n
ci
a
l
p
la
n
n
in
g
a
n
d
fi
sc
a
l
re
sp
o
n
si
b
il

it
y
C
an
id
en
ti
fy
w
hy
bu
dg
et
in
g
an
d
so
un
d
fi
sc
al
m

an
ag
em
en
t
pr
ac
ti
ce
s
ar
e
im
po
rt
an
t,
bu
t
ca
nn
ot
an
al

yz
e
ho
w
an
d/
or
if
su
ch
pr
ac
ti
ce
s
ar
e
be
in
g
us
ed
w
it

hi
n
sp
ec
ifi
c
co
nt
ex
ts
C
an
id
en
ti
fy
fi
sc
al
pl
an
ni
ng
an

d
bu
dg
et
in
g
pr
ac
ti
ce
s
fo
r
a
pa
rt
ic
ul
ar
si
tu
at
io
n

or
co
nt
ex
t,
bu
t
ha
s
li
m
it
ed
ca
pa
ci
ty
to
ev
al
ua
te
th
e

ef
fe
ct
iv
en
es
s
of
a
fi
na
nc
ia
l
m
an
ag
em
en
t
sy
st
em
C

an
id
en
ti
fy
an
d
an
al
yz
e
fi
na
nc
ia
l
m
an
ag
em
en
t
sy
st

em
s,
ne
ed
s,
an
d
em
er
gi
ng
op
po
rt
un
it
ie
s
w
it
hi
n
a
sp

ec
ifi
c
or
ga
ni
za
ti
on
or
ne
tw
or
k
C
an
pr
ov
id
e
ne
w
in
si

gh
ts
in
to
th
e
fi
na
nc
ia
l
m
an
ag
em
en
t
ch
al
le
ng
es
fa
ci

ng
an
or
ga
ni
za
ti
on
or
ne
tw
or
k,
an
d
su
gg
es
t
al
te
rn
at
iv

e
de
si
gn
an
d
bu
dg
et
in
g
sc
en
ar
io
s
C
a
p
a
ci
ty
to
a

ch
ie
ve
co
o
p
er
a
ti
o
n
th
ro
u
g
h
p
a
rt
ic
ip
a
to
ry

p
ra
ct
ic
es
C
an
ex
pl
ai
n
w
hy
it
is
im
po
rt
an
t
fo
r
pu

bl
ic
ad
m
in
is
tr
at
or
s
to
be
op
en
an
d
re
sp
on
si
ve
pr
ac
ti

ti
on
er
s
in
a
va
gu
e
or
ab
st
ra
ct
w
ay
,b
ut
ca
nn
ot
pr
ov
id

e
sp
ec
ifi
c
ex
pl
an
at
io
ns
or
ju
st
ifi
ca
ti
on
s
ap
pl
ie
d
to

pa
rt
ic
ul
ar
co
nt
ex
ts
C
an
id
en
ti
fy
in
st
an
ce
s
in
sp
ec
ifi

c
ca
se
s
or
co
nt
ex
ts
w
he
re
a
pu
bl
ic
ad
m
in
is
tr
at
or
de

m
on
st
ra
te
d
or
fa
il
ed
to
de
m
on
st
ra
te
in
cl
us
iv
e
pr
ac

ti
ce
s
C
an
de
m
on
st
ra
te
ho
w
in
cl
us
iv
e
pr
ac
ti
ce
s
an

d
co
nfl
ic
t
m
an
ag
em
en
t
le
ad
s
to
co
op
er
at
io
n
fo
r
fo

rm
in
g
co
al
it
io
ns
an
d
co
ll
ab
or
at
iv
e
pr
ac
ti
ce
s
C
an

or
ch
es
tr
at
e
an
y
of
th
e
fo
ll
ow
in
g:
co
al
it
io
n
bu
il
di

ng
ac
ro
ss
un
it
s,
or
ga
ni
za
ti
on
s,
or
in
st
it
ut
io
ns
,e
ff
ec

ti
ve
te
am
w
or
k,
an
d/
or
co
nfl
ic
t
m
an
ag
em
en
t
[email protected]

T
ab
le
2.
1
(c
on
ti
nu
ed
)
C
ap
ac
it
y
D
oe
s
no
t
m
ee

t
st
an
da
rd
A
pp
ro
ac
he
s
st
an
da
rd
M
ee
ts
st
an
da
rd
E
xc

ee
ds
st
an
da
rd
C
a
p
a
ci
ty
to
u
n
d
er
ta
ke
h
ig
h
q
u

a
li
ty
o
ra
l,
w
ri
tt
en
co
m
m
u
n
ic
a
ti
o
n
D
em
on
st

ra
te
s
so
m
e
ab
il
it
y
to
ex
pr
es
s
id
ea
s
ve
rb
al
ly
an
d

in
w
ri
ti
ng
.L
ac
ks
co
ns
is
te
nt
ca
pa
ci
ty
to
pr
es
en
t
an
d

w
ri
te
P
os
se
ss
es
th
e
ca
pa
ci
ty
to
w
ri
te
do
cu
m
en
ts
th

at
ar
e
fr
ee
of
gr
am
m
at
ic
al
er
ro
rs
an
d
ar
e
or
ga
ni
ze
d

in
a
cl
ea
r
an
d
ef
fi
ci
en
t
m
an
ne
r.
P
os
se
ss
es
th
e
ca

pa
ci
ty
to
pr
es
en
t
id
ea
s
in
a
pr
of
es
si
on
al
m
an
ne
r.
S

uf
fe
rs
fr
om
a
la
ck
of
co
ns
is
te
nc
y
in
th
e
pr
es
en
ta
ti
on

of
m
at
er
ia
l
an
d
ex
pr
es
si
on
or
or
ig
in
al
id
ea
s
an
d
co

nc
ep
ts
Is
ca
pa
bl
e
of
co
ns
is
te
nt
ly
ex
pr
es
si
ng
id
ea
s
ve

rb
al
ly
an
d
in
w
ri
ti
ng
in
a
pr
of
es
si
on
al
m
an
ne
r
th
at

co
m
m
un
ic
at
es
m
es
sa
ge
s
to
in
te
nd
ed
au
di
en
ce
s
C
an

de
m
on
st
ra
te
so
m
e
in
st
an
ce
s
in
w
hi
ch
ve
rb
al
an
d
w

ri
tt
en
co
m
m
un
ic
at
io
n
ha
s
pe
rs
ua
de
d
ot
he
rs
to
ta
ke

ac
ti
on
C
a
p
a
ci
ty
to
u
n
d
er
ta
ke
h
ig
h
q
u
a
li

ty
el
ec
tr
o
n
ic
a
ll
y
m
ed
ia
te
d
co
m
m
u
n
ic
a
ti
o

n
a
n
d
u
ti
li
ze
in
fo
rm
a
ti
o
n
sy
st
em
s
a
n
d
m
ed

ia
to
a
d
va
n
ce
o
b
je
ct
iv
es
C
an
ex
pl
ai
n
w
hy
in
fo
rm

at
io
n
te
ch
no
lo
gy
is
im
po
rt
an
t
to
co
nt
em
po
ra
ry
w
or
kp

la
ce
s
an
d
pu
bl
ic
ad
m
in
is
tr
at
io
n
en
vi
ro
nm
en
ts
.P
os

se
ss
es
di
re
ct
ex
pe
ri
en
ce
w
it
h
in
fo
rm
at
io
n
te
ch
no
lo

gy
,b
ut
li
tt
le
un
de
rs
ta
nd
in
g
fo
r
ho
w
IT
in
fo
rm
s
pr
of

es
si
on
al
pr
ac
ti
ce
C
an
id
en
ti
fy
in
st
an
ce
s
in
sp
ec
ifi
c

ca
se
s
or
co
nt
ex
t
w
he
re
a
pu
bl
ic
ad
m
in
is
tr
at
or
su
cc

es
sf
ul
ly
or
un
su
cc
es
sf
ul
ly
de
m
on
st
ra
te
d
a
ca
pa
ci
ty

to
us
e
IT
to
fo
st
er
in
no
va
ti
on
,
im
pr
ov
e
se
rv
ic
es
,o
r

de
ep
en
ac
co
un
ta
bi
li
ty
.A
na
ly
si
s
at
th
is
le
ve
l
is
re
le

ga
te
d
to
de
sc
ri
pt
io
ns
an
d
th
in
an
al
ys
is
C
an
id
en
ti
fy

ho
w
IT
im
pa
ct
s
w
or
kp
la
ce
s
an
d
pu
bl
ic
po
li
cy
.
C
an

di
ag
no
se
pr
ob
le
m
s
as
so
ci
at
ed
w
it
h
IT
to
ol
s,
pr
oc
ed

ur
es
,a
nd
us
es
D
em
on
st
ra
te
s
a
ca
pa
ci
ty
to
vi
ew
IT
in
te

rm
s
of
sy
st
em
s
de
si
gn
.I
s
ca
pa
bl
e
of
w
or
ki
ng
w
it
h

IT
pr
of
es
si
on
al
s
in
id
en
ti
fy
in
g
ar
ea
s
of
ne
ed
fo
r
IT

up
gr
ad
es
,I
T
pr
oc
ed
ur
es
,
an
d
IT
us
es
in
re
al
se
tt
in
g

IT
in
fo
rm
at
io
n
te
ch
no
lo
gy
[email protected]
Informatics 23
Competencies in advanced quantitative methods in which
students learn to clean and
manage large databases, perform advanced statistical tests,
develop linear regression
models to describe causal relationship, and the like are needed.
Capacity to work
across software platforms such as Excel, Statistical Package for
the Social Sciences
(SPSS), Analytica, and the like are important. Increasingly, the
capacity to triangu-

late different methods, including qualitative approaches such as
interviews, focus
groups, participant observations is needed.
Data Visualization and Design Not only must analysts be aware
of how these meth-
ods and decision support platforms may be used by practitioners
but also they must
know how to design and implement them. Therefore, we suggest
that policy infor-
matics analysts be exposed to design principles and how they
may be applied to
decision support systems, big data projects, and the like. Policy
informatics analysts
will need to understand and appreciate how data visualization
techniques are being
employed to “tell a story” through data.
Figure 2.1 provides an illustration of one student’s effort to
visualize campaign
donations to state legislatures from the gas-extraction (fracking)
industry undertaken
by a masters student, Jeffery Castle for a system analysis and
strategic management
class taught by Koliba.
Castle’s project demonstrates the power of data visualization to
convey a central
message drawing from existing databases. With a solid research
methods background
and exposure to visualization and design principles in class, he
was able to develop
an insightful policy informatics project.
Basic to Advanced Programming Language Skills Arguably,
policy informatics ana-

lysts will possess a capacity to visualize and present data in a
manner that is accessible.
Increasingly, web-based tools are being used to design user
interfaces. Knowledge
of JAVA and HTML are likely most helpful in these regards. In
some instances,
original programs and models will need to be written through
the use of program-
ming languages such as Python, R, C++, etc. The extent to
which existing software
programs, be they open source or proprietary, provide enough
utility to execute pol-
icy informatics projects, programs, or platforms is a continuing
subject of debate
within the policy informatics community. Exactly how much
and to what extent spe-
cific programming languages and software programs are needing
to be mastered is
a standing question. For the purposes of writing this chapter, we
rely on our current
baseline observations and encourage more discussion and debate
about the range of
competencies needed by successful policy analysts.
Basic to More Advanced Modeling Skills More advanced policy
informatics analysts
will employ computational modeling approaches that allow for
the incorporation of
more complex interactions between variables. These models
may be used to capture
systems as dynamic, emergent, and path dependent. The outputs
of these models
may allow for scenario testing through simulation (Koliba et al.
2011). With the
advancement of modeling software, it is becoming easier for
analysts to develop

system dynamics models, agent-based models, and dynamic
networks designed to
simulate the features of complex adaptive systems. In addition,
the ability to manage
and store data and link or wrap databases is often necessary.
[email protected]
Fig. 2.1 Campaign contributions to the Pennsylvania State
Senate and party membership. The
goal of this analysis is to develop a visualization tool to
translate publically available campaign
contribution information into an easily accessible, visually
appealing, and interactive format. While
campaign contribution data are filed and available to the public
through the Pennsylvania Department
of State, it is not easily synthesized. This analysis uses a
publically available database that has been
published on marcellusmoney.org. In order to visualize the data,
a tool was used that allows for
the creation of a Sankey diagram that is able to be manipulated
and interacted within an Internet
browser. A Sankey diagram visualizes the magnitude of flow
between the nodes of a network (Castle
2014)
The ability of analysts to draw on a diverse array of methods
and theoretical
frameworks to envision and create models is of critical
importance. Any potential
policy informatics project, program, or platform will be enabled
or constrained by the

modeling logic in place. With a plurality of tools at one’s
disposal, policy informatics
analysts will be better positioned to design relevant and
legitimate models.
[email protected]
Informatics 25
Fig. 2.2 End-stage renal disease (ESRD) system dynamics
population model. To provide clinicians
and health care administrators with a greater understanding of
the combined costs associated with
the many critical care pathways associated with ESRD, a system
dynamics model was designed to
simulate the total expenses of ESRD treatment for the USA, as
well as incidence and mortality rates
associated with different critical care pathways: kidney
transplant, hemodialysis, peritoneal dialysis,
and conservative care. Calibrated to US Renal Data System
(USRDS) 2013 Annual and Historical
Data Report and the US Census Bureau for the years 2005–
2010, encompassing all ESRD patients
under treatment in the USA from 2005 to 2010, the ESRD
population model predicts the growth and
costs of ESRD treatment type populations using historical
patterns. The model has been calibrated
against the output of the USRDS’s own prediction for the year
2020 and also tested by running his-
toric scenarios and comparing the output to existing data. Using
a web interface designed to allow
users to alter certain combinations of parameters, several
scenarios are run to project future spending,

incidence, and mortalities if certain combinations of critical
care pathways are pursued. These sce-
narios include: a doubling of kidney donations and transplant
rates, a marked increase in the offering
of peritoneal dialysis, and an increase in conservative care
routes for patients over 65. The results
of these scenario runs are shared, demonstrating sizable cost
savings and increased survival rates.
Implications of clinical practice, public policy, and further
research are drawn (Fernandez 2013)
Figure 2.2 provides an illustration of Luca Fernandez’s system
dynamics model of
critical care pathways for end-stage renal disease (ESRD).
Fernandez took Koliba’s
system analysis and strategic management course and Zia’s
decision-making model-
ing course. This model, constructed using the proprietary
software, AnyLogic, was
initially constructed as a project in Zia’s course.
Castle and Fernandez’s projects illustrate how master’s-level
students with an
eye toward becoming policy informatics analysts can build
skills and capacities to
develop useful informatics projects that can guide policy and
public management.
They were guided to this point by taking advanced courses
designed explicitly with
policy informatics outcomes in mind.
[email protected]

Policy Informatics Analyst
Informatics-Savvy Public
•Advanced research methods •Data visualization and design
techniques •Basic to advanced modeling software skills •Basic
to advanced programming language(s)
•Systems thinking •Basic understanding of research methods
•Knowledge of how to integrate informatics within performance
management •Knowledge of how to integrate inofrmatics within
financial systems•Effecive written communication •Effective
usese of social media / e-governance approaches
Fig. 2.3 The nested capacities of informatics-savvy public
managers and policy informatics analysts
Figure 2.3 illustrates how the competencies of the two different
ideal types of
policy informatics practitioners are nested inside of one
another. A more complete
list of competencies that are needed for the more advanced
forms of policy analy-
sis will need to emerge through robust exchanges between the
computer sciences,
organizational sciences, and policy sciences. These views will
likely hinge on as-
sumptions about the sophistication of the models to be
developed. A key question
here concerning the types of models to be built is: Can adequate
models be built
using existing software or is original programming needed or
desired? Ideally, ad-
vanced policy analysts undertaking policy informatics projects
are “programmers
with a public service motivation.”

2.3 Applications to Professional Masters Programs
Professional graduate degree programs have steadily moved
toward emphasizing the
importance of the mission of particular graduate programs in
determining the optimal
curriculum to suit the learning needs of it students. As a result,
clear definitions of
the learning outcomes and the learning needs of particular
student communities are
defined. Some programs may seek to serve regional or local
needs of the government
and nonprofit sector, while others may have a broader reach,
preparing students to
work within federal or international level governments and
nonprofits.
In addition to geographic scope, accredited MPA and MPP
programs may have
specific areas of concentration. Some programs may focus on
preparing public man-
agers who are charged with managing resources, making
operational, tactical, and
[email protected]
Informatics 27
strategic decisions and, overall, administering to the day-to-day
needs of a govern-
ment or nonprofit organization. Programs may also focus on
training policy analysts
who are responsible for analyzing policies, policy alternatives,

problem definition,
and the like. Historically, the differences between public
management and policy
analysis have distinguished the MPA degree from the MPP
degree. However, recent
studies of NASPAA-accredited programs have found that the
lines between MPA and
MPP programs are increasingly blurred (Hur and Hackbart
2009). The relationship
between public management and policy analysis matters to those
interested in policy
informatics because these distinctions drive what policy
informatics competencies
and capacities are covered within a core curriculum, and what
competencies and
capacities are covered within a suite of electives or
concentrations.
Competency-based assessments are increasingly being used to
evaluate and de-
sign curriculum. Drawing on the core tenants of adult learning
theory and practice,
competency-based assessment involves the derivation of
specific skills, knowledge,
or attitudes that an adult learner must obtain in order to
successfully complete a
course of study or degree requirement. Effective competency-
based graduate pro-
grams call on students to demonstrate a mastery of
competencies through a variety
of means. Portfolio development, test taking, and project
completion are common
applications. Best practices in competency-based education
assert that curriculum
be aligned with specific competencies as much as possible.

By way of example, the University of Vermont’s MPA Program
has had a “systems
thinking” focus since it was first conceived in the middle 1980s.
Within the last 10
years, the two chapter coauthors, along with several core faculty
who have been
associated with the program since its inception, have undertaken
an effort to refine
its mission based on its original systems-focused orientation.
As of 2010, the program mission was refined to read:
Our MPA program is a professional interdisciplinary degree that
prepares pre and in-service
leaders, managers and policy analysts by combining the
theoretical and practical founda-
tions of public administration focusing on the complexity of
governance systems and the
democratic, collaborative traditions that are a hallmark of
Vermont communities.
The mission was revised to include leaders and managers, as
well as policy analysts.
A theory-practice link was made explicit. The phrase,
“complexity of governance
systems” was selected to align with a commonly shared view of
contemporary gover-
nance as a multisectoral and multijurisdictional context.
Concepts such as bounded
rationality, social complexity, the importance of systems
feedback, and path de-
pendency are stressed throughout the curriculum. The sense of
place found within
the State of Vermont was also recognized and used to highlight
the high levels of
engagement found within the program.

The capacities laid out in Table 2.1 have been mapped to the
program’s core
curriculum. The program’s current core is a set of five courses:
PA 301: Foundations
of Public Administration; PA 302: Organizational Behavior and
Change; PA 303:
Research Methods; PA 305: Public and Nonprofit Budgeting and
Finance and PA
306: Policy Systems. In addition, all students are required to
undertake a three-
credit internship and a three-credit Capstone experience in
which they construct a
[email protected]
final learning portfolio. It is within this final portfolio that
students are expected to
provide evidence of meeting or exceeding the standard. An
expanded rubric of all
18 capacities is used by the students to undertake their own
self-assessment. These
assessments are judged against the Capstone instructor’s
evaluation.
In 2009, the MPA faculty revised the core curriculum to align
with the core
competencies. Several course titles and content were revised to
align with these
competencies and the overall systems’ focus of our mission. The
two core courses
taught by the two coauthors, PA 301 and PA 306, are

highlighted here.
2.4 PA 301: Foundations of Public Administration
Designed as a survey of the prevailing public administration
literature during the past
200 plus years, Foundations of Public Administration is
arranged across a continuum
of interconnected themes and topics that are to be addressed in
more in-depth in other
courses and is described in the syllabus in the following way:
This class is designed to provide you with an overview of the
field of public administra-
tion. You will explore the historical foundations, the major
theoretical, organizational, and
political breakthroughs, and the dynamic tensions inherent to
public and nonprofit sector
administration. Special attention will be given to problems
arising from political imperatives
generated within a democratic society.
Each week a series of classic and contemporary texts are read
and reviewed by the
students. In part, to fill a noticeable void in the literature, the
authors co-wrote, along
with Jack Meek, a book on governance networks called:
Governance Networks in
Public Administration and Public Policy (Koliba et al. 2010).
This book is required
reading. Students are also asked to purchase Shafritz and
Hyde’s edited volume,
Classics of Public Administration.
Current events assignments offered through blog posts are
undertaken. Weekly

themes include: the science and art of administration; citizens
and the administra-
tive state; nonprofit, private, and public sector differences;
governance networks;
accountability; and performance management.
During the 2009 reforms of the core curriculum, discrete units
on governance
networks and performance management were added to this
course. Throughout the
entire course, a complex systems lens is employed to describe
and analyze gover-
nance networks and the particular role that performance
management systems play
in providing feedback to governance actors. Students are
exposed to social network
and system dynamics theory, and asked to apply these lenses to
several written cases
taken from the Electronic Hallway. A unit on performance
management systems and
their role within fostering organizational learning are provided
along with readings
and examples of decision support tools and dashboard platforms
currently in use by
government agencies.
Across many units, including units on trends and reforms,
ethical and reflective
leadership, citizens and the administrative state, and
accountability, the increasing
use of social media and other forms of information technology
are discussed. Trends
[email protected]

Informatics 29
shaping the “e-governance” and “e-government” movements
serve as a major focus
on current trends. In addition, students are exposed to current
examples of data
visualizations and open data platforms and asked to consider
their uses.
2.5 PA 306: Policy Systems
Policy Systems is an entry-level graduate policy course
designed to give the MPA
student an overview of the policy process. In 2009, the course
was revised to reflect
a more integrated systems focus. The following text provides an
overview of the
course:
In particular, the emphasis is placed upon meso-, and macro-
scale policy system frame-
works and theories, such as Institutional Analysis and
Development Framework, the Multiple
Streams Framework; Social Construction and Policy Design; the
Network Approach; Punc-
tuated Equilibrium Theory; the Advocacy Coalition Framework;
Innovation and Diffusion
Models and Large-N Comparative Models. Further, students
will apply these micro-, meso-
and macro-scale theories to a substantive policy problem that is
of interest to a community
partner, which could be a government agency or a non-profit
organization. These policy
problems may span, or even cut across, a broad range of policy

domains such as (included
but not limited to) economic policy, food policy, environmental
policy, defense and foreign
policy, space policy, homeland security, disaster and emergency
management, social policy,
transportation policy, land-use policy and health policy.
The core texts for this class are Elinor Ostrom’s, Understanding
Institutional Di-
versity, Paul Sabatier’s edited volume, Theories of the Policy
Process, and Deborah
Stone’s Policy Paradox: The Art of Political Decision-Making.
The course itself is
staged following a micro, to meso, to macro level scale of
policy systems framework.
A service-learning element is incorporated. Students are taught
to view the policy
process through a systems lens. Zia employs examples of policy
systems models us-
ing system dynamics (SD), agent-based modeling (ABM), social
network analysis
(SNA), and hybrid approaches throughout the class. By drawing
on Ostrom, Sabatier,
and other meso level policy processes as a basis, students are
exposed to a number of
“complexity-friendly” theoretical policy frameworks (Koliba
and Zia 2013). Appre-
ciating the value of these policy frameworks, students are
provided with heuristics
for understanding the flow of information across a system. In
addition, students are
shown examples of simulation models of different policy
processes, streams, and
systems.
In addition to PA 301 and PA 306, students are also provided an

in-depth ex-
ploration of organization theory in PA 302 Organizational
Behavior and Change
that is taught through an organizational psychology lens that
emphasizes the role of
organizational culture and learning. “Soft systems” approaches
are applied. PA 303
Research Methods for Policy Analysis and Program Evaluation
exposes students to
a variety of research and program evaluation methodologies
with a particular focus
on quantitative analysis techniques. Within PA 305 Public and
Nonprofit Budgeting
and Finance, students are taught about evidence-based decision-
making and data
management.
w.jager[email protected]
By completing the core curriculum, students are exposed to
some of the founda-
tional competencies needed to use and shape policy informatics
projects. However,
it is not until students enroll in one of the several electives, that
more explicit policy
informatics concepts and applications are taught. Two of these
elective courses are
highlighted here. A third, PA 311 Policy Analysis, also exposes
students to policy
analyst capacities, but is not highlighted here.
2.6 PA 308: Decision-Making Models

A course designated during the original founding of the
University of Vermont
(UVM)-MPA Program, PA 308: Decision-Making Models offers
students with a
more advanced look at decision-making theory and modeling.
The course is described
by Zia in the following manner:
In this advanced graduate level seminar, we will explore and
analyze a wide range of norma-
tive, descriptive and prescriptive decision making models. This
course focuses on systems
level thinking to impart problem-solving skills in complex
decision-making contexts. Deci-
sion making problems in the real-world public policy, business
and management arenas will
be analyzed and modeled with different tools developed in the
fields of Decision Analysis,
Behavioral Sciences, Policy Sciences and Complex Systems.
The emphasis will be placed
on imparting cutting edge skills to enable students to design and
implement multiple criteria
decision analysis models, decision making models under risk
and uncertainty and computer
simulation models such as Monte Carlo simulation, system
dynamic models, agent based
models, Bayesian decision making models, participatory and
deliberative decision making
models, and interactive scenario planning approaches. AnyLogic
version 6.6 will be made
available to the students for working with some of these
computer simulation models.
2.7 PA 317: Systems Analysis and Strategic Management

Another course designate during the early inception of the
program, systems analysis
and strategic management is described by Koliba in the course
syllabus as follows:
This course combines systems and network analysis with
organizational learning theory and
practices to provide students with a heightened capacity to
analyze and effectively operate in
complex organizations and networks. The architecture for the
course is grounded in many of
the fundamental conceptual frameworks found in network,
systems and complexity analysis,
as well as some of the fundamental frameworks employed
within the public administration
and policy studies fields. In this course, strategic management
and systems analysis are
linked together through the concept of situational awareness and
design principles. Several
units focusing on teaching network analysis tools using UCINet
have been incorporated.
One of the key challenges to offering these informatics-oriented
electives lies in the
capacities that the traditional MPA students possess to thrive
within them. Increas-
ingly, these elective courses are being populated by doctoral
and master of science
students looking to apply what they are learning to their
dissertations or thesis. Our
MPA program offers a thesis option and we have had some
success with these more
[email protected]

Informatics 31
professionally oriented students undertaking high quality
informatics focused thesis.
Our experience begs a larger question pertaining to the degree
to which the baseline
informatics-savvy public manager capacities lead into more
complex policy analysts
competencies associated with the actual design and construction
of policy informatics
projects, programs, and platforms.
Table 2.2 provides an overview of where within the curriculum
certain policy
informatics capacities are covered. When associated with the
class, students are
exposed to the uses of informatics projects, programs, or
platforms or provided
opportunities for concrete skill development.
The University of Vermont context is one that can be replicated
in other programs.
The capacity of the MPA program to offer these courses hinges
on the expertise of
two faculties who teach in the core and these two electives.
With additional re-
sources, a more advanced curriculum may be pursued, one that
pursues closer ties
with the computer science department (Zia has a secondary
appointment) around
curricular alignment. Examples of more advanced curriculum to
support the devel-
opment of policy informatics analysts may be found at such
institutions as Carnegie

Mellon University, Arizona State University, George Mason
University, University
at Albany, Delft University of Technology, Massachusetts
Institute of Technology,
among many others. The University of Vermont case suggests,
however, that pol-
icy informatics education can be integrated into the main stream
with relatively low
resource investments leveraged by strategic relationships with
other disciplines and
core faculty with the right skills, training, and vision.
2.8 Conclusion
It is difficult to argue that with the advancement of high speed
computing, the dig-
itization of data and the increasing collaboration occurring
around the development
of informatics projects, programs, and platforms, that the
educational establishment,
particularly at the professional master degree levels, will need
to evolve. This chap-
ter lays out a preliminary look at some of the core competencies
and capacities that
public managers and policy analysts will need to lead the next
generation of policy
informatics integration.
[email protected]
Table 2.2 Policy informatics capacities covered within the
UVM-MPA program curriculum

Course title Policy informatics-savvy public
management capacities covered
Policy informatics analysis
capacities covered
PA 301: Foundations of
public administration
Systems thinking
Policy as process
Performance management
Financial management
Basic communication
Social media/IT/e-governance
Collaborative–cooperative capacity
building
Data visualization and
design
PA 306: Policy systems Systems thinking
Policy as process Basic
communication
Basic modeling skills
PA 302: Organizational
behavior and change
Systems thinking Basic
communication
building

PA 303: Research methods
for policy analysis and
program evaluation
Research methods
Basic communication
design
PA 305: Public and
nonprofit budgeting and
finance
Financial management
Basic communication
PA 308: Decision-making
modeling
Systems thinking
Policy as process
Research methods
Advanced research methods
design techniques
PA 311: Policy analysis Systems thinking
Policy as process
Research methods

Basic communication
Advanced research methods
design
PA 317: Systems analysis
and strategic analysis
Systems thinking
Policy as process
Research methods
building
Basic communication
design
[email protected]
Informatics 33
2.9 Appendix A: University of Vermont’s MPA Program
Learning Competencies and Capacities
NASPAA core standard UVM-MPA learning capacity
To lead and manage in

public governance
Capacity to understand accountability and democratic theory
Capacity to manage the lines of authority for public, private,
and
nonprofit collaboration, and to address sectorial differences to
overcome obstacles
Capacity to apply knowledge of system dynamics and network
structures in PA practice
Capacity to carry out effective policy implementation
To participate in and
contribute to the policy
process
Capacity to apply policy streams, cycles, systems foci upon
past,
present, and future policy issues, and to understand how
problem
identification impacts public administration
Capacity to conduct policy analysis/evaluation
Capacity to employ quantitative and qualitative research
methods for
program evaluation and action research
To analyze, synthesize,
think critically, solve
problems, and make
decisions
Capacity to initiate strategic planning, and apply organizational

learning and development principles
Capacity to apply sound performance measurement and
management
practices
Capacity to apply sound financial planning and fiscal
responsibility
Capacity to employ quantitative and qualitative research
methods for
program evaluation and action research
To articulate and apply a
public service
perspective
Capacity to understand the value of authentic citizen
participation in
PA practice
Capacity to understand the value of social and economic equity
in
PA practices
Capacity to lead in an ethical and reflective manner
Capacity to achieve cooperation through participatory practices
To communicate and
interact productively
with a diverse and
changing workforce and
citizenry
Capacity to undertake high quality oral, written, and

electronically
mediated communication and utilize information systems and
media
to advance objectives
Capacity to appreciate the value of pluralism, multiculturalism,
and
cultural diversity
Capacity to carry out effective human resource management
Capacity to undertake high quality oral, written, and
electronically
mediated communication and utilize information systems and
media
to advance objectives
NASPAA Network of Schools of Public Affairs and
Administration, UVM University of Vermont,
MPA Master of Public Administration, PA Public administration
[email protected]
References
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method, and practice. Addison-
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Endsley MR (1995) Toward a theory of situation awareness in
dynamic systems. Hum Fact 37(1):32–
64
Fernandez L (2013) An ESRD system dynamics population
model for the United States. Final
project for PA 308
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a difference? J Public Aff Educ
15(4):397–424
Katz D, Khan R (1978) The social psychology of organizations.
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(eds) COMPACT I: public administration in complexity.
Emergent, Litchfield Park

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administration and public policy.
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to manage complex governance networks. Innov J Innov Publ
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[email protected]
Chapter 3
The Quality of Social Simulation: An Example
from Research Policy Modelling
Petra Ahrweiler and Nigel Gilbert
Abstract This chapter deals with the assessment of the quality
of a simulation. The
first section points out the problems of the standard view and
the constructivist view
in evaluating social simulations. A simulation is good when we
get from it what
we originally would have liked to get from the target; in this,
the evaluation of
the simulation is guided by the expectations, anticipations, and
experience of the
community that uses it. This makes the user community view
the most promising
mechanism to assess the quality of a policy-modelling exercise.
The second section
looks at a concrete policy-modelling example to test this idea. It

shows that the very
first negotiation and discussion with the user community to
identify their questions
is highly user-driven, interactive, and iterative. It requires
communicative skills,
patience, willingness to compromise on both sides, and
motivation to make the
formal world of modellers and the narrative world of practical
policy making meet.
Often, the user community is involved in providing data for
calibrating the model. It
is not an easy issue to confirm the existence, quality, and
availability of data and check
for formats and database requirements. As the quality of the
simulation in the eyes of
the user will very much depend on the quality of the informing
data and the quality
of the model calibration, much time and effort need to be spent
in coordinating this
issue with the user community. Last but not least, the user
community has to check
the validity of simulation results and has to believe in their
quality. Users have to be
enabled to understand the model, to agree with its processes and
ways to produce
results, to judge similarity between empirical and simulated
data, etc. Although the
user community view might be the most promising, it is the
most work-intensive
mechanism to assess the quality of a simulation. Summarising,
to trust the quality
of a simulation means to trust the process that produced its
results. This process
includes not only the design and construction of the simulation
model itself but also
the whole interaction between stakeholders, study team, model,

and findings.
P. Ahrweiler (�)
EA European Academy of Technology and Innovation
Assessment GmbH,
Bad Neuenahr-Ahrweiler, Germany
N. Gilbert
University of Surrey, Guildford, UK
10.1007/978-3-319-12784-2_3
[email protected]
36 P. Ahrweiler and N. Gilbert
Table 3.1 Comparing simulations
Caffè Nero simulation Science simulation
Target Venetian Café “Real system”
Goal Getting “the feeling” (customers) and
profit (owners) from it
Getting understanding and/or predictions
from it
Model By reducing the many features of a
Venetian Café to a few parameters

By reducing the many features of the
target to a few parameters
Question Is it a good simulation, i.e. do we get
from it what we want?
Is it a good simulation, i.e. do we get
from it what we want?
This chapter deals with the assessment of the quality of a
simulation. After dis-
cussing this issue on a general level, we apply and test the
assessment mechanisms
using an example from policy modelling.
3.1 Quality in Social Simulation
The construction of a scientific social simulation implies the
following process: “We
wish to acquire something from a target entity T. We cannot get
what we want from
T directly. So, we proceed indirectly. Instead of T we construct
another entity M, the
‘model’, which is sufficiently similar to T that we are confident
that M will deliver
(or reveal) the acquired something which we want to get from T.
[. . .] At a moment in
time, the model has structure. With the passage of time the
structure changes and that
is behaviour. [. . .] Clearly we wish to know the behaviour of
the model. How? We
may set the model running (possibly in special sets of
circumstances of our choice)
and watch what it does. It is this that we refer to as‘simulation’
of the target” (quoted
with slight modifications from Doran and Gilbert 1994).

We also habitually refer to “simulations” in everyday life,
mostly in the sense
that a simulation is “an illusory appearance that manages a
reality effect” (cf. Norris
1992), or as Baudrillard put it, “to simulate is to feign to have
what one hasn’t”
while “substituting signs for the real” (Baudrillard 1988). In a
previous publication
(Ahrweiler and Gilbert 2005), we used the example of the Caffè
Nero in Guildford,
50 km southwest of London, as a simulation of a Venetian
café—which will serve
as the “real” to illustrate this view. The purpose of the café is to
“serve the best
coffee north of Milan”. It tries to give the impression that you
are in a real Italian
café—although, most of the time, the weather outside can make
the illusion difficult
to maintain.
The construction of everyday simulations like Caffè Nero has
some resemblance
to the construction of scientific social simulations (see Table
3.1):
In both cases, we build models from a target by reducing the
characteristics of the
latter sufficiently for the purpose at hand; in each case, we want
something from the
model we cannot achieve easily from the target. In the case of
Caffè Nero, we cannot
simply go to Venice, drink our coffee, be happy, and return. It
is too expensive and
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Policy Modelling 37
time-consuming. We have to use the simulation. In the case of a
science simulation,
we cannot get data from the real system to learn about its
behaviour. We have to use
the simulation.
The question, whether one or the other is a good simulation, can
therefore be
reformulated as: Do we get from the simulation what we
constructed it for?
Heeding these similarities, we shall now try to apply evaluation
methods typically
used for everyday simulations to scientific simulation and vice
versa. Before doing
so, we shall briefly discuss the “ordinary” method of evaluating
simulations called
the “standard view” and its adversary, a constructivist approach
asserting, “anything
goes”.
3.1.1 The Standard View
The standard view refers to the well-known questions and
methods of verification,
namely whether the code does what it is supposed to do and
whether there are
any bugs, and validation, namely whether the outputs (for given
inputs/parameters)
resemble observations of the target, although (because the

processes being modelled
are stochastic and because of unmeasured factors) identical
outputs are not to be
expected, as discussed in detail in Gilbert and Troitzsch (1997).
This standard view
relies on a realist perspective because it refers to the
observability of reality in order
to compare the “real” with artificial data produced by the
simulation.
Applying the standard view to the Caffè Nero example, we can
find quantitative
and sometimes qualitative measures for evaluating the
simulation. Using quantitative
measures of similarity between it and a “real” Venetian café, we
can ask, for example,
• Whether the coffee tastes the same (by measuring, for
example, a quality score at
blind tasting),
• Whether the Caffè is a cool place (e.g. measuring the relative
temperatures inside
and outside),
• Whether the noise level is the same (using a dB meter for
measuring pur-
poses),whether the lighting level is the same (using a light
meter), and
• Whether there are the same number of tables and chairs per
square metre for the
customers (counting them), and so on.
In applying qualitative measures of similarity, we can again
ask:

• Whether the coffee tastes the same (while documenting what
comes to mind when
customers drink the coffee),
• Whether the Caffè is a “cool” place (this time meaning
whether it is a fashionable
place to hang out),
• Whether it is a vivid, buzzing place, full of life (observing the
liveliness of groups
of customers),
• Whether there is the same pattern of social relationships
(difficult to opera-
tionalise: perhaps by observing whether the waiters spend their
time talking to
the customers or to the other staff), and
[email protected]
• Whether there is a ritual for serving coffee and whether it is
felt to be the same as
in a Venetian café.
The assumption lying behind these measures is that there is a
“real” café and a
“simulation” café and that in both of these, we can make
observations. Similarly,
we generally assume that the theories and models that lie at the
base of science
simulations are well grounded and can be validated by

observation of empirical
facts. However, the philosophy of science forces us to be more
modest.
3.1.1.1 The Problem of Under-determination
Some philosophers of science argue that theories are under-
determined by observa-
tional data or experience, that is, the same empirical data may
be in accord with
many alternative theories. An adherent of the standard view
would respond in that
one important role of simulations (and of any form of model
building) is to derive
from theories as many testable implications as possible so that
eventually validity
can be assessed in a cumulative process1. Simulation is indeed a
powerful tool for
testing theories in that way if we are followers of the standard
view.
However, the problem that theories are under-determined by
empirical data can-
not be solved by cumulative data gathering: it is more general
and therefore more
serious. The under-determination problem is not about a missing
quantity of data
but about the relation between data and theory. As Quine (1977)
presents it: If it is
possible to construct two or more incompatible theories by
relying on the same set
of experimental data, the choice between these theories cannot
depend on “empirical
facts”. Quine showed that there is no procedure to establish a
relation of uniqueness
between theory and data in a logically exclusive way. This

leaves us with an annoying
freedom: “sometimes, the same datum is interpreted by such
different assumptions
and theoretical orientations using different terminologies that
one wonders whether
the theorists are really thinking of the same datum” (Harbodt
1974, p. 258 f., own
translation).
The proposal mentioned above to solve the under-determination
problem by sim-
ulation does not touch the underlying reference problem at all.
It just extends the
theory, adding to it its “implications”, hoping them to be more
easily testable than
the theory’s core theorems. The general reference between
theoretical statement—
be it implication or core theorem—and observed data has not
changed by applying
this extension: The point here is that we cannot establish a
relation of uniqueness
between the observed data and the theoretical statement. This
applies to any segment
of theorising at the centre or at the periphery of the theory on
any level—a matter
that cannot be improved by a cumulative strategy.
1 We owe the suggestion that simulation could be a tool to make
theories more determined by data
to one of the referees of Ahrweiler and Gilbert (2005).
[email protected]

Policy Modelling 39
3.1.1.2 The Theory-Ladenness of Observations
Observations are supposed to validate theories, but in fact
theories guide our ob-
servations, decide on our set of observables, and prepare our
interpretation of the
data. Take, for example, the different concepts of two authors
concerning Venetian
cafés: For one, a Venetian café is a quiet place to read
newspapers and relax with a
good cup of coffee; for the other, a Venetian café is a lively
place to meet and talk
to people with a good cup of coffee. The first attribute of these
different conceptions
of a Venetian café is supported by one and the same observable,
namely the noise
level, although one author expects a low level, the other a high
one. The second
attribute is completely different: the first conception is
supported by a high number
of newspaper readers, the second by a high number of people
talking. Accordingly,
a “good” simulation would mean a different thing for each of
the authors. A good
simulation for one would be a poor simulation for the other and
vice versa. Here,
you can easily see the influence of theory on the observables.
This example could
just lead to an extensive discussion about the “nature” of a
Venetian café between
two authors, but the theory-ladenness of observations again
leads to more serious
difficulties. Our access to data is compromised by involving
theory, with the con-

sequence that observations are not the “bed rock elements”
(Balzer et al. 1987) our
theories can safely rely on. At the very base of theory is again
theory. The attempt
to validate our theories by “pure” theory-neutral observational
concepts is mistaken
from the beginning.
Balzer et al. summarise the long debate about the standard view
on this issue as
follows: “First, all criteria of observability proposed up to now
are vulnerable to
serious objections. Second, these criteria would not contribute
to our task because
in all advanced theories there will be no observational concepts
at all—at least if
we take ‘observational’ in the more philosophical sense of not
involving any theory.
Third, it can be shown that none of the concepts of an advanced
theory can be defined
in terms of observational concepts” (Balzer et al. 1987, p. 48).
Not only can you
not verify a theory by empirical observation, but you cannot
even be certain about
falsifying a theory. A theory is not validated by “observations”
but by other theories
(observational theories). Because of this reference to other
theories, in fact a nested
structure, the theory-ladenness of each observation has negative
consequences for the
completeness and self-sufficiency of scientific theories (cf.
Carrier 1994, pp. 1–19).
These problems apply equally to simulations that are just
theories in process.
We can give examples of these difficulties in the area of social

simulation. To
compare Axelrod’s The Evolution of Cooperation (Axelrod
1984) and all the subse-
quent work on iterated prisoners’ dilemmas with the “real
world”, we would need
to observe “real” IPDs, but this cannot be done in a theory-
neutral way. The same
problems arise with the growing body of work on opinion
dynamics (e.g. Deffuant
et al. 2000; Ben-Naim et al. 2003; Weisbuch 2004). The latter
starts with some sim-
ple assumptions about how agents’ opinions affect the opinions
of other agents, and
shows under which circumstances the result is a consensus,
polarisation, or fragmen-
tation. However, how could these results be validated against
observations without
involving again a considerable amount of theory?
[email protected]
Important features of the target might not be observable at all.
We cannot, for
example, observe learning. We can just use some indicators to
measure the conse-
quences of learning and assume that learning has taken place. In
science simulations,
the lack of observability of significant features is one of the
prime motivations for
carrying out a simulation in the first place.
There are also more technical problems. Validity tests should be

“exercised over a
full range of inputs and the outputs are observed for
correctness” (Cole 2000, p. 23).
However, the possibility of such testing is rejected: “real life
systems have too many
inputs, resulting in a combinatorial explosion of test cases”.
Therefore, simulations
have “too many inputs/outputs to be able to test strictly” (Cole
2000, p. 23).
While this point does not refute the standard view in principle
but only emphasises
difficulties in execution, the former arguments reveal problems
arising from the
logic of validity assessment. We can try to marginalise, neglect,
or even deny these
problems, but this will disclose our position as mere “believers”
of the standard view.
3.1.2 The Constructivist View
Validating a simulation against empirical data is not about
comparing “the real world”
and the simulation output; it is comparing what you observe as
the real world with
what you observe as the output. Both are constructions of an
observer and his/her
views concerning relevant agents and their attributes.
Constructing reality and simu-
lation are just two ways of an observer seeing the world. The
issue of object formation
is not normally considered by computer scientists relying on the
standard view: data
is “organized by a human programmer who appropriately fits
them into the chosen
representational structure. Usually, researchers use their prior

knowledge of the na-
ture of the problem to hand-code a representation of the data
into a near-optimal
form. Only after all this hand-coding is completed is the
representation allowed to
be manipulated by the machine. The problem of representation-
formation [. . .] is
ignored” (Chalmers et al. 1995, p. 173).
However, what happens if we question the possibility of
validating a simulation
by comparing it with empirical data from the “real world”? We
need to refer to the
modellers/observers in order to get at their different
constructions. The constructivists
reject the possibility of evaluation because there is no common
“reality” we might
refer to. This observer-oriented opponent of the realist view is a
nightmare to most
scientists: “Where anything goes, freedom of thought begins.
And this freedom of
thought consists of all people blabbering around and everybody
is right as long as
he does not refer to truth. Because truth is divisible like the
coat of Saint Martin;
everybody gets a piece of it and everybody has a nice feeling”
(Droste 1994, p. 50).
Clearly, we can put some central thoughts from this view much
more carefully: “In
dealing with experience, in trying to explain and control it, we
accept as legitimate
and appropriate to experiment with different conceptual
settings, to combine the flow
of experience to different ‘objects”’ (Gellner 1990, p. 75).

[email protected]
Policy Modelling 41
However, this still leads to highly questionable consequences:
There seems to
be no way to distinguish between different
constructions/simulations in terms of
“truth”, “objectivity”, “validity”, etc. Science is going
coffeehouse: Everything is
just construction, rhetoric, and arbitrary talk. Can we so easily
dismiss the possibility
of evaluation?
3.1.3 The User Community View
We take refuge at the place we started from: What happens if
we go back to the
Venetian café simulation and ask for an evaluation of its
performance? It is probably
the case that most customers in the Guildford Caffè Nero have
never been to an
Italian café. Nevertheless, they manage to “evaluate” its
performance—against their
concept of an Italian café that is not inspired by any “real” data.
However, there is
something “real” in this evaluation, namely the customers, their
constructions, and
a “something” out there, which everybody refers to, relying on
some sort of shared
meaning and having a “real” discussion about it. The
philosopher Searle shows in
his work on the Construction of Social Reality (Searle 1997)

how conventions are
“real”: They are not deficient for the support of a relativistic
approach because they
are constructed.
Consensus about the “reality observed by us” is generated by an
interaction pro-
cess that must itself be considered real. At the base of the
constructivist view is a
strong reference to reality, that is, conventions and expectations
that are socially cre-
ated and enforced. When evaluating the Caffè Nero simulation,
we can refer to the
expert community (customers, owners) who use the simulation
to get from it what
they would expect to get from the target. A good simulation for
them would satisfy
the customers who want to have the “Venetian feeling” and
would satisfy the owners
who want to get the “Venetian profit”.
For science, equally, the foundation of every validity discussion
is the ordinary
everyday interaction that creates an area of shared meanings and
expectations. This
area takes the place left open by the under-determination of
theories and the theo-
reticity problem of the standard view.2 Our view comes close to
that of empirical
epistemology, which points out that the criteria for quality
assessment “do not come
from some a priori standard but rest on the description of the
way research is actually
conducted” (Kértesz 1993, p. 32).
2 Thomas Nickles claims new work opportunities for sociology

at this point: “the job of phi-
losophy is simply to lay out the necessary logico-
methodological connections against which the
under-determination of scientific claims may be seen; in other
words, to reveal the necessity of so-
ciological analysis. Philosophy reveals the depths of the under-
determination problem, which has
always been the central problem of methodology, but is
powerless to do anything about it. Under-
determination now becomes the province of sociologists, who
see the limits of under-determination
as the bounds of sociology. Sociology will furnish the
contingent connections, the relations, which
a priori philosophy cannot” (Nickles 1989, p. 234 f.).
[email protected]
If the target for a social science simulation is itself a
construction, then the simu-
lation is a second-order construction. In order to evaluate the
simulation, we can rely
on the ordinary (but sophisticated) institutions of (social)
science and their practice.
The actual evaluation of science comes from answers to
questions such as: Do others
accept the results as being coherent with existing knowledge?
Do other scientists use
it to support their work? Do other scientists use it to inspire
their own investigations?
An example of such validity discourse in the area of social
simulation is the

history of the tipping model first proposed by Schelling, and
now rather well known
in the social simulation community. The Schelling model
purports to demonstrate the
reasons for the persistence of urban residential segregation in
the USA and elsewhere.
It consists of a grid of square cells, on which are placed agents,
each either black or
white. The agents have a “tolerance” for the number of agents
of the other colour
in the surrounding eight cells that they are content to have
around them. If there are
“too many” agents of the other colour, the unhappy agents move
to other cells until
they find a context in which there are a tolerable number of
other-coloured agents.
Starting with a random distribution, even with high levels of
tolerance, the agents
will still congregate into clusters of agents of the same colour.
The point Schelling
and others have taken from this model is that residential
segregation will form and
persist even when agents are rather tolerant.
The obvious place to undertake a realist validation of this model
is a US city. One
could collect data about residential mobility and, perhaps, on
“tolerance”. However,
the exercise is harder than it looks. Even US city blocks are not
all regular and
square, so the real city does not look anything like the usual
model grid. Residents
move into the city from outside, migrate to other cities, are born
and die, so the tidy
picture of mobility in the model is far from the messy reality.
Asking residents how

many people of the other colour they would be tolerant of is
also an exercise fraught
with difficulty: the question is hypothetical and abstract, and
answers are likely
to be biased by social desirability considerations.
Notwithstanding these practical
methodological difficulties, some attempts have been made to
verify the model. The
results have not provided much support. For instance, Benenson
(2005) analysed
residential distribution for nine Israeli cities using census data
and demonstrated that
whatever the variable tested—family income, number of
children, education level—
there was a great deal of ethnic and economic heterogeneity
within neighbourhoods,
contrary to the model’s predictions.
This apparent lack of empirical support has not, however,
dimmed the fame of the
model. The difficulty of obtaining reliable data provides a ready
answer to doubts
about whether the model is “really” a good representation of
urban segregation dy-
namics. Another response has been to elaborate the model at the
theoretical level.
For instance, Bruch (2005) demonstrates that clustering only
emerges in Schelling’s
model for discontinuous functional forms for residents’
opinions, while data from
surveys suggest that people’s actual decision functions for race
are continuous. She
shows that using income instead of race as the sorting factor
also does not lead to
clustering, but if it is assumed that both race and income are
significant, segregation

appears. Thus, the model continues to be influential, although it
has little or no em-
pirical support, because it remains a fruitful source for
theorising and for developing
[email protected]
Policy Modelling 43
new models. In short, it satisfies the criterion that it is “valid”
because it generates
further scientific work.
Summarising the first part of this chapter, we have argued that a
simulation is good
when we get from it what we originally would have liked to get
from the target. It is
good if it works. As Glasersfeld (1987, p. 429) puts it:
“Anything goes if it works”.
The evaluation of the simulation is guided by the expectations,
anticipations and
experience of the community that uses it—for practical purposes
(Caffè Nero), or
for intellectual understanding and for building new knowledge
(science simulation).
3.2 An Example of Assessing Quality
In this part, we will apply and test the assessment mechanisms
outlined using as an
example our work with the simulating knowledge dynamics in
innovation networks
(SKIN) model in its application to research policy modelling.

There are now a number of policy-modelling studies using
SKIN (Gilbert
et al. 2014). We will here refer to just one recent example, on
the impact, assess-
ment and ex-ante evaluation of European funding policies in the
Information and
Communication Technologies (ICT) research domain (Ahrweiler
et al. 2014b).
3.2.1 A Policy-Modelling Application of SKIN
The basic SKIN model has been described and discussed in
detail elsewhere
(e.g. Pyka et al. 2007; Gilbert et al. 2007; Ahrweiler et al.
2011). On its most general
level, SKIN is an agent-based model where agents are
knowledge-intensive organi-
sations, which try to generate new knowledge by research, be it
basic or applied, or
creating new products and processes by innovation processes.
Agents are located in
a changing and complex social environment, which evaluates
their performance; e.g.
the market if the agents target innovation or the scientific
community if the agents
target publications through their research activities. Agents
have various options to
act: each agent has an individual knowledge base called its
“kene” (cf. Gilbert 1997),
which it takes as the source and basis for its research and
innovation activities. The
agent kene is not static: the agent can learn, either alone by
doing incremental or
radical research, or from others, by exchanging and improving
knowledge in partner-

ships and networks. The latter feature is important, because
research and innovation
happens in networks, both in science and in knowledge-
intensive industries. This
is why SKIN agents have a variety of strategies and mechanisms
for collaborative
arrangements, i.e. for choosing partners, forming partnerships,
starting knowledge
collaborations, creating collaborative outputs, and distributing
rewards. Summaris-
ing, usually a SKIN application has agents interacting on the
knowledge level and
on the social level while both levels are interconnected. It is all
about knowledge and
networks.
[email protected]
This general architecture is quite flexible, which is why the
SKIN model has been
called a “platform” (cf. Ahrweiler et al. 2014a), and has been
used for a variety of
applications ranging from the small such as simulating the
Vienna biotech cluster
(Korber and Paier 2014) to intermediate such as simulating the
Norwegian defence
industry (Castelacci et al. 2014), to large-scale applications
such as the EU-funded
ICT research landscape in Europe (Ahrweiler et al. 2014b). We
will use the latter
study as an example after explaining why the SKIN model is
appropriate for realistic

policy modelling in particular.
The birth of the SKIN model was inspired by the idea of
bringing a theory on
innovation networks, stemming mainly from innovation
economics and economic so-
ciology, onto the computer—a computer theory, which can be
instantiated, calibrated,
tested, and validated by empirical data. In 1998, the first EU
project developing the
model “Simulating Self-Organizing Innovation Networks”
(SEIN) consisted of a
three-step procedure: theory formation, empirical research
collecting data both on
the quantitative and on the case study level, and agent-based
modelling implementing
the theory and using the data to inform the model (Pyka et al.
2003).
This is why the SKIN model applications use empirical data and
claim to be
“realistic simulations” insofar as the aim is to derive
conclusions by “inductive the-
orising”. The quality of the SKIN simulation derives from an
interaction between
the theory underlying the simulation and the empirical data used
for calibration and
validation.
In what way does the SKIN model handle empirical data? We
will now turn
to our policy-modelling example to explain the data-to-model
workflow, which is
introduced in greater detail in Schilperoord and Ahrweiler
(2014).

3.2.1.1 Policy Modelling for Ex-ante Evaluation of EU Funding
Programmes
The INFSO-SKIN application, developed for the Directorate
General Information
Society and Media of the European Commission (DG INFSO),
was intended to help
to understand and manage the relationship between research
funding and the goals
of EU policy. The agents of the INFSO-SKIN application are
research institutions
such as universities, large diversified firms or small and
medium-sized enterprises
(SMEs). The model (see Fig. 3.1) simulated real-world activity
in which the calls
of the commission specify the composition of consortia, the
minimum number of
partners, and the length of the project; the deadline for
submission; a range of
capabilities, a sufficient number of which must appear in an
eligible proposal; and
the number of projects that will be funded. The rules of
interaction and decision
implemented in the model corresponded to Framework
Programme (FP) rules; to
increase the usefulness for policy designers, the names of the
rules corresponded
closely to FP terminology. For the Calls 1–6 that had occurred
in FP7, the model
used empirical information on the number of participants and
the number of funded
projects, together with data on project size (as measured by
participant numbers),
duration and average funding. Analysis of this information
produced data on the
functioning of, and relationships within, actual collaborative

networks within the
[email protected]
Policy Modelling 45
Fig. 3.1 Flowchart of INFSO-SKIN
context of the FP. Using this data in the model provided a good
match with the
empirical data from EU-funded ICT networks in FP7: the model
accurately reflected
what actually happened and could be used as a test bed for
potential policy choices
(cf. Ahrweiler et al. 2014b).
Altering elements of the model that equate to policy
interventions, such as the
amount of funding, the size of consortia, or encouraging
specific sections of the
research community, enabled the use of INFSO-SKIN as a tool
for modelling and
evaluating the results of specific interactions between policies,
funding strategies
and agents. Because changing parameters within the model is
analogous to applying
different policy options in the real world, the model could be
used to examine the
likely real-world effects of different policy options before they
were implemented.
3.2.1.2 The Data-to-Model Workflow

The first contact with “the real world” occurred in the definition
phase of the project.
What do the stakeholders want to know in terms of policies for
a certain research or
innovation network? Identifying relevant issues, discussing
interesting aspects about
them, forming questions and suggesting hypotheses for potential
answers formed a
first important step. This step was intended to conclude with a
set of questions and a
corresponding set of designs for experiments using the model
that could answer those
questions. This was an interactive and participative process
between the study team,
[email protected]
which knew about the possibilities and limitations of the model,
and the stakeholders,
who could be assumed to know what are the relevant issues in
their day-to-day
practice of policy making.
After discussing the evaluative questions for the ex-ante
evaluation part of this
study with the stakeholders from DG INFSO, the following
questions were singled
out for experiments:
1. What if there are no changes, and funding policies of DG
INFSO continued in
Horizon 2020 as they were in FP7?

2. What if there are changes to the currently eight thematic
areas funded in the ICT
domain prioritising certain areas in Horizon 2020?
3. What if there are changes to the instruments of funding and
fund larger/smaller
consortia in Horizon 2020 than in FP7?
4. What if there are interventions concerning the scope or
outreach of funding
providing much more/much less resource to more/fewer actors?
5. What if there are interventions concerning the participation
of certain actors in
the network (e.g. SMEs)?
The next step (see Fig. 3.2) was to collect relevant data to
address these questions and
hypotheses. The issues were not different from the ones every
empirical researcher
is confronted with. To identify relevant variables for
operationalising hypotheses, to
be as simple as possible but as detailed as necessary for
description and explana-
tion, is in line with the requirements of all empirical social
research. For SKIN, the
most important data are about knowledge dynamics (e.g.
knowledge flows, amount
of knowledge, and diversity of knowledge) and their indicators
(e.g. publications,
patents, and innovative ideas), and about dynamics concerning
actors, networks, their
measures, and their performance (e.g. descriptive statistics
about actors, network
analysis measures, and aggregate performance data).

These data were used to calibrate the initial knowledge bases of
the agents, the
social configurations of agents (“starting networks”), and the
configuration of an
environment at a given point in time. DG INFSO provided the
data needed to calibrate
the knowledge bases of the agents (in this case the research
organisations in the
European research area), the descriptive statistics on agents and
networks and their
interactions (in this case data on funded organisations and
projects in ICT under
FP7).
The time series data were used to validate the simulations by
comparing the
empirical data with the simulation outputs. Once we were
satisfied with the model
performance in that respect, experiments were conducted and
the artificially produced
data analysed and interpreted. The stakeholders were again
invited to provide their
feedback and suggestions about how to finetune and adapt the
study to their changing
user requirements as the study proceeded.
The last step was again stakeholder-centred as it involved
visualisation and com-
munication of data and results. We had to prove the credibility
of the work and the
commitment of the stakeholders to the policy-modelling
activity.
We worked from an already existing application of the SKIN
model adapted to the

European research area (Scholz et al. 2010), implemented the
scenarios according
[email protected]
Policy Modelling 47
Baseline
Thematic
change
Instruments
change
Funding level
change
Participants
chenge
Evaluative questions
Horizon 2020
INFSO FP7
Database
Calls, Themes,
Participants,
Projects
21

Network Vis.
& Statistics
Gephi
Scenario
Development Tool
K"5"(Java
3 4
Participants
impacts
Proposals
impacts
Projects
impacts
Knowlwdge
impacts
Network
impacts
SKIN
model
Netlogo & lava
Simulation
Database
CSV

Computational
Policy Lab
MySQL
Calls, Themes,
Participants,
Proposals, Projects,
Knowlwdge flows
Fig. 3.2 Horizon 2020 study workflow (Schilperoord and
Ahrweiler 2014). First (on the left), a set
of issues was isolated, in discussion with stakeholders. Data
describing the network of FP7 projects
and participants, by theme and Call, obtained from DG INFSO
were entered into a database.
These data were used to calibrate the INFSO-SKIN model. This
model was then used to generate
simulated data under various policy options. The simulated data
were fed into a second database
and visualised using additional network visualisation and
statistical software in order to assess the
expected impacts of those policy options
to the evaluative questions, and produced artificial data as
output of the simulations.
The results are reported in the final report presented to the
European Cabinet, and
were communicated to the stakeholders at DG INFSO.
3.2.2 The INFSO-SKIN Example as Seen by the Standard View
The standard view refers to verification, namely whether the
code does what it is sup-
posed to do, and validation, namely whether the outputs (for
given inputs/parameters)

sufficiently resemble observations of the target. To aid in
verifying the model, it was
completely recoded in another programming language and the
two implementations
cross-checked to ensure that they generated the same outputs
given the same inputs.
To enable validation of the model, we needed to create a
simulation resembling
the stakeholders’ own world as they perceived it. The
simulation needed to create the
effect of similar complexity, similar structures and processes,
and similar objects and
options for interventions. To be under this similarity threshold
would have led to the
rejection of the model as a “toy model” that is not realistic and
is under-determined
by empirical data.
[email protected]
In the eyes of these stakeholders, the more features of the model
that can be
validated against empirical data points, the better. Of course,
there will always be
an empirical “under-determination” of the model due to the
necessary selection and
abstraction process of model construction, empirical
unobservables, missing data for
observables, random features of the model, and so on. However,
to find the “right”
trade-off between empirical under-determination and model

credibility was a crucial
issue in the discussions between the study team and the
stakeholders.
3.2.3 The INFSO-SKIN Example as Seen by the Constructivist
View
The strength of a modelling methodology lies in the opportunity
to ask what-if
questions (ex-ante evaluation), an option that is normally not
easily available in
the policy-making world. INFSO-SKIN uses scenario modelling
as a worksite for
“reality constructions”, in line with Gellner’s statement quoted
above about the
constructivist approach: “In dealing with experience, in trying
to explain and control
it, we accept as legitimate and appropriate to experiment with
different conceptual
settings, to combine the flow of experience to different
‘objects”’ (Gellner 1990,
p. 75). Scenario modelling was employed in the study both for
the impact assessment
of existing funding policies, where we measured the impact of
policy measures by
experimenting with different scenarios where these policies are
absent, changed or
meet different conditions, and for ex-ante evaluation, where we
developed a range of
potential futures for the European Research Area in ICT by
asking what-if questions.
These are in-silico experiments that construct potential futures.
Is this then a
relativist approach where “anything goes”, because everything
is just a construction?

For the general aspects of this question, we refer to Part I of
this article. There we talk
about the “reality requirements” of the constructivist approach,
which mediates its
claims. For the limits of constructivist ideas applied to SKIN,
we refer to Sect. 2.1.
3.2.4 The INFSO-SKIN Example as Seen by the User
Community
View
The user community view is the most promising, although the
most work-intensive
mechanism to assess the quality of this policy-modelling
exercise.
3.2.4.1 Identifying User Questions
In our example, SKIN was applied to a tender study with a clear
client demand behind
it, where the questions the simulation needs to answer were
more or less predefined
[email protected]
Policy Modelling 49
from the onset of the project. Enough time should, however, be
dedicated to identi-
fying and discussing the exact set of questions the stakeholders
of the work want to
see addressed. We found that the best way to do this is applying
an iterative process

of communication between study team and clients, where
stakeholders learn about
the scope and applicability of the methods, and where
researchers get acquainted
with the problems policy makers have to solve and with the kind
of decisions for
which sound background information is needed. This iterative
process should result
in an agreed set of questions for the simulation, which will very
often decisively
differ from the set proposed at the start of the study. In our
example, the so-called
“steering committee” was assigned to us by the European
Commission consisting of
policy makers and evaluation experts of DG INFSO.
There are various difficulties and limitations to overcome in
identifying user ques-
tions. In the case of the DG INFSO study, although the
questions under study
were outlined in the Tender Specifications in great detail, this
was a complicated
negotiation process where the stakeholder group:
• Had to find out about the exact nature and direction of their
questions while they
talked to the study team;
• Had questioned the original set of the Tender Specifications in
the meantime and
negotiated among each other for an alternative set;
• Did not share the same opinion about what questions should be
in the final sample,
and how potential questions should be ranked in importance;

• Did not share the same hypotheses about questions in the final
sample.
The specification of evaluative questions might be the first time
stakeholders talk to
each other and discuss their viewpoints.
What is the process for identifying user questions for policy
modelling? In the
INFSO-SKIN application, the following mechanism was used by
the study team and
proved to be valuable:
• Scan written project specification by client (in this case the
Tender Specifications
of DG INFSO) and identify the original set of questions;
• Do a literature review and context analysis for each question
(policy background,
scope, meaning, etc.) to inform the study team;
• Meet stakeholders to get their views on written project
specifications and their
view on the context of questions; inform the stakeholders about
what the model is
about, what it can and cannot do; discuss until stakeholder
group and study team
is “on the same page”;
• Evaluate the meeting and revise original set of questions if
necessary (probably
an iterative process between study team and different
stakeholders individually
where study team acts as coordinator and mediator of the
process);

• Meet stakeholders to discuss the final set of questions, get
their written consent
on this, and get their hypotheses concerning potential answers
and potential ways
to address the questions;
• Evaluate the meeting and develop experiments that are able to
operationalise the
hypotheses and address the questions;
[email protected]
• Meet stakeholders and get their feedback and consent that the
experiments meet
questions/hypotheses;
• Evaluate the meeting and refine the experimental setup
concerning the final set
of questions.
This negotiation and discussion process is highly user-driven,
interactive, and itera-
tive. It requires communicative skills, patience, willingness to
compromise on both
sides, and motivation to make both ends meet—the formal world
of modellers and the
narrative world of policy making in practice. The process is
highly time-consuming.
In our example, we needed about 6 months of a 12-month-
contract research study to
get to satisfactory results on this first step.

3.2.4.2 Getting Their Best: Users Need to Provide Data
The study team will know best what types of empirical data are
needed to inform
the policy modelling. In SKIN, data availability is an important
issue, because the
findings need to be evidence-based and realistic. This is in the
best interest of the
stakeholders, who need to trust the findings. This will be the
more likely to the extent
that the simulated data resembles the empirical data known to
the user (see Sect. 2.1).
However, the study team might discover that the desired data is
not available, either
because it does not exist or because it is not willingly released
by the stakeholders
or whoever holds it.
In our example, the stakeholders were data collectors on a big
scale themselves.
The evaluation unit of DG INFSO employs a data collection
group, which provides
information about funded projects and organisations at a
detailed level. Furthermore,
the DG is used to provide data to the study teams of the projects
they contract for
their evaluation projects. Consequently we benefitted from
having a large and clean
database concerning all issues the study team was interested in.
However, it was still
an issue to confirm the existence, quality and availability of the
data and check for
formats and database requirements. Even if the data is there in
principal, enough
time should be reserved for data management issues. The
quality of the simulation

in the eyes of the user will very much depend on the quality of
the informing data
and the quality of the model calibration.
What would have been the more common process if the study
team had not struck
lucky as in our example? In other SKIN applications, the
following mechanism was
used by the study team and proved to be valuable (the ones with
asterisks apply to
our INFSO-SKIN example as well):
• Identify the rough type of data required for the study from the
project specifications
• Estimate financial resources for data access in the proposal of
project to
stakeholders (this can sometimes happen in interaction with the
funding body);
• After the second meeting with stakeholders (see Sect. 2.3.1),
identify relevant
data concerning variables to answer study questions and
address/test hypotheses
of Sect. 2.3.1*;
[email protected]
Policy Modelling 51
• Communicate exact data requirements to those stakeholders
who are experts on
their own empirical data environment*;

• Review existing data bases including the ones stakeholders
might hold or can get
access to*;
• Meet stakeholders to discuss data issues; help them understand
and agree on the
scope and limitations of data access*;
• If needed and required by stakeholders, collect data;
• Meet stakeholders to discuss the final database;
• Evaluate the meeting and develop data-to-model procedures*.
3.2.4.3 Interacting with Users to Check the Validity of
Simulation Results
The stakeholders put heavy demands on the study team
concerning understanding
and trusting the simulation findings. The first and most
important is that the clients
want to understand the model. To trust results means to trust the
process that produced
them. Here, the advantage of the adapted SKIN model is that it
relies on a narrative
that tells the story of the users’ every-day world of decision-
making (see Sect. 2.1.1).
In the SKIN model, a good example for “reality” requirements
is the necessity to
model the knowledge and behaviour of agents. Blackboxing
knowledge of agents
or creating merely reactive simple agents would not have been
an option, because
stakeholders do not think the world works that way.
The SKIN model is based on empirical quantitative and
qualitative research in

innovation economics, sociology, science and technology
studies, and business stud-
ies. Agents and behaviours are informed by what we know about
them; the model
is calibrated by data from this research. We found that there is a
big advantage in
having a model where stakeholders can recognise the relevant
features they see at
work in their social contexts. In setting up and adapting the
model to study needs,
stakeholders can actively intervene and ask for additional agent
characteristics or
behavioural rules; they can refine the model and inform
blackbox areas where they
have information on the underlying processes.
However, here again, we encountered the diversity of
stakeholder preferences.
Different members of the DG INFSO Steering Committee opted
for different changes
and modifications of the model. Some were manageable with
given time constraints
and financial resources; some would have outlived the duration
of the project if
realised. The final course of action for adapting the model to
study needs was the
result of discussions between stakeholders about model
credibility and increasing
complexity and of discussions between stakeholders and the
study team concerning
feasibility and reducing complexity.
Once the stakeholders were familiar with the features of the
model and had con-
tributed to its adaptation to study requirements, there was an
initial willingness to

trust model findings. This was strengthened by letting the model
reproduce FP7 data
as the baseline scenario that all policy experiments would be
benchmarked against.
If the networks created by real life and those created by the
agent-based model cor-
respond closely, the simulation experiments can be
characterized as history-friendly
[email protected]
experiments, which reproduce the empirical data and cover the
decisive mechanisms
and resulting dynamics of the real networks (see standard view).
In presenting the results of the INFSO-SKIN study, however, it
became clear
that there were, again, certain caveats coming from the user
community. The policy
analysts did not want to look at a multitude of tables and scan
through endless
numbers of simulation results for interesting parameters; nor
did they expect to
watch the running model producing its results, because a typical
run lasted 48 hours.
Presenting results in an appealing and convincing way required
visualisations and
interactive methods where users could intuitively understand
what they see, had
access to more detailed information if wanted, e.g. in a
hyperlink structure, and
could decide themselves in which format, in which order and in

which detail they
want to go through findings. This part of the process still needs
further work: new
visualisation and interactive technologies can help to make
simulation results more
accessible to stakeholders.
This leads to the last issue to be discussed in this section. What
happens after
the credibility of simulation results is established? In the
INFSO-SKIN study, the
objective was policy advice for Horizon 2020. The stakeholders
wanted the study
team to communicate the results as “recommendations” rather
than as “findings”.
They required a so-called “utility summary” that included
statements about what
they should do in their policy domain justified according to the
results of the study.
Here the study team proved to be hesitant—not due to a lack of
confidence in their
model, but due to the recognition of its predictive limitations
and a reluctance to
formulate normative statements, which were seen as a matter of
political opinion
and not a responsibility of a scientific advisor. The negotiation
of the wording in the
Utility Summary was another instance of an intense dialogue
between stakeholders
and study team. Nevertheless, the extent to which the results
influenced or were
somehow useful in the actual political process of finalising
Horizon 2020 policies
was not part of the stakeholder feedback after the study ended
and is still not known
to us. The feedback consisted merely of a formal approval that

we had fulfilled the
project contract.
3.3 Conclusions
To trust the quality of a simulation means to trust the process
that produced its results.
This process is not only the one incorporated in the simulation
model itself. It is the
whole interaction between stakeholders, study team, model, and
findings.
The first section of this contribution pointed out the problems
of the Standard
View and the constructivist view in evaluating social
simulations. We argued that
a simulation is good when we get from it what we originally
would have liked to
get from the target; in this, the evaluation of the simulation
would be guided by the
expectations, anticipations, and experience of the community
that uses it. This makes
the user community view the most promising mechanism to
assess the quality of a
policy-modelling exercise.
[email protected]
Policy Modelling 53
The second section looked at a concrete policy-modelling
example to test this
idea. It showed that the very first negotiation and discussion

with the user commu-
nity to identify their questions were highly user-driven,
interactive, and iterative. It
required communicative skills, patience, willingness to
compromise on both sides,
and motivation to link the formal world of modellers and the
narrative world of policy
making in practice.
Often, the user community is involved in providing data for
calibrating the model.
It is not an easy issue to confirm the existence, quality, and
availability of the data and
check for formats and database requirements. Because the
quality of the simulation
in the eyes of the user will depend on the quality of the
informing data and the quality
of the model calibration, much time and effort need to be spent
in coordinating this
issue with the user community.
Last but not least, the user community has to check the validity
of simulation
results and has to believe in their quality. Users have to be
helped to understand the
model, to agree with its processes and ways to produce results,
to judge similarity
between empirical and simulated data, etc.
The standard view is epistemologically questionable due to the
two problems
of under-determination of theory and of theory-ladenness of
observations; the con-
structivist view is difficult due to its inherent relativism, which
annihilates its own
validity claims. The user community view relies on social model

building and model
assessment practices and, in a way, bridges the two other views,
because it rests
on the realism of these practices. This is why we advocate its
quality assessment
mechanisms.
Summarising, in our eyes, the user community view might be
the most promis-
ing, but is definitely the most work-intensive mechanism to
assess the quality of a
simulation. It all depends on who the user community is and
whom it consists of: if
there is more than one member, the user community will never
be homogenous. It is
difficult to refer to a “community”, if people have radically
different opinions.
Furthermore, there are all sorts of practical contingencies to
deal with. People
might not be interested, or they might not be willing or able to
dedicate as much of
their time and attention to the study as needed. There is also the
time dimension: the
users at the end of a simulation project might not be the same as
those who initiated it,
as a result of job changes, resignations, promotions, and
organisational restructuring.
Moreover, the user community and the simulation modellers
may affect each other,
with the modellers helping in some ways to construct a user
community in order to
solve the practical contingencies that get in the way of
assessing the quality of the
simulation, while the user community may in turn have an effect
on the modellers

(not least in terms of influencing the financial and recognition
rewards the modellers
receive).
If trusting the quality of a simulation indeed means trusting the
process that pro-
duced its results, then we need to address the entire interaction
process between user
community, researchers, data, model, and findings as the
relevant assessment mech-
anism. Researchers have to be aware that they are codesigners
of the mechanisms
they need to participate in with the user community for
assessing the quality of a
social simulation.
[email protected]
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[email protected]
Chapter 4
Policy Making and Modelling in a Complex
World
Wander Jager and Bruce Edmonds
Abstract In this chapter, we discuss the consequences of
complexity in the real world
together with some meaningful ways of understanding and
managing such situations.
The implications of such complexity are that many social
systems are unpredictable
by nature, especially when in the presence of structural change
(transitions). We
shortly discuss the problems arising from a too-narrow focus on
quantification in
managing complex systems. We criticise some of the approaches
that ignore these
difficulties and pretend to predict using simplistic models.
However, lack of pre-

dictability does not automatically imply a lack of managerial
possibilities. We will
discuss how some insights and tools from “complexity science”
can help with such
management. Managing a complex system requires a good
understanding of the
dynamics of the system in question—to know, before they
occur, some of the real
possibilities that might occur and be ready so they can be
reacted to as responsively
as possible. Agent-based simulation will be discussed as a tool
that is suitable for
this task, and its particular strengths and weaknesses for this are
discussed.
4.1 Introduction
Some time ago, one of us (WJ) attended a meeting of specialists
in the energy sector.
A former minister was talking about the energy transition,
advocating for directing
this transition; I sighed, because I realized that the energy
transition, involving a
multitude of interdependent actors and many unforeseen
developments, would make
a planned direction of such a process a fundamental
impossibility. Yet I decided not to
interfere, since my comment would have required a mini lecture
on the management
of complex systems, and in the setting of this meeting this
would have required too
much time. So the speaker went on, and one of the listeners
stood up and asked, “But
W. Jager (�)
Groningen Center of Social Complexity Studies, University

Groningen, Groningen,
The Netherlands
B. Edmonds
Manchester Metropolitan University, Manchester, UK
10.1007/978-3-319-12784-2_4
[email protected]
58 W. Jager and B. Edmonds
Fig. 4.1 Double pendulum.
(Source: Wikipedia)
sir, what if the storage capacity of batteries will drastically
improve?” The speakers
answered, “this is an uncertainty we cannot include in our
models, so in our transition
scenarios we don’t include such events”. This remark made
clear that, in many cases,
policymakers are not aware of the complexities in the systems
they operate in, and are
not prepared to deal with surprises in systems. Because the
transitional idea is being
used very frequently to explain wide-ranging changes related to
the transformation
of our energy system, and the change towards a sustainable
society, it seems relevant
to address the issue of complexity in this chapter, and discuss
the implications for

policy making in complex behaving system. After explaining
what complexity is,
we will discuss the common mistakes being made in managing
complex systems.
Following that, we will discuss the use of models in policy
making, specifically
addressing agent-based models because of their capacity to
model social complex
systems that are often being addressed by policy.
4.2 What is Complexity?
The word “complexity” can be used to indicate a variety of
kinds of difficulties.
However, the kind of complexity we are specifically dealing
with in this chapter
is where a system is composed of multiple interacting elements
whose possible
behavioural states can combine in ways that are hard to predict
or characterise. One
of the simplest examples is that of a double pendulum (Fig 4.1).
[email protected]
4 Policy Making and Modelling in a Complex World 59
Although only consisting of a few parts connected by joints, it
has complex and un-
predictable behaviour when set swinging under gravity. If this
pendulum is released,
it will move chaotically due to the interactions between the
upper (θ1) and lower (θ2)
joint. Whereas it is possible to formally represent this simple
system in detail, e.g.

including aspects such as air pressure, friction in the hinge, the
exact behaviour of
the double pendulum is unpredictable.1 This is due to the
fundamental uncertainty
of the precise position of its parts2and the unsolvability of the
three-body problem as
proven by Bruns and Poincaré in 1887. Just after release, its
motion is predictable to
a considerable degree of accuracy, but then starts to deviate
from any prediction until
it is moving in a different manner. Whereas the precise motion
at these stages is not
predictable, we know that after a while, the swinging motion
will become less erratic,
and ultimately it will hang still (due to friction). This
demonstrates that even in very
simple physical systems, interactions may give rise to complex
behaviour, expressed
in different types of behaviour, ranging from very stable to
chaotic. Obviously, many
physical systems are much more complicated, such as our
atmospheric system. As
can be expected, biological or social systems also display
complex behaviour be-
cause they are composed of large numbers of interacting agents.
Also, when such
systems are described by a simple set of equations, complex
behaviour may arise.
This is nicely illustrated by the “logistic equation”, which was
originally introduced
as a simple model of biological populations in a situation of
limited resources (May
1976). Here the population, x, in the next year (expressed as a
proportion of its max-
imum possible) is determined based on the corresponding value
in the last year as

rx(1-x), where r is a parameter (the rate of unrestrained
population increase). Again,
this apparently simple model leads to some complex behaviour.
Figure 4.2 shows
the possible long-term values of x for different values of r,
showing that increasing
r creates more possible long-term states for x. Where on the left
hand side (r< 3.0)
the state of x is fixed, at higher levels the number of possible
states increases with
the number of states increasing rapidly until, for levels of r
above 3.6, almost any
state can occur, indicating a chaotic situation. In this case,
although the system may
be predictable under some circumstances (low r), in others it
will not be (higher r).
What is remarkable is that, despite the inherent unpredictability
of their environ-
ment, organisms have survived and developed intricate webs of
interdependence in
terms of their ecologies. This is due to the adaptive capacity of
organisms, allowing
them to self-organise. It is exactly this capacity of organisms to
adapt to changing cir-
cumstances (learning) that differentiates ‘regular’ complex
systems from ‘complex
adaptive systems’ (CAS). Hence complex adaptive systems have
a strong capacity
to self-organise, which can be seen in, i.e. plant growth, the
structure of ant nests
and the organisation of human society. Yet these very systems
have been observed
to exist in both stable and unstable stages, with notable
transitions between these

1 Obviously predictions can always be made, but it has been
proved analytically that the predictive
value of models is zero in these cases.
2 Even if one could measure them with extreme accuracy, there
would never be complete accuracy
due to the uncertainty theorem of Heisenberg (1927).
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Fig. 4.2 Bifurcation diagram. (Source: Wikipedia)
stages. Ecological science has observed that major transitions in
ecological systems
towards a different regime (transition) are often preceded by
increased variances,
slower recovery from small perturbations (critical slowing
down) and increased re-
turn times (Boettiger and Hastings 2012; Dai and Vorselen et al.
2012; Dakos and
Carpenter et al. 2012). A classic example here is that of the
transition from a clear
lake to a turbid state due to eutrophication. Here an increase in
mineral and organic
nutrients in the water gives rise to the growth of plants, in
particular algae. In the
stage preceding to a transition, short periods of increased algal
blooms may occur,
decreasing visibility and oxygen levels, causing the population
of top predating fish
hunting on eyesight to decrease, causing a growth in
populations of other species,
etc. The increased variance (e.g. in population levels of

different species in the lake)
indicates that a regime shift is near, and that the lake may
radically shift from a clear
state to a turbid state with a complete different ecosystem, with
an attendant loss of
local species.
The hope is that for other complex systems, such indicators may
also identify the
approach of a tipping point and a regime shift or transition
(Scheffer et al. 2009).
For policy making, this is a relevant perspective, as it helps in
understanding what a
transition or regime shift is, and has implications for policy
development. A transition
implies a large-scale restructuring of a system that is composed
of many interacting
parts. As such, the energy system and our economy at large are
examples of complex
systems where billions of actors are involved, and a large
number of stakeholders such
as companies and countries are influencing each other. The
transformation from, for
example, a fossil fuel-based economy towards a sustainable
energy system requires
that many actors that depend on each other have to
simultaneously change their
behaviour. An analogy with the logistic process illustrated in
Fig. 4.2 can be made.
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Imagine a move from the lower stable situation x = 0.5 at r =
3.3 to the upper stable
situation x = 0.8. This could be achieved by increasing the value
of r, moving towards
the more turbulent regime of the system and then reducing r
again, allowing the new
state to be settled into. This implies that moving from one
stable regime towards
another stable regime may require a period of turbulence where
the transition can
happen. Something like a period of turbulence demarcating
regime shifts is what
seems to have occurred during many transitions in the history of
the world.
4.3 Two Common Mistakes in Managing Complex Systems
Turbulent stages in social systems are usually experienced as
gruesome by policy-
makers and managers. Most of them prefer to have grip on a
situation, and try to
develop and communicate a clear perspective on how their
actions will affect future
outcomes. Especially in communicating the rationale of their
decisions to the out-
side world, the complex nature of social systems is often lost. It
is neither possible
nor particularly useful to try and list all of the “mistakes” that
policymakers might
make in the face of complex systems, but two of the ways in
which systems are
oversimplified are quantification and compartmentalisation.
Quantification implies that policy is biased towards those
attributes of a system
that are easy to quantify. Hence, it comes as no surprise that

economic outcomes,
in terms of money, are often the dominating criteria in
evaluating policy. Often, this
results in choosing a solution that will result in the best
financial economic outcome.
Whereas non-quantifiable outcomes are often acknowledged,
usually the bottom
line is that “we obviously have to select the most economical
viable option” because
“money can be spent only once”. In such a case, many other
complex and qualitative
outcomes might be undervalued or even ignored since the
complex system has been
reduced to easily measurable quantities. In many situations, this
causes resistance to
policies, because the non-quantifiable outcomes often have an
important impact on
the quality of life of people. An example would be the recent
earthquakes in the north
of the Netherlands due to the extraction of natural gas, where
the policy perspective
was mainly focussing on compensating the costs of damage to
housing, whereas
the population experienced a loss of quality of life due to fear
and feelings of unfair
treatment by the government, qualities that are hard to quantify
and were undervalued
in the discussion. The more complex a system is, the more
appealing it seems to be
to get a grip on the decision context by quantifying the problem,
often in economical
terms. Hence, in many complex problems, e.g. related to
investments in sustainable
energy, the discussion revolves around returns on investment,
whereas other relevant
qualities, whereas being acknowledged, lose importance because

they cannot be
included in the complicated calculations. Further, the ability to
encapsulate and
manipulate number-based representations in mathematics may
give such exercises
an appearance of being scientific and hence reinforce the
impression that the situation
is under control. However, what has happened here is a
conflation of indicators with
the overall quality of the goals and outcomes themselves.
Indicators may well be
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useful to help judge goals and outcomes; but in complex
situations, it is rare that
such a judgement can be reduced to such simple dimensions.
Compartimentalization is a second response of many
policymakers in trying to
simplify complex social systems. This is a strategy whereby a
system or organi-
sation is split into different parts that act (to a large extent)
independently of each
other as separated entities, with their own goals and internal
structures. As a conse-
quence, the policy/management organization will follow the
structure of its division
into parts. Being responsible for one part of the system implies
that a bias emerges
towards optimizing the performance of the own part. This is
further stimulated by

rewarding managers for the performance of the subsystem they
are responsible for,
independently of the others. However, this approach makes it
difficult to account for
spillover effects towards other parts of the system, particularly
when the outcomes
in related parts of the system are more difficult to quantify. An
example would be the
savings on health care concerning psychiatric care. Reducing
the number of maxi-
mum number of consults being covered by health insurance
resulted in a significant
financial savings in health care nationally. However, as a result,
more people in
need of psychiatric help could not afford this help, and, as a
consequence, may have
contributed to an increase in problems such as street crime,
annoyance, and deviant
behaviour. Because these developments are often qualitative in
nature, hard num-
bers are not available, and hence these effects are more being
debated than actually
being included in policy development. Interestingly, due to this
compartmentalisa-
tion, the direct financial savings due to the reduction of the
insurance conditions
may be surpassed by the additional costs made in various other
parts as the system
such as policing, costs of crime, and increased need for crisis
intervention. Thus, the
problems of quantification and compartmentalisation can
exacerbate each other: A
quantitative approach may facilitate compartmentalisation since
it makes measure-
ment of each compartment easier and if one takes simple
indicates as one’s goals,

then it is tempting to reduce institutional structures to separate
compartments that
can concentrate on these narrow targets. We coin the term
“Excellification”—after
Microsoft Excel—to express the tendency to use quantitative
measurements and
compartmentalise systems in getting a grip on systems.
Whereas we are absolutely convinced of the value of using
measurements in
developing and evaluating policy/management, it is our stance
that policy making
in complex systems is requiring a deeper level of understanding
the processes that
guide the developments in the system at hand. When trying to
steer policy in the
face of a complex and dynamic situation, there are essentially
two kinds of strategies
being used in developing this understanding: instrumental and
representational. We
look at these next, before we discuss how agent-based
modelling may contribute to
understanding and policy making in complex systems.
4.4 Complexity and Policy Making
An instrumental approach is where one chooses between a set of
possible policies
and then evaluates them according to some assessment of their
past effectiveness.
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Fig. 4.3 An illustration of the
instrumental approach
Choose
one and
put it into
effect
(work out
what to do)
actionindicators
Strategy 1
Strategy 2
etc.
Strategy 3
Evaluate
how
successful
strategy
was
In future iterations, one then adapts and/or changes the chosen
policy in the light
of its track record. The idea is illustrated in Fig. 4.3. This can
be a highly adaptive
approach, reacting rapidly in the light of the current
effectiveness of different strate-
gies. No initial knowledge is needed for this approach, but
rather the better strategies
develop over time, given feedback from the environment.

Maybe, the purest form of
this is the “blind variation and selective retention” of Campbell
(1960), where new
variants of strategies are produced (essentially) at random, and
those that work badly
are eliminated, as in biological evolution. The instrumental
approach works better
when: there is a sufficient range of strategies to choose
between, there is an effective
assessment of their efficacy, and the iterative cycle of trial and
assessment is rapid
and repeated over a substantial period of time. The instrumental
approach is often
used by practitioners who might develop a sophisticated “menu”
of what strategies
seem to work under different sets of circumstances.
An example of this might be adjusting the level of some policy
instrument such
as the level of tolls that are designed to reduce congestion on
certain roads. If there
is still too much congestion, the toll might be raised; if there is
too little usage, the
toll might be progressively lowered.
The representational approach is a little more complicated. One
has a series
of “models” of the environment. The models are assessed by
their ability to pre-
dict/mirror observed aspects of the environment. The best model
is then used to
evaluate possible actions in terms of an evaluation of the
predicted outcomes from
those actions and the one with the best outcome chosen to enact.
Thus, there are two
“loops” involved: One in terms of working out predictions of

the models and seeing
which best predicts what is observed, and the second is a loop
of evaluating possible
actions using the best model to determine which action to
deploy. Figure 4.4 illus-
trates this approach. The task of developing, evaluating, and
changing the models
is an expensive one, so the predictive power of these models
needs to be weighed
against this cost. Also, the time taken to develop the models
means that this approach
is often slower to adapt to changes in the environment than a
corresponding instru-
mental approach. However, one significant advantage of this
approach is that, as a
result of the models, one might have a good idea of why certain
things were hap-
pening in the environment, and hence know which models might
be more helpful,
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Fig. 4.4 An illustration of the
representational approach
Choose one,
work out
predictions of
effects of
possible
actions

actionperception
Model 1
Model 2
etc.
Model 3
Evaluate
whether
predicitons
were
accurate
as well as allowing for the development of longer term
strategies addressing the root
causes of such change. The representational approach is the one
generally followed
by scientists because they are interested in understanding what
is happening.
An example of the representational approach might be the use of
epidemiolog-
ical models to predict the spread of an animal disease, given
different contain-
ment/mitigation strategies to deal with the crisis. The models
are used to predict
the outcomes of various strategies, which can inform the choice
of strategy. This
prediction can be useful even if the models are being improved,
at the same time,
due to the new data coming in because of the events.
Of course, these two approaches are frequently mixed. For

example, representa-
tional models might be used to constrain which strategies are
considered within an
otherwise instrumental approach (even if the representational
models themselves are
not very good at prediction). If a central bank is considering
what interest rate to set,
there is a certain amount of trial and error: thus, exactly how
low one has to drop the
interest rates to get an economy going might be impossible to
predict, and one just
has to progressively lower them until the desired effect
achieved. However, some
theory will also be useful: thus, one would know that dropping
interest rates would
not be the way to cool an over-heating economy. Thus, even
very rough models with
relatively poor predictive ability (such as “raising interest rates
tends to reduce the
volume of economic activity and lowering them increases it”)
can be useful.
Complexity theory is useful for the consideration of policy in
two different ways.
First, it can help provide representational models that might be
used to constrain
the range of strategies under consideration and, second, can
help inform second-
order considerations concerning the ways in which policy might
be developed and/or
adopted—the policy adaption process itself. In the following
section, we first look
at the nature and kinds of models so as to inform their best use
within the policy
modelling, and later look at how second-order considerations
may inform how we

might use such models.
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Fig. 4.5 An illustration in
some of the opposing
desiderata of models
simplicity
generality
validity
formality
4.4.1 Using Formal Models in Policy Making
The use of models in policy making starts with the question—
what the appropriate
policy models are? Many models are often available because (1)
improving mod-
els following the representational approach will yield series of
models that further
improve the representation of the process in terms of cause–
effect relations, and (2)
sometimes more extended models are required for explaining a
process, whereas
often simpler models are used to represent a particular
behaviour.
Realising that many models are often available, we still have to

keep in mind
that any model is an abstraction. A useful model is necessarily
simpler than what it
represents, so that much is left out—abstracted away. However,
the decision as to
what needs to be represented in a model and what can be safely
left out is a difficult
one. Some models will be useful in some circumstances and
useless in others. Also,
a model that is useful for one purpose may well be useless for
another. Many of the
problems associated with the use of models to aid the
formulation and steering of
policy derive from an assumption that a model will have value
per se, independent
of context and purpose.
One of the things that affect the uses to which models can be
put is the compromise
that went into the formulation of the models. Figure 4.5
illustrates some of these
tensions in a simple way.
These illustrated desiderata refer to a model that is being used.
Simplicity is
how simple the model is, the extent to which the model itself
can be completely
understood. Analytically solvable mathematical models, most
statistical models, and
abstract simulation models are at the relatively simple end of
the spectrum. Clearly, a
simple model has many advantages in terms of using the model,
checking it for bugs
and mistakes (Galán et al. 2009), and communicating it.
However, when modelling
complex systems, such as what policymakers face, such

simplicity may not be worth
it if gaining it means a loss of other desirable properties.
Generality is the extent of
the model scope: How many different kinds of situations could
the model be usefully
applied. Clearly, some level of generality is desirable;
otherwise one could only apply
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the model in a single situation. However, all policy models will
not be completely
general—there will always be assumptions used in their
construction, which limit
their generality. Authors are often rather lax about making the
scope of their models
clear—often implying a greater level of generality that can be
substantiated. Finally,
validity means the extent to which the model outcomes match
what is observed to
occur—it is what is established in the process of model
validation. This might be
as close a match as a point forecast, or as loose as projecting
qualitative aspects of
possible outcomes.
What policymakers want, above all, is validity, with generality
(so they do not
have to keep going back to the modellers) and simplicity (so
there is an accessible
narrative to build support for any associated policy) coming
after this. Simplicity and

generality are nice if you can get them, but one cannot assume
that these are achiev-
able (Edmonds 2013). Validity should be an overwhelming
priority for modellers;
otherwise, they are not doing any sort of empirical science.
However, they often put
this off into the future, preferring the attractions of the apparent
generality offered
by analogical models (Edmonds 2001, 2010).
Formality is the degree to which a model is built in a precise
language or system.
A system of equations or a computer simulation is formal,
vague, but intuitive ideas
expressed in natural language are informal. It must be
remembered that formality for
those in the policy world is not a virtue but more of a problem.
They may be convinced
it is necessary (to provide the backing of “science”), but it
means that the model is
inevitably somewhat opaque and not entirely under their
control. This is the nub of
the relationship between modellers and the policy world—if the
policy side did not
feel any need for the formality, then they would have no need of
modellers—they are
already skilled at making decisions using informal methods. For
the modellers, the
situation is reverse. Formality is at the root of modelling, so
that they can replicate
their results and so that the model can be unambiguously passed
to other researchers
for examination, critique, and further development (Edmonds
2000). For this reason,
we will discuss formality a bit and analyse its nature and
consequences.

Two dimensions of formality can be usefully distinguished here,
these are:
a. The extent to which the referents of the representation are
constrained (“specificity
of reference”).
b. The extent to which the ways in which instantiations of the
representation can be
manipulated are constrained (“specificity of manipulation”).
For example, an analogy expressed in natural language has a
low specificity of
reference since, what its parts refer to are reconstructed by each
hearer in each
situation. For example, the phrase “a tidal wave of crime”
implies that concerted and
highly coordinated action is needed in order to prevent people
being engulfed, but the
level of danger and what (if anything) is necessary to do must
be determined by each
listener. In contrast to this is a detailed description where what
it refers to is severely
limited by its content, e.g. “Recorded burglaries in London rose
by 15 % compared
to the previous year”. Data are characterised by a high
specificity of reference, since
what it refers to is very precise, but has a low specificity of
manipulation because
there are few constraints in what one can do with it.
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A system of mathematics or computer code has a high
specificity of manipulation
since the ways these can be manipulated are determined by
precise rules—what
one person infers from them can be exactly replicated by
another. Thus, all formal
models (the ones we are mostly concentrating on here) have a
high specificity of
manipulation, but not necessarily a high specificity of
representation. A piece of
natural language that can be used to draw inferences in many
different ways, only
limited by the manipulators’ imagination and linguistic ability,
has a low specificity
of manipulation. One might get the impression that any
“scientific” model expressed
in mathematics must be formal in both ways. However, just
because a representation
has high specificity of manipulation, it does not mean that the
meaning of its parts
in terms of what it represents is well determined.
Many simulations, for example, do not represent anything we
observe directly, but
are rather explorations of ideas. We, as intelligent interpreters,
may mentally fill in
what it might refer to in any particular context but these
“mappings” to reality are not
well defined. Such models are more in the nature of an analogy,
albeit one in formal
form—they are not testable in a scientific manner since it is not
clear as to precisely
what they represent. Whilst it may be obvious when a system of
mathematics is very

abstract and not directly connected with what is observed,
simulations (especially
agent-based simulations) can give a false impression of their
applicability because
they are readily interpretable (but informally). This does not
mean they are useless
for all purposes. For example, Schelling’s abstract simulation of
racial segregation
did not have any direct referents in terms of anything
measurable,3 but it was an
effective counterexample that can show that an assumption that
segregation must
be caused by strong racial prejudice was unsound. Thus, such
“analogical models”
(those with low specificity of reference) can give useful
insights—they can inform
thought, but cannot give reliable forecasts or explanations as to
what is observed.
In practice, a variety of models are used by modellers in the
consideration of
any issue, including: informal analogies or stories that
summarise understanding
and are used as a rough guide to formal manipulation, data
models that abstract
and represent the situation being modelled via observation and
measurement, the
simulation or mathematical model that is used to infer
something about outcomes
from initial situations, representations of the outcomes in terms
of summary measures
and graphs, and the interpretations of the results in terms of the
target situation. When
considering very complex situations, it is inevitable that more
models will become
involved, abstracting different aspects of the target situation in

different ways and
“staging” abstraction so that the meaning and reference can be
maintained. However,
good practice in terms of maintaining “clusters” of highly
related models has yet
to be established in the modelling community, so that a
policymaker might well
be bewildered by different models (using different assumptions)
giving apparently
conflicting results. However, the response to this should not be
to reject this variety,
and enforce comforting (but ultimately illusory) consistency of
outcomes, but accept
3 Subsequent elaborations of this model have tried to make the
relationship to what is observed
more direct, but the original model, however visually
suggestive, was not related to any data.
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that it is useful to have different viewpoints from models as
much as it is to have
different viewpoints from experts. It is the job of policymakers
to use their experience
and judgement in assessing and combining these views of
reality. Of course, equally it
is the job of the modellers to understand and explain why
models appear to contradict
each other and the significance of this as much as they can.
A model that looks scientific (e.g. is composed of equations,

hence quantified)
might well inspire more confidence than one that does not. In
fact, the formality
of models is very much a two-edged sword, giving advantages
and disadvantages
in ways that are not immediately obvious to a nonmodeller. We
will start with the
disadvantages and then consider the advantages.
Most formal models will be able to output series of numbers
composed of mea-
sures on the outcomes of the model. However, just because
numbers are by their
nature precise,4 does not mean that this precision is
representative of the certainty
to which these outcomes will map to observed outcomes. Thus,
numerical outcomes
can give a very false sense of security, and lead those involved
in policy to falsely
think that prediction of such values is possible. Although many
forecasters now will
add indications of uncertainty “around” forecasts, this can still
be deeply misleading
as it still implies that there is a central tendency about which
future outcomes will
gravitate.5
Many modellers are now reluctant to make such predictions
because they know
how misleading these can be. This is, understandably,
frustrating for those involved
in policy, whose response might be, “I know its complex, but
we do not have the
time/money to develop a more sophisticated model so just give
me your ‘best guess”’.
This attitude implies that some prediction is better than none,

and that the reliability
of a prediction is monotonic to the amount of effort one puts in.
It seems that many
imagine that the reliability of a prediction increases with effort,
albeit unevenly—so a
prediction with a small amount of effort will be better than none
at all. Unfortunately,
this is far from the case, and a prediction based on a “quick and
dirty” method may
be more misleading than helpful and merely give a false sense
of security.
One of the consequences of the complexity of social phenomena
is that the pre-
diction of policy matters is hard, rare, and only obtained as a
result of the most
specific and pragmatic kind of modelling developed over
relatively long periods of
time.6 It is more likely that a model is appropriate for
establishing and understanding
candidate explanations of what is happening, which will inform
policy making in a
less exact manner than prediction, being part of the mix of
factors that a policymaker
will take into account when deciding action. It is common for
policy people to want
a prediction of the impact of possible interventions “however
rough”, rather than
settle for some level of understanding of what is happening.
However, this can be
4 Even if, as in statistics, they are being precise about variation
and levels of uncertainty of other
numbers.
5 This apparent central tendency might be merely the result of
the way data are extracted from the

model and the assumptions built into the model rather than
anything that represents the fundamental
behaviour being modelled.
6 For an account of actual forecasting and its reality, see Silver
(2012).
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illusory—if one really wanted a prediction “however rough”,
one would settle for a
random prediction7 dressed up as a complicated “black box”
model. If we are wiser,
we should accept the complexity of what we are dealing and
reject models that give
us ill-founded predictions.
Maybe a better approach is to use the modelling to inform the
researchers about
the kinds of process that might emerge from a situation—
showing them possible
“trajectories” that they would not otherwise have imagined.
Using visualisations of
these trajectories and the critical indicators clarifies the
complex decision context for
policymakers. In this way, the burden of uncertainty and
decision making remains
with the policymakers and not the researchers, but they will be
more intelligently
informed about the complexity of what is currently happening,
allowing them to
“drive” decision making better.

As we have discussed above, one feature of complex systems is
that they can
result in completely unexpected outcomes, where due to the
relevant interactions in
the system, a new kind of process has developed resulting in
qualitatively different
results. It is for this reason that complex models of these
systems do not give prob-
abilities (since these may be meaningless, or worse be
downright misleading) but
rather trace some (but not all) of the possible outcomes. This is
useful as one can
then be as prepared as possible for such outcomes, which
otherwise would not have
been thought of.
On the positive side, the use of formal modelling techniques can
be very helpful
for integrating different kinds of understanding and evidence
into a more “well-
rounded” assessment of options. The formality of the models
means that it can be
shared without ambiguity or misunderstanding between experts
in different domains.
This contrasts with communication using natural language
where, inevitably, people
have different assumptions, different meanings, and different
inferences for key
terms and systems. This ability to integrate different kinds of
expertise turns out to
be especially useful in the technique we will discuss next—
agent-based simulation.
4.4.2 The Use of Agent-Based Models to Aid Policy Formation
In recent years, agent-based simulation has gained momentum

as a tool allowing the
computer to simulate the interactions between a great number of
agents. An agent-
based simulation implies that individuals can be represented as
separate computer
models that capture their motives and behaviour. Letting these
so-called agents in-
teract though a network, and confront them with changing
circumstances, creates
an artificial environment where complex and highly dynamic
processes can be stud-
ied. Because agent-based models address the interactions
between many different
agents, they offer a very suitable tool to represent and recreate
the complexities in so-
cial systems. Hence, agent-based modeling has become an
influential methodology
7 Or other null model, such as “what happened last time” or “no
change”.
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to study a variety of social systems, ranging from ant colonies
to aspects of human
society. In the context of agent-based simulation of human
behaviour, one of the
challenges is connecting the knowledge from behavioural
sciences in agent-based
models that can be used to model behaviour in some kind of
environment. These mod-
elled environments may differ largely, and may reflect different

(inter)disciplinary
fields. Examples of environments where agents can operate in
are, e.g. financial
markets, agricultural settings, the introduction of new
technologies in markets, and
transportation systems, just to name a few. A key advantage
here is that a model
creates a common formal language for different disciplines to
communicate. This
is important, as it allows for speaking the same language in
targeting issues that
are interdisciplinary by nature. Rather than taking information
from social scientists
as an interesting qualitative advice, it becomes possible to
actually simulate what
the behaviour dynamical effects of policies are. This is, in our
view, an important
step in addressing interdisciplinary policy issues in an effective
way. An additional
advantage of social simulation is that formalizing theory and
empirical data in mod-
els requires researchers to be exact in the assumptions, which,
in turn, may result in
specific research questions for field and/or lab experiments.
Hence, social simulation
is a tool that both stimulates the interaction between scientific
disciplines, and may
stimulate theory development/specification within the
behavioural sciences.
An increasing number of agent-based models is being used in a
policy context.
A recent inventory on the SIMSOC mailing list by Nigel
Gilbert8 resulted in a list
of modelling projects that in some way were related to actual
policy making. Topics

included energy systems, littering, water management, crowd
dynamics, financial
crisis, health management, deforestation, industrial clustering,
biogas use, military
interventions, diffusion of electric cars, organization of an
emergency centre, natural
park management, postal service organization, urban design,
introduction of renew-
able technology, and vaccination programmes. Whereas some
models were actually
being used by policymakers, in most instances, the models were
being used to in-
form policy makers about the complexities in the system they
were interacting with.
The basic idea is that a better understanding of the complex
dynamics of the system
contributes to understanding how to manage these systems, even
if they are unpre-
dictable by nature. Here, a comparison can be made with sailing
as a managerial
process.
Sailing can be seen as a managerial challenge in using different
forces that con-
stantly change and interact in order to move the ship to a certain
destination. In stable
and calm weather conditions, it is quite well possible to set the
sails in a certain posi-
tion and fix the rudder, and make an accurate prediction where
of the course the boat
will follow. The situation becomes different when you enter
more turbulent stages
in the system, and strong and variable winds, in combination
with bigger waves
and streams, requiring the sailor to be very adaptive to the
circumstances. A small

deviation from the course, due to a gush or a wave, may alter
the angle of the wind
8 See mailing list [email protected] Mail distributed by Nigel
Gilbert on December
14, 2013, subject: ABMs in action: second summary.
[email protected]
in the sail, which may give rise to further deviations of the
course. This is typically
a feedback process, and obviously an experienced sailor is well
aware of all these
dynamics, and, as a consequence, the sailor responds very
adaptive to these small
disturbances, yet keeps the long-term outcome—the destination
port—also in mind.
The social systems that we are dealing with, in transitions, are
way more com-
plex than the sailing example. Yet, the underlying rational is the
same: the better we
learn to understand the dynamics of change, the better we will
be capable of coping
with turbulences in the process, whilst keeping the long-term
goals in focus. Hence,
policy aims such that the transition towards a sustainable energy
future provides a
reasonably clear picture of the direction we are aiming for, but
the turbulences in the
process towards this future are not well known. Where the sailor
has a deep under-

standing of the dynamics that govern the behaviour of his boat,
for policymakers,
this understanding is often limited, as the opening example
demonstrated.
Using agent-based models for policy would contribute to a
better understand-
ing and management of social complex phenomena. First, agent-
based models will
be useful in identifying under what conditions a social system
will behave rela-
tively stable (predictable) versus turbulent (unpredictable). This
is critical for policy
making, because in relatively stable situations, predictions can
be made concern-
ing the effects of policy, whereas in turbulent regimes, a more
adaptive policy is
recommended. Adaptive policy implies that the turbulent
developments are being
followed closely, and that policymakers try to block
developments to grow in an
undesired direction, and benefit and support beneficial
developments. Second, if
simulated agents are more realistic in the sense that they are
equipped with differ-
ent utilities/needs/preferences, the simulations will not only
show what the possible
behavioural developments are but also reveal the impact on a
more psychologi-
cal quality-of-life level. Whereas currently many policy models
assess behavioural
change from a more financial/economical drivers, agent-based
models open a pos-
sibility to strengthen policy models by including additional
outcomes. Examples
would be outcomes relating to the stability and support in social

networks, and
general satisfaction levels.
Agent-based models, thus, can provide a richer and more
complex representation
of what may be happening within complex and highly dynamic
situations, allowing
for some of the real possibilities within the system to be
explored. This exploration of
possibilities can inform the risk analysis of policy, and help
ensure that policymakers
are ready for more of what the world may throw at them, for
example, by having put
in place custom-designed indicators that give them the soonest-
possible indication
that certain kinds of processes or structural changes are
underway.
4.5 Conclusions
The bad news for policymakers is that predictive models
perform worst exactly at
the moment policymakers need them most—during turbulent
stages. Yet, we observe
that many policymakers, not being aware of the complex nature
of the system they
[email protected]
are interfering with, still have a mechanistic worldview, and
base their decisions on
classical predictions. This may be one of the reasons for

scepticism by policymakers
of any modelling approaches (see e.g. Waldherr and Wijermans
2013). Even nowa-
days, when complexity has turned into a buzzword, many
policymakers still confuse
this concept with “complicatedness”, not embracing the essence
and meaning of
what complexity means for understanding social systems. As a
consequence, still
many policymakers are “Cartesian9” in their demand for better
predictive models.
On the other side, still many modellers working from a
mechanistic perspective (e.g.
linear and/or generic models), holding out the false hope of
“scientifically” predic-
tive models, look for more resources to incrementally improve
their models, e.g.
covering more variables. However, whereas it is sometimes
justified to argue for the
inclusion of more variables in a model, this will not contribute
to a better predictive
capacity of the model. As Scott Moss reports in his paper (Moss
2002), there are no
reported correct real-time forecasts of the volatile clusters or
the post-cluster levels
in financial market indices or macroeconomic trade cycles,
despite their incremental
“refinement” over many years. Characteristically, they predict
well in periods where
nothing much changes, but miss all the “turning points” where
structural change
occurs.
Even if policymakers have some understanding of the complex
nature of the
systems they are managing, they still often respond with “I

know it is complex, but
how else can I decide policy except by using the numbers I
have?”, indicating that the
numbers are often an important justification of decisions, even
if people are aware of
the uncertainties behind them. The example of the former
minister in the introduction
is a prototypical example of this decision making.
The challenge, hence, is not in trying to convince policymakers
of the value of
simulation models, but providing them with a deeper level
understanding of complex
systems. Here, simulation models can provide an important role
by creating learning
experiences. But before going to simulation models, it might be
important to use a
strong metaphor in anchoring the core idea of managing
complex systems. Sailing
offers an excellent metaphor here, because many people know
the basics of sailing,
and understand that it deals with the management of a ship in
sometimes turbulent
circumstances. What is critical in this metaphor is that in more
turbulent conditions,
the crew should become more adaptive to the developments in
the system.
Agent-based simulation is increasingly being used as a
modelling tool to explore
the possibilities and potential impacts of policy making in
complex systems. They
are inherently possibilistic rather than probabilistic. However,
the models being used
are usually not very accessible for policymakers. Also, in the
context of education,

not many models are available that allow for an easy access to
experiencing policy
making in complex systems. In Chap. 13 of this book, Jager and
Van der Vegt
suggest using based gaming as a promising venue to make
agent-based models more
9 Descartes’ mechanistic worldview implies that the universe
works like a clockwork, and prediction
is possible when one has knowledge of all the wheels, gears,
and levers of the clockwork. In policy
this translates as the viable society.
[email protected]
accessible in education and practical policy settings. A setting
where valid games are
being used to increase our understanding of the processes in
complex management
issues is expected to contribute to an improvement of the
policy-making process in
complex systems.
Acknowledgments This chapter has been written in the context
of the eGovPoliNet project. More
information can be found on http://www.policy-community.eu/.
References
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to population collapse. Science 336(6085):1175–1177
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the development of science. CPM
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Edmonds B (2001) The use of models—making MABS actually
work. In: Moss S, Davidsson
P (eds) Multi agent based simulation. Lecture Notes in
Artificial Intelligence 1979. Springer,
Berlin, pp 15–32
Edmonds B (2010) Bootstrapping knowledge about social
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(http://jasss.soc.surrey.ac.uk/13/1/8.html)
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Soc Soc Simul 12(1):1
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Mechanik Zeitschriftfür Physik 43:172–198. English translation
in (Wheeler and Zurek, 1983),
pp 62–84
May RM (1976) Simple mathematical models with very
complicated dynamics. Nature
261(5560):459–467
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Natl Acad Sci 99(Suppl 3):7267–7274
Scheffer et al (2009) Early warnings of critical transitions.
Nature 461:53–59
Silver N (2012) The signal and the noise: why so many
predictions fail-but some don’t. Penguin,
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simulation models to sceptical minds.
J Artif Soc Soc Simul 16(4):13
[email protected]
Chapter 5
From Building a Model to Adaptive Robust
Decision Making Using Systems Modeling
Erik Pruyt

Abstract Starting from the state-of-the-art and recent evolutions
in the field of
system dynamics modeling and simulation, this chapter sketches
a plausible near
term future of the broader field of systems modeling and
simulation. In the near
term future, different systems modeling schools are expected to
further integrate and
accelerate the adoption of methods and techniques from related
fields like policy
analysis, data science, machine learning, and computer science.
The resulting future
state of the art of the modeling field is illustrated by three
recent pilot projects. Each
of these projects required further integration of different
modeling and simulation
approaches and related disciplines as discussed in this chapter.
These examples also
illustrate which gaps need to be filled in order to meet the
expectations of real decision
makers facing complex uncertain issues.
5.1 Introduction
Many systems, issues, and grand challenges are characterized by
dynamic com-
plexity, i.e., intricate time evolutionary behavior, often on
multiple dimensions of
interest. Many dynamically complex systems and issues are
relatively well known,
but have persisted for a long time due to the fact that their
dynamic complexity makes
them hard to understand and properly manage or solve. Other
complex systems
and issues—especially rapidly changing systems and future

grand challenges—are
largely unknown and unpredictable. Most unaided human beings
are notoriously
bad at dealing with dynamically complex issues—whether the
issues dealt with are
persistent or unknown. That is, without the help of
computational approaches, most
human beings are unable to assess potential dynamics of
complex systems and issues,
and are unable to assess the appropriateness of policies to
manage or address them.
E. Pruyt (�)
University of Technology, Delft,
The Netherlands
Netherlands Institute for Advanced Study, Wassenaar, The
Netherlands
10.1007/978-3-319-12784-2_5
[email protected]
76 E. Pruyt
Modeling and simulation is a field that develops and applies
computational meth-
ods to study complex systems and solve problems related to
complex issues. Over
the past half century, multiple modeling methods for simulating

such issues and
for advising decision makers facing them have emerged or have
been further devel-
oped. Examples include system dynamics (SD) modeling,
discrete event simulation
(DES), multi-actor systems modeling (MAS), agent-based
modeling (ABM), and
complex adaptive systems modeling (CAS). All too often, these
developments have
taken place in distinct fields, such as the SD field or the ABM
field, developing into
separate “schools,” each ascribing dynamic complexity to the
complex underlying
mechanisms they focus on, such as feedback effects and
accumulation effects in SD or
heterogenous actor-specific (inter)actions in ABM. The isolated
development within
separate traditions has limited the potential to learn across
fields and advance faster
and more effectively towards the shared goal of understanding
complex systems and
supporting decision makers facing complex issues.
Recent evolutions in modeling and simulation together with the
recent explosive
growth in computational power, data, social media, and other
evolutions in computer
science have created new opportunities for model-based
analysis and decision mak-
ing. These internal and external evolutions are likely to break
through silos of old,
open up new opportunities for social simulation and model-
based decision making,
and stir up the broader field of systems modeling and
simulation. Today, different
modeling approaches are already used in parallel, in series, and

in mixed form, and
several hybrid approaches are emerging. But not only are
different modeling tradi-
tions being mixed and matched in multiple ways, modeling and
simulation fields
have also started to adopt—or have accelerated their adoption
of—useful methods
and techniques from other disciplines including operations
research, policy analysis,
data analytics, machine learning, and computer science. The
field of modeling and
simulation is consequently turning into an interdisciplinary
field in which various
modeling schools and related disciplines are gradually being
integrated. In prac-
tice, the blending process and the adoption of methodological
innovations have just
started. Although some ways to integrate systems modeling
methods and many in-
novations have been demonstrated, further integration and
massive adoption are still
awaited. Moreover, other multi-methods and potential
innovations are still in an
experimental phase or are yet to be demonstrated and adopted.
In this chapter, some of these developments will be discussed, a
picture of the near
future state of the art of modeling and simulation is drawn, and
a few examples of
integrated systems modeling are briefly discussed. The SD
method is used to illustrate
these developments. Starting with a short introduction to the
traditional SD method
in Sect. 5.2, some recent and current innovations in SD are
discussed in Sect. 5.3,
resulting in a picture of the state of modeling and simulation in

Sect. 5.4. A few
examples are then briefly discussed in Sect. 5.5 to illustrate
what these developments
could result in and what the future state-of-the-art of systems
could look like. Finally, conclusions are drawn in Sect. 5.6.
[email protected]
Using Systems Modeling 77
5.2 System Dynamics Modeling and Simulation of Old
System dynamics was first developed in the second half of the
1950s by Jay W.
Forrester and was further developed into a consistent method
built on specific method-
ological choices1. It is a method for modeling and simulating
dynamically complex
systems or issues characterized by feedback effects and
accumulation effects. Feed-
back means that the present and future of issues or systems,
depend—through a
chain of causal relations—on their own past. In SD models,
system boundaries are
set broadly enough to include all important feedback effects and
generative mecha-
nisms. Accumulation relates not only to building up real
stocks—of people, items,
(infra)structures, etc.,—but also to building up mental or other
states. In SD mod-
els, stock variables and the underlying integral equations are
used to group largely

homogenous persons/items/. . . and keep track of their
aggregated dynamics over
time. Together, feedback and accumulation effects generate
dynamically complex
behavior both inside SD models and—so it is assumed in SD—
in real systems.
Other important characteristic of SD are (i) the reliance on
relatively enduring
conceptual systems representations in people’s minds, aka
mental models (Doyle and
Ford 1999, p. 414), as prime source of “rich” information
(Forrester 1961; Doyle
and Ford 1998); (ii) the use of causal loop diagrams and stock-
flow diagrams to
represent feedback and accumulation effects (Lane 2000); (iii)
the use of credibility
and fitness for purpose as main criteria for model validation
(Barlas 1996); and (iv)
the interpretation of simulation runs in terms of general
behavior patterns, aka modes
of behavior (Meadows and Robinson 1985).
In SD, the behavior of a system is to be explained by a dynamic
hypothesis, i.e.,
a causal theory for the behavior (Lane 2000; Sterman 2000).
This causal theory is
formalized as a model that can be simulated to generate
dynamic behavior. Simulating
the model thus allows one to explore the link between the
hypothesized system
structure and the time evolutionary behavior arising out of it
(Lane 2000).
Not surprisingly, these characteristics make SD particularly
useful for dealing

with complex systems or issues that are characterized by
important system feedback
effects and accumulation effects. SD modeling is mostly used to
model core system
structures or core structures underlying issues, to simulate their
resulting behavior,
and to study the link between the underlying causal structure of
issues and models and
the resulting behavior. SD models, which are mostly relatively
small and manageable,
thus allow for experimentation in a virtual laboratory. As a
consequence, SD models
are also extremely useful for model-based policy analysis, for
designing adaptive
policies (i.e., policies that automatically adapt to the
circumstances), and for testing
their policy robustness (i.e., whether they perform well enough
across a large variety
of circumstances).
1 See Forrester (1991, 2007), Sterman (2007) for accounts of
the inception of the SD field. See
Sterman (2000), Pruyt (2013) for introductions to SD. And see
Forrester (1961, 1969), Homer
(2012) for well-known examples of traditional SD.
[email protected]
78 E. Pruyt
In terms of application domains, SD is used for studying many
complex social–
technical systems and solving policy problems in many
application domains, for

example, in health policy, resource policy, energy policy,
environmental policy,
housing policy, education policy, innovation policy, social–
economic policy, and
other public policy domains. But it is also used for studying all
sorts of business
dynamics problems, for strategic planning, for solving supply
chain problems, etc.
At the inception of the SD method, SD models were almost
entirely continuous,
i.e., systems of differential equations, but over time more and
more discrete and other
noncontinuous elements crept in. Other evolutionary adaptations
in line with ideas
from the earliest days of the field, like the use of Group Model
Building to elicit
mental models of groups of stakeholders (Vennix 1996) or the
use of SD models as
engines for serious games, were also readily adopted by almost
the entire field. But
slightly more revolutionary innovations were not as easily and
massively adopted.
In other words, the identity and appearance of traditional SD
was well established
by the mid-1980s and does—at first sight—not seem to have
changed fundamentally
since then.
5.3 Recent Innovations and Expected Evolutions
5.3.1 Recent and Current Innovations
Looking in somewhat more detail at innovations within the SD
field and its adop-
tion of innovations from other fields shows that many—often

seemingly more
revolutionary—innovations have been introduced and
demonstrated, but that they
have not been massively adopted yet.
For instance, in terms of quantitative modeling, system
dynamicists have invested
in spatially specific SD modeling (Ruth and Pieper 1994;
Struben 2005; BenDor and
Kaza 2012), individual agent-based SD modeling as well as
mixed and hybrid ABM-
SD modeling (Castillo and Saysal 2005; Osgood 2009; Feola et
al. 2012; Rahmandad
and Sterman 2008), and micro–macro modeling (Fallah-Fini et
al. 2014). Examples
of recent developments in simulation setup and execution
include model calibration
and bootstrapping (Oliva 2003; Dogan 2007), different types of
sampling (Fiddaman
2002; Ford 1990; Clemson et al. 1995; Islam and Pruyt 2014),
multi-model and multi-
method simulation (Pruyt and Kwakkel 2014; Moorlag 2014),
and different types of
optimization approaches used for a variety of purposes (Coyle
1985; Miller 1998;
Coyle 1999; Graham and Ariza 1998; Hamarat et al. 2013,
2014). Recent innovations
in model testing, analysis, and visualization of model outputs in
SD include the
development and application of new methods for sensitivity and
uncertainty analysis
(Hearne 2010; Eker et al. 2014), formal model analysis methods
to study the link
between structure and behavior (Kampmann and Oliva 2008,
2009; Saleh et al.
2010), methods for testing policy robustness across wide ranges

of uncertainties
(Lempert et al. 2003), statistical packages and screening
techniques (Ford and Flynn
2005; Taylor et al. 2010), pattern testing and time series
classification techniques
[email protected]
(Yücel and Barlas 2011; Yücel 2012; Sucullu and Yücel 2014;
Islam and Pruyt 2014),
and machine learning techniques (Pruyt et al. 2013; Kwakkel et
al. 2014; Pruyt et
al. 2014c). These methods and techniques can be used together
with SD models to
identify root causes of problems, to identify adaptive policies
that properly address
these root causes, to test and optimize the effectiveness of
policies across wide ranges
of assumptions (i.e., policy robustness), etc. From this
perspective, these methods
and techniques are actually just evolutionary innovations in line
with early SD ideas.
And large-scale adoption of the aforementioned innovations
would allow the SD
field, and by extension the larger systems modeling field, to
move from “experiential
art” to “computational science.”
Most of the aforementioned innovations are actually integrated
in particular SD
approaches like in exploratory system dynamics modelling and

analysis (ESDMA),
which is an SD approach for studying dynamic complexity
under deep uncertainty.
Deep uncertainty could be defined as a situation in which
analysts do not know or
cannot agree on (i) an underlying model, (ii) probability
distributions of key variables
and parameters, and/or (iii) the value of alternative outcomes
(Lempert et al. 2003). It
is often encountered in situations characterized by either too
little information or too
much information (e.g., conflicting information or different
worldviews). ESDMA
is the combination of exploratory modeling and analysis (EMA),
aka robust decision
making, developed during the past two decades (Bankes 1993;
Lempert et al. 2000;
Bankes 2002; Lempert et al. 2006) and SD modeling. EMA is a
research methodology
for developing and using models to support decision making
under deep uncertainty.
It is not a modeling method, in spite of the fact that it requires
computational models.
EMA can be useful when relevant information that can be
exploited by building
computational models exists, but this information is insufficient
to specify a single
model that accurately describes system behavior (Kwakkel and
Pruyt 2013a). In
such situations, it is better to construct and use ensembles of
plausible models since
ensembles of models can capture more of the un/available
information than any
individual model (Bankes 2002). Ensembles of models can then
be used to deal with
model uncertainty, different perspectives, value diversity,

inconsistent information,
etc.—in short, with deep uncertainty.2
In EMA (and thus in ESDMA), the influence of a plethora of
uncertainties, includ-
ing method and model uncertainty, are systematically assessed
and used to design
policies: sampling and multi-model/multi-method simulation are
used to generate
ensembles of simulation runs to which time series classification
and machine learning
techniques are applied for generating insights. Multi-objective
robust optimization
(Hamarat et al. 2013, 2014) is used to identify policy levers and
define policy triggers,
and by doing so, support the design of adaptive robust policies.
And regret-based
approaches are used to test policy robustness across large
ensembles of plausible
runs (Lempert et al. 2003). EMA and ESDMA can be performed
with TU Delft’s
2 For ESDMA, see among else Pruyt and Hamarat (2010),
Logtens et al. (2012), Pruyt et al. (2013),
Kwakkel and Pruyt (2013a, b), Kwakkel et al. (2013), Pruyt and
Kwakkel (2014).
[email protected]
80 E. Pruyt
EMA workbench software, which is an open source tool3 that
integrates multi-
method, multi-model, multi-policy simulation with data

management, visualization,
and analysis.
The latter is just one of the recent innovations in modeling and
simulation software
and platforms: online modeling and simulation platforms, online
flight simulator
and gaming platforms, and packages for making hybrid models
have been developed
too. And modeling and simulation across platforms will also
become reality soon:
the eXtensible Model Interchange LanguagE (XMILE) project
(Diker and Allen
2005; Eberlein and Chichakly 2013) aims at facilitating the
storage, sharing, and
combination of simulation models and parts thereof across
software packages and
across modeling schools and may ease the interconnection with
(real-time) databases,
statistical and analytical software packages, and organizational
information and com-
munication technology (ICT) infrastructures. Note that this is
already possible today
with scripting languages and software packages with scripting
capabilities like the
aforementioned EMA workbench.
5.3.2 Current and Expected Evolutions
Three current evolutions are expected to further reinforce this
shift from “experiential
art” to “computational science.”
The first evolution relates to the development of “smarter”
methods, techniques,
and tools (i.e., methods, techniques, and tools that provide more

insights and deeper
understanding at reduced computational cost). Similar to the
development of formal
model analysis techniques that smartened the traditional SD
approach, new meth-
ods, techniques, and tools are currently being developed to
smarten modeling and
simulation approaches that rely on “brute force” sampling, for
example, adaptive
output-oriented sampling to span the space of possible dynamics
(Islam and Pruyt
2014) or smarter machine learning techniques (Pruyt et al.
2013; Kwakkel et al. 2014;
Pruyt et al. 2014c) and time series classification techniques
(Yücel and Barlas 2011;
Yücel 2012; Sucullu and Yücel 2014; Islam and Pruyt 2014),
and (multi-objective)
robust optimization techniques (Hamarat et al. 2013, 2014).
Partly related to the previous evolution are developments relates
to “big data,”
data management, and data science. Although traditional SD
modeling is sometimes
called data-poor modeling, it does not mean it is, nor should be.
SD software packages
allow one to get data from, and write simulation runs to,
databases. Moreover,
data are also used in SD to calibrate parameters or bootstrap
parameter ranges. But
more could be done, especially in the era of “big data.” Big data
simply refers
here to much more data than was until recently manageable. Big
data requires data
science techniques to make it manageable and useful. Data
science may be used in

3 The EMA workbench can be downloaded for free from
http://simulation.tbm.tudelft.nl/
ema-workbench/contents.html
[email protected]
http://simulation.tbm.tudelft.nl/ema-workbench/contents.html
http://simulation.tbm.tudelft.nl/ema-workbench/contents.html
modeling and simulation (i) to obtain useful inputs from data
(e.g., from real-time big
data sources), (ii) to analyze and interpret model-generated data
(i.e., big artificial
data), (iii) to compare simulated and real dynamics (i.e., for
monitoring and control),
and (iv) to infer parts of models from data (Pruyt et al. 2014c).
Interestingly, data
science techniques that are useful for obtaining useful inputs
from data may also be
made useful for analyzing and interpreting model-generated
data, and vice versa.
Online social media are interesting sources of real-world big
data for modeling and
simulation, both as inputs to models, to compare simulated and
real dynamics, and to
inform model development or model selection. There are many
application domains
in which the combination of data science and modeling and
simulation would be
beneficial. Examples, some of which are elaborated below,
include policy making
with regard to crime fighting, infectious diseases, cybersecurity,

national safety and
security, financial stress testing, energy transitions, and
marketing.
Another urgently needed innovation relates to model-based
empowerment of de-
cision makers. Although existing flight simulator and gaming
platforms are useful for
developing and distributing educational flight simulators and
games, and interfaces
can be built in SD packages, using them to develop interfaces
for real-world real-time
decision making and integrating them into existing ICT systems
is difficult and time
consuming. In many cases, companies and organizations want
these capabilities in-
house, even in their boardroom, instead of being dependent on
analyses by external
or internal analysts. The latter requires user-friendly interfaces
on top of (sets of)
models possibly connected to real-time data sources. These
interfaces should allow
for experimentation, simulation, thoroughly analysis of
simulation results, adaptive
robust policy design, and policy robustness testing.
5.4 Future State of Practice of Systems Modeling and
Simulation
These recent evolutions in modeling and simulation together
with the recent explosive
growth in computational power, data, social media, and other
evolutions in computer
science may herald the beginning of a new wave of innovation
and adoption, moving
the modeling and simulation field from building a single model

to simultaneously
simulating multiple models and uncertainties; from single
method to multi-method
and hybrid modeling and simulation; from modeling and
simulation with sparse
data to modeling and simulation with (near real-time) big data;
from simulating and
analyzing a few simulation runs to simulating and
simultaneously analyzing well-
selected ensembles of runs; from using models for intuitive
policy testing to using
models as instruments for designing adaptive robust policies;
and from developing
educational flight simulators to fully integrated decision
support.
For each of the modeling schools, additional adaptations could
be foreseen too.
In case of SD, it may for example involve a shift from
developing purely endoge-
nous to largely endogenous models; from fully aggregated
models to sufficiently
spatially explicit and heterogenous models; from qualitative
participatory modeling
[email protected]
82 E. Pruyt
Fig. 5.1 Picture of the state of science/future state of the art of
to quantitative participatory simulation; and from using SD to
combining problem

structuring and policy analysis tools, modeling and simulation,
machine learning
techniques, and (multi-objective) robust optimization.
Adoption of these recent, current, and expected innovations
could result in the
future state of the art4 of systems modeling as displayed in Fig.
5.1. As indicated
by (I) in Fig. 5.1, it will be possible to simultaneously use
multiple hypotheses (i.e.,
simulation models from the same or different traditions or
hybrids), for different goals
including the search for deeper understanding and policy
insights, experimentation in
a virtual laboratory, future-oriented exploration, robust policy
design, and robustness
testing under deep uncertainty. Sets of simulation models may
be used to represent
different perspectives or plausible theories, to deal with
methodological uncertainty,
or to deal with a plethora of important characteristics (e.g.,
agent characteristics,
feedback and accumulation effects, spatial and network effects)
without necessarily
having to integrate them in a single simulation model. The main
advantages of using
multiple models for doing so are that each of the models in the
ensemble of models
remains manageable and that the ensemble of simulation runs
generated with the
4 Given the fact that it takes a while before innovations are
adopted by software developers and
practitioners, this picture of the current state of science is at the
same time a plausible picture of the
medium term future of the field of modeling and simulation.

[email protected]
ensemble of models is likely to be more diverse which allows
for testing policy
robustness across a wider range of plausible futures.
Some of these models may be connected to real-time or near
real-time data
streams, and some models may even be inferred in part with
smart data science
tools from data sources (see (II) in Fig. 5.1). Storing the outputs
of these simulation
models in databases and applying data science techniques may
enhance our under-
standing, may generate policy insights, and may allow for
testing policy robustness
across large multidimensional uncertainty spaces (see (III) in
Fig. 5.1). And user-
friendly interfaces on top of these interconnected models may
eventually empower
policy makers, enabling them to really do model-based policy
making.
Note, however, that the integrated systems modeling approach
sketched in Fig. 5.1
may only suit a limited set of goals, decision makers, and
issues. Single model
simulation properly serves many goals, decision makers, and
issues well enough for
multi-model/multi-method, data-rich, exploratory, policy-

oriented approaches not
to be required. However, there are most certainly goals,
decision makers, and issues
that do.
5.5 Examples
Although all of the above is possible today, it should be noted
that this is the current
state of science, not the state of common practice yet. Applying
all these methods
and techniques to real issues is still challenging, and shows
where innovations are
most needed. The following examples illustrate what is possible
today as well as
what the most important gaps are that remain to be filled.
The first example shows that relatively simple systems models
simulated under
deep uncertainty allow for generating useful ensembles of many
simulation runs.
Using methods and techniques from related disciplines to
analyze the resulting arti-
ficial data sets helps to generate important policy insights. And
simulation of policies
across the ensembles allows to test for policy robustness. This
first case nevertheless
shows that there are opportunities for multi-method and hybrid
approaches as well
as for connecting systems models to real-time data streams.
The second example extends the first example towards a
system-of-systems ap-
proach with many simulation models generating even larger
ensembles of simulation
runs. Smart sampling and scenario discovery techniques are

then required to reduce
the resulting data sets to manageable proportions.
The third example shows a recent attempt to develop a smart
model-based
decision-support system for dealing with another deeply
uncertain issue. This ex-
ample shows that it is almost possible to empower decision
makers. Interfaces with
advanced analytical capabilities as well as easier and better
integration with existing
ICT systems are required though. This example also illustrates
the need for more
advanced hybrid systems models as well as the need to connect
systems models to
real-time geo-spatial data.
[email protected]
84 E. Pruyt
5.5.1 Assessing the Risk, and Monitoring, of New Infectious
Diseases
The first case, which is described in more detail in (Pruyt and
Hamarat 2010; Pruyt et
al. 2013), relates to assessing outbreaks of new flu variants.
Outbreaks of new (vari-
ants of) infectious diseases are deeply uncertain. For example,
in the first months
after the first reports about the outbreak of a new flu variant in
Mexico and the USA,
much remained unknown about the possible dynamics and
consequences of this pos-

sible epidemic/pandemic of the new flu variant, referred to
today as new influenza
A(H1N1)v. Table 5.1 shows that more and better information
became available over
time, but also that many uncertainties remained. However, even
with these remaining
uncertainties, it is possible to model and simulate this flu
variant under deep uncer-
tainty, for example with the simplistic simulation model
displayed in Fig. 5.2, since
flu outbreaks can be modeled.
Simulating this model thousands of times over very wide
uncertainty ranges for
each of the uncertain variables generates the 3D cloud of
potential outbreaks dis-
played in Fig. 5.3a. In this figure, the worst flu peak (0–50
months) is displayed
on the X-axis, the infected fraction during the worst flu peak
(0–50 %) is displayed
on the Y -axis, and the cumulative number of fatal cases in the
Western world (0–
50.000.000) is displayed on the Z-axis. This 3D plot shows that
the most catastrophic
outbreaks are likely to happen within the first year or during the
first winter season
following the outbreak. Using machine learning algorithms to
explore this ensemble
of simulation runs helps to generate important policy insights
(e.g., which policy
levers to address). Testing different variants of the same policy
shows that adaptive
policies outperform their static counterparts (compare Fig. 5.3b
and c). Figure 5.3d
finally shows that adaptive policies can be further improved
using multi-objective

robust optimization.
However, taking deep uncertainty seriously into account would
require simulating
more than a single model from a single modeling method: it
would be better to
simultaneously simulate CAS, ABM, SD, and hybrid models
under deep uncertainty
and use the resulting ensemble of simulation runs. Moreover,
near real-time geo-
spatial data (from twitter, medical records, etc.) may also be
used in combination
with simulation models, for example, to gradually reduce the
ensemble of model-
generated data. Both suggested improvements would be possible
today.
5.5.2 Integrated Risk-Capability Analysis under Deep
Uncertainty
The second example relates to risk assessment and capability
planning for National
Safety and Security. Since 2001, many nations have invested in
the development
of all-hazard integrated risk-capability assessment (IRCA)
approaches. All-hazard
IRCAs integrate scenario-based risk assessment, capability
analysis, and capability-
based planning approaches to reduce all sorts of risks—from
natural hazards, over
technical failures to malicious threats—by enhancing
capabilities for dealing with
[email protected]

T
ab
le
5.
1
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fo
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ug
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it
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[email protected]
86 E. Pruyt

F
ig
.5
.2
R
eg
io
n
1
of
a
tw
o-
re
gi
on
sy
st
em
dy
na
m
ic
s

(S
D
)
fl
u
m
od
el
[email protected]
Fig. 5.3 3D scatter plots of 20,000 Latin-Hypercube samples for
region 1 with X-axis: worst flu
peak (0–50 months); Y -axis: infected fraction during the worst
flu peak (0–50 %); Z-axis: fatal
cases (0–5 × 107)
them. Current IRCAs mainly allow dealing with one or a few
specific scenarios for
a limited set of relatively simple event-based and relatively
certain risks, but not for
dealing with a plethora of risks that are highly uncertain and
complex, combina-
tions of measures and capabilities with uncertain and dynamic
effects, and divergent
opinions about degrees of (un)desirability of risks and
capability investments.

The next generation model-based IRCAs may solve many of the
shortcomings of
the IRCAs that are currently being used. Figure 5.4 displays a
next generation IRCA
for dealing with all sorts of highly uncertain dynamic risks.
This IRCA approach,
described in more detail in Pruyt et al. (2012), combines EMA
and modeling and
simulation, both for the risk assessment and the capability
analysis phases. First,
risks—like outbreaks of new flu variants—are modeled and
simulated many times
across their multidimensional uncertainty spaces to generate an
ensemble of plausible
risk scenarios for each of the risks. Time series classification
and machine learning
techniques are then used to identify much smaller ensembles of
exemplars that are
representative for the larger ensembles. These ensembles of
exemplars are then used
as inputs to a generic capability analysis model. The capability
analysis model is
subsequently simulated for different capabilities strategies
under deep uncertainty
(i.e., simulating the uncertainty pertaining to their
effectiveness) over all ensembles
of exemplars to calculate the potential of capabilities strategies
to reduce these risks.
[email protected]
88 E. Pruyt

Fig. 5.4 Model-based integrated risk-capability analysis (IRCA)
Finally, multi-objective robust optimization helps to identify
capabilities strategies
that are robust.
Not only does this systems-of-systems approach allow to
generate thousands of
variants per risk type over many types of risks and to perform
capability analy-
ses across all sorts of risk and under uncertainty, it also allows
one to find sets
of capabilities that are effective across many uncertain risks.
Hence, this integrated
model-based approach allows for dealing with capabilities in an
all-hazard way under
deep uncertainty.
This approach is currently being smartened using adaptive
output-oriented sam-
pling techniques and new time-series classification methods that
together help to
identify the largest variety of dynamics with the minimal
amount of simulations.
Covering the largest variety of dynamics with the minimal
amount of exemplars is
desirable, for performing automated multi-hazard capability
analysis over many risks
is—due to the nature of the multi-objective robust optimization
techniques used—
computationally very expensive. This approach is also being
changed from a multi-
model approach into a multi-method approach. Whereas, until
recently, sets of SD
models were used; there are good reasons to extend this
approach to other types of

systems modeling approaches that may be better suited for
particular risks or—using
multiple approaches—help to deal with methodological
uncertainty. Finally, settings
of some of the risks and capabilities, as well as exogenous
uncertainties, may also
be fed with (near) real-world data.
5.5.3 Policing Under Deep Uncertainty
The third example relates to another deeply uncertain issue,
high-impact crimes
(HIC). An SD model and related tools (see Fig. 5.5) were
developed some years ago
in view of increasing the effectiveness of the fight against HIC,
more specifically the
fight against robbery and burglary. HICs require a systemic
perspective and approach:
[email protected]
Fig. 5.5 (I) Exploratory system dynamics modelling and
analysis (ESDMA) model, (II) interface
for policy makers, (III) analytical module for analyzing the
high-impact crimes (HIC)system under
deep uncertainty, (IV) real-world pilots based on analyses, and
(V) monitoring of real-world data
from the pilots and the HIC system
These crimes are characterized by important systemic effects in
time and space, such

as learning and specialization effects, “waterbed effects”
between different HICs
and precincts, accumulations (prison time) and delays (in
policing and jurisdiction),
preventive effects, and other causal effects (ex-post preventive
measures). HICs are
also characterized by deep uncertainty: Most perpetrators are
unknown and even
though their archetypal crime-related habits may be known to
some extent at some
point in time, accurate time and geographically specific
predictions cannot be made.
At the same time, is part of the HIC system well known and is a
lot of real-world
information related to these crimes available.
Important players in the HIC system besides the police and
(potential) perpetrators
are potential victims (households and shopkeepers), partners in
the judicial system
(the public prosecution service, the prison system, etc.). Hence,
the HIC system
is dynamically complex, deeply uncertain, but also data rich,
and contingent upon
external conditions.
The main goals of this pilot project were to support strategic
policy making under
deep uncertainty and to test and monitor the effectiveness of
policies to fight HIC.
The SD model (I) was used as an engine behind the interface for
policy makers
(II) to explore plausible effects of policies under deep
uncertainty and identify real-
world pilots that could possibly increase the understanding
about the system and

effectiveness of interventions (III), to implement these pilots
(IV), and monitor their
outcomes (V). Real-world data from the pilots and improved
understanding about
the functioning of the real system allow for improving the
model.
[email protected]
90 E. Pruyt
Today, a lot of real-world geo-spatial information related to
HICs is available
online and in (near) real time which allows to automatically
update the data and
model, and hence, increase its value for the policy makers. The
model used in this
project was an ESDMA model. That is, uncertainties were
included by means of
sets of plausible assumptions and uncertainty ranges. Although
this could already
be argued to be a multi-model approach, hybrid models or a
multi-method approach
would really be needed to deal more properly with systems,
agents, and spatial
characteristics. Moreover, better interfaces and connectors to
existing ICT systems
and databases would also be needed to turn this pilot into a real
decision-support
system that would allow chiefs of police to experiment in a
virtual world connected
to the real world, and to develop and test adaptive robust
policies on the spot.

5.6 Conclusions
Recent and current evolutions in modeling and simulation
together with the recent
explosive growth in computational power, data, social media,
and other evolutions
in computer science have created new opportunities for model-
based analysis and
decision making.
Multi-method and hybrid modeling and simulation approaches
are being devel-
oped to make existing modeling and simulation approaches
appropriate for dealing
with agent system characteristics, spatial and network aspects,
deep uncertainty, and
other important aspects. Data science and machine learning
techniques are currently
being developed into techniques that can provide useful inputs
for simulation models
as well as for building models. Machine learning algorithms,
formal model analysis
methods, analytical approaches, and new visualization
techniques are being devel-
oped to make sense of models and generate useful policy
insights. And methods and
tools are being developed to turn intuitive policy making into
model-based policy
design. Some of these evolutions were discussed and illustrated
in this chapter.
It was also argued and shown that easier connectors to
databases, to social media,
to other computer programs, and to ICT systems, as well as
better interfacing software
need to be developed to allow any systems modeler to turn

systems models into
real decision-support systems. Doing so would turn the art of
modeling into the
computational science of simulation. It would most likely also
shift the focus of
attention from building a model to using ensembles of systems
models for adaptive
robust decision making.
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triple jump towards real-world
complexity. TU Delft Library, Delft.
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dynamics in the era of big data. In: Proceedings of the 32nd
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uncertainty: a multi-model exploration of
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doi:10.1002/sdr.1510
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approach for exploring deeply uncertain issues, analyzing
models, and designing adaptive robust
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the System Dynamics Society.
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comprehensive analytical approach for
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modeling for a complex world.
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[email protected]
Chapter 6
Features and Added Value of Simulation Models
Using Different Modelling Approaches
Supporting Policy-Making: A Comparative
Analysis
Dragana Majstorovic, Maria A. Wimmer, Roy Lay-Yee, Peter
Davis
and Petra Ahrweiler
Abstract Using computer simulations in examining, explaining
and predicting so-
cial processes and relationships as well as measuring the
possible impact of policies
has become an important part of policy-making. This chapter
presents a compara-
tive analysis of simulation models utilised in the field of policy-
making. Different
models and modelling theories and approaches are examined
and compared to each
other with respect to their role in public decision-making
processes. The analysis
has shown that none of the theories alone is able to address all
aspects of complex
policy interactions, which indicates the need for the
development of hybrid simula-
tion models consisting of a combinatory set of models built on
different modelling
theories. Building such hybrid simulation models will also
demand the development

of new and more comprehensive simulation modelling
platforms.
D. Majstorovic (�) · M. A. Wimmer
M. A. Wimmer
R. Lay-Yee · P. Davis
Centre of Methods and Policy Application in the Social
Sciences (COMPASS Research Centre),
University of Auckland, Private Bag 92019, 1142 Auckland,
New Zealand
P. Davis
P. Ahrweiler
EA European Academy of Technology and Innovation
Assessment GmbH,
Bad Neuenahr-Ahrweiler, Germany
10.1007/978-3-319-12784-2_6
[email protected]
96 D. Majstorovic et al.
6.1 Introduction
Using computer simulation as a tool in examining, explaining
and predicting social

processes and relationships started intensively during 1990s
(Gilbert and Troitzsch
2005). Since 2000s, a growing recognition of simulation models
playing a role in
public decision modelling processes can be noted (van Egmond
and Zeiss 2010). One
reason for this increased attention is that simulation models
enable the examination of
complex social processes and interactions between different
entities and the potential
impact of policies. For example, simulation models can be used
to examine the impact
of measures such as school closure and vaccination in stopping
the spread of influenza
as the cases described in Sect. 3.1 and 3.2 demonstrate; or to
examine the influence of
different policies in the early years of life as the case outlined
in Sect. 3.3 evidences.
This chapter presents a comparative analysis of different
simulation models with
respect to their role in public decision-making processes. The
focus is on investigating
the differences between simulation models and their underlying
modelling theories in
order to find variables that impact the effectiveness of the usage
of simulation models
in policy-making. The ultimate goal is to provide an
understanding of the peculiarities
and the added value of different kinds of simulation models
generated on the basis of
particular modelling approaches. The chapter also aims at
giving indications of how
existing approaches to policy simulation can and should be
combined to effectively
support public policy-making in a comprehensive way.

This comparative analysis was performed as part of the
eGovPoliNet1 initia-
tive, which aims at developing an international
multidisciplinary policy community
in information and communication (ICT) solutions for
governance and policy
modelling. eGovPoliNet brings researchers from different
disciplines and com-
munities together for sharing ideas, discussing knowledge assets
and developing
joint research findings. The project fosters a multidisciplinary
approach to in-
vestigate different concepts in policy modelling. In
investigating these concepts,
researchers from different disciplines (such as information
systems, e-government
and e-participation, computer science, social sciences,
sociology, psychology, or-
ganisational sciences, administrative sciences, etc.) collaborate
to study the—so
far mostly mono-disciplinary—approaches towards policy
modelling. With this ap-
proach, eGovPoliNet aims at contributing to overcoming the
existing fragmentation
of research in policy modelling across different disciplines.
The research carried out in this paper was based on the
literature study of policy
modelling approaches whereby the authors collaborated with
expertise from their
own academic background. On the other hand, a comparative
analysis of five differ-
ent simulation models was performed using a framework of
comparison developed
along the eGovPoliNet initiative. The selection of the cases was

based on the authors’
1 eGovPoliNet—Building a global multidisciplinary digital
governance and policy modelling re-
search.and practice community. See http://www.policy-
community.eu/ (last access: 28th July
2014).
[email protected]
Different Modelling. . . 97
access to and involvement in generating the particular models.
Accordingly, the re-
spective authors also elaborated the individual descriptions of
the simulation models.
The subsequent comparison and synthesis of simulation models
based on different
modelling approaches was done in a collaborative way. Findings
were developed
jointly and present views of different disciplines concerning the
role of simulation
models in policy modelling as well as possible ways of
advancement of the usage of
combinations of simulation models and joint elaborations
thereof.
The key research questions guiding the comparative
investigation of different
simulation models are twofold: (1) What particular modelling
theories, frameworks
and/or methods build the theoretical and methodical foundations
of simulation mod-

els in policy modelling? (2) In what way do simulation models
developed on the basis
of different foundations (cf. question (1)) differ and what
lessons can be drawn from
using different simulation models in policy modelling? To
answer research question
(1), the different theoretical and methodical grounds of
simulation approaches will
be studied, while for research question (2), five different
simulation models will be
compared and analysed.
The chapter is organised as follows: Sect. 6.2 first provides an
understanding of
the key terms and subsequently examines three different and
widely used theories and
approaches to simulation modelling (system dynamics, micro-
simulation and agent-
based modelling (ABM)) in order to establish common grounds
for the research
context. Subsequently, a framework for the comparative
analysis of simulation mod-
els based on these modelling paradigms is introduced and five
different simulation
models are analysed in Sect. 6.3 (VirSim, MicroSim, MEL-C,
OCOPOMO’s Kosice
model and SKIN). Then in Sect. 6.4, these models are compared
and discussed to
extract features of usage, benefits and the main characteristics
of specific approaches
to simulation modelling in policy-making. Some reflections on
the research and
practice implications as well as further research needs are also
drawn from the com-
parative analysis. We conclude with a reflection on the results
and insights gathered

in Sect. 6.5.
6.2 Foundations of Simulation Modelling
Gilbert and Troitzsch define a simulation model as ‘a
simplification—smaller, less
detailed, less complex, or all of these together—of some other
structure or system’
(Gilbert and Troitzsch 2005). A simulation model is a computer
program that captures
the behaviour of a real-world system and its input and possible
output processes. It
relies on data from the real world to create an artificial one that
mimics the original but
upon which experiments can be performed (Gilbert and
Troitzsch 2005. According to
Gilbert and Troitzsch, simulation models are useful for many
reasons—it is easier,
less expensive and in many cases the only appropriate way (e.g.
spreading of a
disease) or only feasible way (e.g. the consequences of some
policy decisions can be
seen only many years ahead as is the case with urbanisation) of
examining possible
impacts of policies. The output of a simulation is a set of
measurements describing
[email protected]
the observable reactions and a performance of a real-world
system. For example,
simulation models may produce forecasts or projections as

output into the future,
hence supporting policy-making processes, while stakeholders
could use simulation
models as support tools in examining possible impacts of
different policies(Gilbert
and Troitzsch 2005. Simulation models can also be used for a
better understanding of
real-world processes and relationships (Gilbert and Troitzsch
2005, for entertainment
(e.g. the simulation game MoPoS2, where a player is a central
bank governor), as
well as for education and training purposes (e.g. the simulation
model GAIM3). On a
higher abstraction level, simulation models can even be used for
the formalisation of
social theories producing social science specifications (Gilbert
and Troitzsch 2005).
Different paradigms (i.e. approaches and theories) to simulation
modelling ex-
ist in the literature. They vary in aspects of the reality they
model as well as in
methods they use to produce a simulation model (Gilbert and
Troitzsch 2005. Three
approaches are considered in this chapter: system dynamics as a
representative type
of macro-simulation, ABM as a representative type of modelling
social behaviour of
groups, and micro-simulation focusing on modelling
individual’s evolution. These
approaches have been selected as they are well-known and
widely used.
The system dynamics approach models a situation at a global
level to describe
a real-world system using analytical means via systems of

differential equations
(Gilbert and Troitzsch 2005. A real-world system is described
and analysed as a
whole at the macro-level (Forrester 1961) and represented using
flow diagrams and
internal feedback loops (Harrison et al. 2007). Such a model
does not require much
data and the output of the model consists of plots describing
behaviour and the
changes of the initial values of the variables and parameters of
the model over time.
To describe behaviour of the real-world process accurately, a
model needs to be run
many times with different parameter values (Maria 1997). A
typical use of system
dynamics models is macro-economic modelling as well as for
describing the impact
of policies during, for example, a spread of a disease.
According to (Astolfi et al.
2012), system dynamics models are well suited for predicting
short-term policy
impacts.
Complex policy issues require approaches that enable research
synthesis and the
use of systems thinking (Milne et al. 2014). Micro-simulation
modelling has the
potential to represent systems and processes in various social
domains and to test
their functioning for policy purposes (Anderson and Hicks
2011; Zaidi et al. 2009).
A micro-simulation model is based on empirical individual-
level data and it can
account for social complexity, heterogeneity, and change
(Orcutt 1957; Spielauer
2011). Micro-simulation operates at the level of individual

units, each with a set of
associated attributes as a starting point. A set of rules, for
example equations derived
from statistical analysis of (often multiple) survey data sets, is
then applied in a
stochastic manner to the starting sample to simulate changes in
state or behaviour.
2 MoPoS—A monetary Policy Simulation Game (Lengwiler
2004).
3 GAIM—Gestione Accoglienza IMmigrati (Sedehi 2006) is
used for the training of foreign
intercultural mediators in the immigration housing management
courses.
[email protected]
Modifications of influential factors can then be carried out to
test hypothetical ‘what
if’ scenarios on a key outcome of policy interest (Davis et al.
2010). Micro-simulation
can integrate, and accommodate the manipulation of, and the
effects of variables
across multiple model equations (often derived from multiple
data sources) in a single
simulation run. Thus, each otherwise separate equation is given
its social context and
influence among the other equations, representing a system of
interdependent social
processes.

Gilbert defines ABM as ‘a computational method that enables a
researcher to cre-
ate, analyse and experiment with models composed of agents
that interact within an
environment’ (Gilbert 2007). In artificial intelligence, agents
are ‘self-contained pro-
grams that can control their own actions based on their
perceptions of the operating
environment’ (Gilbert and Troitzsch 2005). Applied to social
processes, agents are
individuals or groups of individuals aware of their environment
and at the same time
proactive in interactions with each other and their surroundings.
Agent-based simu-
lation models capture and explain the behaviour of agents and
the dynamics of their
social interactions, and they usually do not assume future
predictions (Srbljanovic
and Skunca 2003, Gilbert and Troitzsch 2005). ABM is
considered a powerful tool
for developing, testing and formalising social theories and
examining complex social
interactions (Gilbert and Troitzsch 2005). For example, agent-
based simulations are
able to describe complex social phenomena at a global macro-
level emerging from
simple micro-level interactions between the agents (Srbljanovic
and Skunca 2003).
The application of ABM offers two major advantages (Gilbert
and Troitzsch 2005): a
capability to show from where collective phenomena come
based on isolation of crit-
ical behaviour and the main agents, and a possibility to explore
various alternatives
of development.

Building a simulation model means developing a computer
program either from
scratch or from adapting existing models. To achieve this, tools
such as AnyLogic4,
NetLogo5, etc.6 are being used. Prior to programming the
simulation model, ap-
propriate knowledge about the policy context that should be
explored needs to be
collected. Different literature provide indications of the steps in
an ordered process
as shown in Fig. 6.1. It is to be noted that not necessarily every
step is carried out by a
simulation modeller. Depending on the expertise of
programmers or policy analysts,
steps 1 and 2 are in some cases merged, or steps 3 and 4 are not
differentiated. In
the simplest case, an expert policy modeller might even just
perform steps 1, 4 and
5. However, to support a wider understanding of policy
modelling, the sharing of
the overall concept of analysis and programming, and a higher
quality of simulation
models, the performance of all five steps is highly
recommended.
The first three steps to generate a simulation model are to (1)
collect source data,
(2) to develop a conceptual model and (3) to design the
simulation model. These
4 http://www.anylogic.com/ (last access: 28th July 2014).
5 http://ccl.northwestern.edu/netlogo/ (last access: 28th July
2014).
6 A more detailed overview of tools and technologies
supporting policy making is provided in
(Kamateri et al. 2014).

[email protected]
Fig. 6.1 Generic steps for
developing simulation models
Program the simula on
model
Conceptual
modelling
Simula on
model
design
Analysis of
source data
Verifica on and
Valida on
steps form the analytical work of policy modelling and can also
be labelled ‘policy
analysis’ and ‘conceptual modelling’. The ways and methods to
collect and analyse
source data (step 1) depend on the type of simulation model to
be generated and its
underlying modelling paradigm. For example, micro-simulation
is based on large
amounts of representative data gathered on individuals; it
considers characteristics

of individuals and is able to reproduce social reality (Martini
and Trivellato 1997).
Micro-simulation is beneficial in predicting both short-term as
well as long-term
impacts of policies (Gilbert and Troitzsch 2005). System
dynamics models the real-
world system as a whole, i.e. at the macro-level (Forrester
1961) by using (a small
set of) aggregated data. ABM is valuable for describing and
explaining complex
social interactions and behaviour, thus contributing to the
understanding of a real-
world social system and to a better management of different
social processes (Gilbert
and Troitzsch 2005). The focus is on groups and individuals
interacting in a social
system and the amount of data needed for developing the
simulation model can be
considered moderate. The particular approach of simulation
modelling determines
the complexity of data analysis (i.e. highly complex and intense
for micro-simulation,
moderate to high complexity for ABM (depending on the
number of agents and the
aggregation concept), and rather low for system dynamics).
Inputs for data analysis
are features, descriptions, relationships and specifications of the
observed real-world
system (Gilbert and Troitzsch 2005). Data analysis can be
performed through many
different ways ranging from qualitative and quantitative data
analysis methods of
the social sciences (Mayring 2011) to action research
(Greenwood and Levin 2006),
design research (Collins et al. 2004) and active stakeholder
engagement using, e.g.

scenario-building and online citizen participation methods
(Wimmer et al. 2012).
The second step—conceptual modelling—is not always
explicitly implemented as
already mentioned. It is a step that is widely used in action
research and in design
[email protected]
research. Newer approaches to policy modelling are based on
the value of concep-
tualising a policy context and accordingly building conceptual
models from the data
analysed, as is, e.g. described in (Scherer et al. 2013). Since
simulation models are
simplifications of reality (Zeigler 1976), conceptual modelling,
in practical terms,
means to decide on which characteristics of the real-world
system are to be included
in a simulation model and which ones are not (Gilbert and
Troitzsch 2005). In the third
step—design of the actual simulation model—the programming
of the simulation
model is prepared by building a construct of the simulation
model (again dependent
on the modelling paradigm). The fourth step—programming the
simulation model—
involves putting hands on writing the code using a particular
tool for programming.
The fifth step—verification and validation—aims to check if a
simulation model

behaves as desired (i.e. verification) and whether the model
describes the intended
real-world system in a satisfactory way and gives reliable
outputs (i.e. validation).
Validation can be conducted by comparing known behaviour
and parameters of the
real-world system with the outputs of a simulation model (Maria
1997).
Based on the insights of different paradigms of simulation
models, the next
sections analyse and compare different models built on these
theories and approaches.
6.3 Analysis of Simulation Models of Different Modelling
Approaches
In this section, the analysis of five simulation models is
presented with respect to
their contribution to policy modelling in different public
domains. The main goal is
to describe and compare different simulation models in order to
identify similarities
and differences that suggest approaches, tools and techniques
that are useful and
effective in different policy modelling contexts. For the
comparative analysis, eGov-
PoliNet developed a framework which serves as a template to
ensure comparability
across particular aspects of study and to simplify understanding.
The framework is
divided into three parts: (1) abstract, which gives a brief
summary of the model under
investigation, and its context; (2) metadata, providing general
information such as
name of the model, developer, the publication date, background

documents used in
developing the model, references, tools needed to run the
simulation model (for an
ordinary user), and a reference to the source of the model; and
(3) conceptual aspects
of interest in the comparison such as disciplines involved in the
model development,
underlying theory, particular methods applied to develop the
model, technical frame-
works and tools used to develop the simulation model7,
application domain of the
model, constraints of using the model in a particular way,
examples of (re)use of
the formal model (i.e. giving reference to policy cases and
projects where the model
7 A comparative analysis of tools and technical frameworks is
provided in (Kamateri et al. 2014).
[email protected]
Table 6.1 Simulation models examined in the comparative
analysis
Based on theory Simulation model
System dynamics VirSim—a model to support pandemic policy-
making
(cf. Sect. 6.3.1)
Micro-simulation MicroSim—micro-simulation model:
modelling the Swedish

population (cf. Sect. 6.3.2)
MEL-C—Modelling the early life-course (cf. Sect. 6.3.3)
Agent-based modelling (ABM) OCOPOMO’s Kosice case (cf.
Sect. 6.3.4)
SKIN—simulating knowledge dynamics in innovation
networks (cf. Sect. 6.3.5)
is/was used), transferability of the simulation model in other
domains or disciplinary
contexts, and concluding recommendations on the model
development and use).8
The simulation models studied in this chapter are based on the
modelling
paradigms presented in Sect. 6.2, namely system dynamics,
micro-simulation, and
ABM. Table 6.1 indicates the five simulation models examined
in the comparative
analysis and presented in the subsections below. It was not the
aim of the authors
to present an exhaustive list of models but rather a collection
that is an informative
choice of specific simulation models corresponding to different
modelling theories.
The models were chosen because the authors had the access to,
and knowledge about
these simulation models, and they were directly involved in the
development of the
simulation models analysed in Sect. 3.3, 3.4 and 3.5. The two
models described in
Sect. 3.1 and 3.2 are interesting for the comparison since they
represent the same
policy domain and use the same data but represent

implementations of different
modelling paradigms and methods.
The five simulation models are presented in the following
subsections. The de-
scriptions follow the structure suggested by the framework
proposed by eGovPoliNet,
i.e. abstract, metadata and conceptual aspects of interest. While
the abstract is
provided as a narrative paragraph, the metadata and conceptual
aspects are each elab-
orated in a tabular form. The subsequent Sect. 6.4 provides a
comparative discussion
of the different models and their added value to policy
modelling.
6.3.1 VirSim—A Model to Support Pandemic Policy-Making
VirSim simulates the spread of pandemic influenza and enables
evaluating the effect
of different policy measures (Fasth et al. 2010). The main goal
is to find the most
optimal policies connected to the starting time and the duration
of school closure as
8 The framework is published in Annex I to technical report D
4.2 of eGovPoliNet: Maria A.
Wimmer and Dragana Majstorovic (Eds.): Synthesis Report of
Knowledge Assets, including
Visions (D 4.2). eGovPoliNet consortium, 2014, report
available under http://www.policy-
community.eu/results/public-deliverables/ (last access: 28th
July 2014).
[email protected]

well as the pace and the vaccination coverage. Using the model,
it is also possible
to estimate public costs due to the absence of staff from work
during sick leave.
The model considers real population data in Sweden at both
national and regional
levels (Fasth et al. 2010). In VirSim, the population is divided
into three age groups:
individuals less than 20 years old, those between 20 and 59
years, and people aged
60 years and more. It is initially assumed that influenza spreads
within and between
groups with different probabilities. For each age group, a SEIR
model (Susceptible,
Exposed, Infected, and Recovered) is constructed, which
represents the dynamics
of spread of the disease. This means that a healthy person starts
as a susceptible
(S), becomes exposed (E), then infected (I) and, after some time
recovered (R) (or
dead). VirSim supports scenario analysis (i.e. ‘what-if’
analysis), which means that
a user can combine a number of different parameters producing
‘real’ scenarios and
examine the impact of policies while asking ‘what could happen
if we apply policy
XY’ (Fasth et al. 2010) (Table 6.2).
Table 6.2 Analysis of metadata and conceptual data of the
simulation model VirSim

Metadata
Name VirSim
Developer Tobias Fasth, Marcus Ihlar, Lisa Brouwers
Publication date 2010
Background documents To segregate the Swedish population
into three age
groups: (Statistics Sweden (Statistiska centralbyrån,
SCB 2009)
To estimate frequency of social contacts between and
within age groups: (Wallinga 2006)
To decide on the duration of a latent period: (Carrat
et al. 2008, Fraser et al. 2009)
Reference(s) (Fasth et al. 2010)
Tools needed to run the model Web browser, Internet
Source of the model http://www.anylogic.com/articles/virsim-a-
model-to-
support-pandemic-policy-making (last access: 28th
July 2014)
http://people.dsv.su.se/∼maih4743/VirSim/VirSim.html
(needs Java Platform SE 7 U activated; last access:
28th July 2014)
Conceptual aspects
Discipline(s) Health science, Information
technology/E-Government
Based on theory System dynamics

Developed through method SEIR model (susceptible, exposed,
infected,
recovered)
Technical framework/tools used for
development
AnyLogic
Application domain(s) Policy-making under pandemic influenza
[email protected]
Table 6.2 (continued)
Metadata
Constraints of using the model in a
particular way
The VirSim model does not take into account parameters
that are also important for the transmission and spreading
of the influenza virus, such as effect of weather and
temperature conditions, geographical differences between
regions as well as diverse social structures including
travelling frequency, gender and hygiene habits. It is not
possible to analyse many of the missing parameters since
the underlying SEIR model and system dynamics method
do not take into account social differences. Hence, the
same infection probability was assigned to all people
within the three age groups

Examples of use (projects/cases) Policy-making under pandemic
influenza in Sweden in
2009. Tested policies were vaccination and school closure
(Fasth et al. 2010)
Transferability of formal model in
other domains or disciplinary
contexts
The initial values for all parameters are provided, for
example, starting time of vaccination and the infection risk
for different age groups, based on the documents and the
data available in the time of the development of the model.
However, VirSim allows for the change of all parameter
values, including those initially assumed. This assures that
the model is re-usable when other data become available.
To our knowledge, the model is not transferable to other
domains and contexts since it does not allow for a change
of the parameters as such, their number, and the
underlying differential equations
Concluding remarks on simulation
model development and/or use
VirSim runs fast and a user can easily manipulate different
parameters. However, the user interface does not include
descriptions of the parameters; a user has to guess their
meaning and a range of values, based on their names. In
some cases, this is difficult, for example, for the parameter
‘vaccination . . . starts after’ with the initial value of
147—it is not clear for what the given initial value stands.
Apart from this issue, the model is intuitive and easy to
work with. The model is based on the scenario analysis—a
user posts a question (‘what could happen if we apply
certain policy under certain conditions . . . ’) and gets the
answer in a form of suitable plots. This allows

policy-making officials to discuss policies further towards
finding the most suitable ones. To provide accurate and
significant results, VirSim uses real population data in
Sweden, at the national and regional level (Fasth et al.
2010). To use the simulation model in similar contexts, the
model development should become flexible to support the
definition of custom variables and at least some classes of
differential equations that are suitable for modelling
similar phenomena. Also, based on supported types of
processes, the description of possible domains of
application to which the model could be transferred would
be helpful
[email protected]
6.3.2 MicroSim—Micro-simulation Model: Modelling the
Swedish Population
According to Brouwers et al, MicroSim addresses the problem
of the spread of
influenza in Sweden. It is an event-driven micro-simulation
model with discrete
time steps of an hour, developed for exploring the impact of
different intervention
policies based on vaccination, isolation and social distancing.
Each person living in
Sweden was modelled in many details, including age, family
status, employment
details, and important geographical data, such as home and
workplace coordinates.
Such a modelling strategy provided a fine-grained

differentiation between age groups,
people’s daily routines and their educational level. This enabled
examining the spread
of influenza through different social contacts as well as
analysing the spatial spread
of the disease within the time range of 1 h (Brouwers et al.
2009a, 2009b) (Table 6.3)
simulation model MicroSim
Metadata
Name MicroSim—micro-simulation model: modelling the
Swedish
population
Developer Lisa Brouwers, Martin Camitz, Baki Cakici, Kalle
Mäkilä, Paul
Saretok
Background documents MicroSim uses registry data obtained
from Statistics Sweden
(Statistiska centralbyrån, SCB)a to generate the simulated
population, in particular:
National Population Register (2002) to describe age, marital
status, children, employment, IDs father and mother;
Employment Register (2002) to describe company, workplace,
branch, municipality of the workplace for each individual;
Employment Register (2002) to obtain family household
coordinates, workplace coordinates, and school coordinates
Reference(s) (Brouwers et al. 2009a, 2009b)

Tools needed to run the
model
Executable that runs within C++ environment
Source of the model Available on demand from
http://arxiv.org/abs/0902.0901 (last
access: 28th July 2014)
Conceptual aspects
Discipline(s) Health science, Information technology/E-
Government
Based on theory Micro-simulation
Developed through method Population analysis
Framework/tools used for
development
C++
Application domain(s) Policy-making under pandemic influenza
[email protected]
Metadata
Constraints of using the

model in a particular way
The model relies on particular data from Sweden and therefore
cannot be used for other countries. Also, due to possible
migrations and changes in structure of Swedish population, the
model has to be validated against new available data
Examples of use
(projects/cases)
Policy-making under pandemic influenza in Sweden in Autumn
of 2009
Transferability of formal
model in other domains or
disciplinary contexts
The model can be used as a basis for examining effects of
different policies as well as dependencies in the real-world
systems and processes based on social and geographical
distributions
Concluding remarks on
simulation model
development and/or use
While micro-simulation models in general use only sample data
of the population, MicroSim uses personal, employment and
geographic data of the complete Swedish population
(approximately 9 million people), which provides an explicit
enhancement of the model’s accuracy and reliability. Such
detailed representation provides conditions suitable for realistic
simulations of influenza outbreaks in Sweden. However,
micro-simulation models based on the ontology of the
population
is not robust towards demographic changes in the social

structure
of a population
a http://www.scb.se (last access: 28th July 2014).
6.3.3 MEL-C—Modelling the Early Life-Course
MEL-C is a Knowledge-based Inquiry tool With Intervention
modelling (KIWI)
developed on the early life-course as a decision support aid to
policy analysts and
advisors in New Zealand. Underlying the tool is a dynamic
discrete-time micro-
simulation model using a social determinants framework to
predict child outcomes,
for which the key parameters have been estimated from existing
longitudinal cohort
studies in New Zealand, initially the Christchurch Health and
Development Study.
These parameters were applied to a starting sample synthesised
from a combination
of data from the national census and from the longitudinal
studies. Thus a set of
synthetic representative early life histories was created that
reproduced patterns found
in the original data. The tool can be interrogated with realistic
policy scenarios by
changing baseline features or parameters in the model and
observing the effect on
outcomes, for example, ‘what if’ the initial social determinants
were different and
what would be their impact. The model content, tool interface
and inquiry system
have been developed in cooperation with central government
policy advisors drawn
from the agencies with a special interest in the early life-course

(Mannion et al. 2012)
(Table 6.4).
[email protected]
simulation model MEL-C
Metadata
Name MEL-C—modelling the early life-course
Developer COMPASSa
Background documents (Fergusson et al. 1989; Solar and Irwin
2010)
Reference(s) (Mannion et al. 2012; Milne, et al. 2014; McLay et
al. 2014;
Lay-Yee et al. 2014)
model
The MEL-C executable, which includes JAMSIM (consisting of
ASCAPE, JAVA and R) and simulation code run with R and
tailored functions from the R Simario package developed by
COMPASS

Source of the model See http://code.google.com/p/jamsim/ for
JASMIM (last access:
28th July 2014) See http://code.google.com/p/simario/ for R
SIMARIO package (last access: 28th July 2014) MEL-C
simulation model accessible on request from the COMPASS
research centre
Conceptual aspects
Discipline(s) Social and health sciences (sociology, psychology,
epidemiology), statistics, computer science, policy sciences
Based on theory Child development, Social determinants of
health,
Micro-simulation
Developed through method Regression analysis R and JAVA
programming Micro-simulation
modelling End-user engagement Cluster matching and data
imputation
development
MEL-C as a single executable software application in which
users can interrogate the model from the ‘front end’ and not
need
to deal with the ‘behind-the-scenes’ computer programs and
statistical models. The tools used are: Eclipse, StatEt, Git
control,
Ivy. ASCAPEb and Jamsimc (JAVA) for front end Simario (R)d
for execution of models
Application domain(s) Early life-course, Health, Justice,
Education, Social Policy,

Policy scenarios, User interface
Limited by variables available in the source data sets.
Relationships between variables are un-directional with no
feedback. Scenarios tested involve changing the distribution of
variables not the effects (e.g. the effect of X on Y). Potential
geographical and period limits of data sources. Discrete time
only
Examples of use
(projects/cases)
Illustrative application to social determinants of health and
end-user engagement
[email protected]
Metadata
The model is of generic applicability in early life-course
analysis. Subject to data availability and funding, it is possible
to extend the model to later periods in the life-course and other
domains. There may be other dynamic socio-demographic

processes where this approach can be applied
simulation model
The model is restricted to a notional ‘evidence-based’/
science-informed approach to policy development. The model is
conceptually predicated on the primacy of social determinants.
The role of stakeholders is limited to the rather formal role of a
policy advisor or analyst seeking to weigh different options
within a prescribed range. The model is able to reproduce
actualities and to produce plausible substantive results in
scenario testing. The model has the great potential of combining
a realistic data framework with estimates derived from
meta-analyses, systematic reviews and other research sources.
The model is a simplification of reality but is nevertheless a
powerful source of information that can be interrogated by
end-users and can be considered alongside other evidence for
policy
a http://www.arts.auckland.ac.nz/en/about/ourresearch-
1/research-centres-and-archives/centre-of-
methods-and-policy-application-in-the-social-sciences-
compass/about-compass.html (last access:
28th July 2014).
b http://ascape.sourceforge.net/ (last access: 28th July 2014).
c http://code.google.com/p/jamsim/ (last access: 28th July
2014).
d http://code.google.com/p/simario/ (last access: 28th July
2014).
6.3.4 Ocopomo’s Kosice Case
Energy policy is increasingly receiving attention, especially in
exploring renewable

energy sources, energy saving and to raise awareness about
energy policy among
citizens. The aim of the simulation model developed for the
Kosice self-governing
region was to capture the behaviour of key stakeholders and
decision-makers towards
a new energy policy moving to better house insulation and to
using renewable en-
ergy sources. The renewable energy policy case combined the
scenario-method with
ABM to explore social behaviour and interrelations between
stakeholders, economic
conditions of the region and realistic social dynamics. Based on
the stakeholder in-
puts gathered through scenarios developed via an online e-
participation platform, a
conceptual model was developed which informed the agent-
based simulation model.
The simulation model helped to test the effectiveness of various
policy options (e.g.
to support better insulation of houses, to invest in renewable
energy sources such as
gas, coal, and biomass, etc.) (Wimmer et al. 2012). A
particularity of the model is the
possibility to trace evidence data provided in stakeholders’
scenarios or background
documents via a conceptual model to inform the simulation
model. Accordingly it is
possible for stakeholders to navigate from the simulation
outputs back to the evidence
input (Lotzmann and Wimmer 2013) (Table 6.5).
[email protected]

simulation model developed in
OCOPOMO’s Kosice case
Metadata
Name OCOPOMO’s Kosice case
Developer Partners of the OCOPOMO consortium, involving
University of
Koblenz-Landau, Scott Moss Associates, Technical University
of
Kosice, Intersoft and Kosice Self-Governing Region (KSR)
Background documents Based on the OCOPOMO approach
(Wimmer et al. 2012) a
number of background documents was used, such as: Analysis
of
structural funds (2007–2013) and Projects Approved in 2009 in
KSR Energy policy of KSR (2007) Strategy of Renewable
Energy
Sources Utilization in KSR (2006) Demographic composition of
the households (1996) Annual report 2009, Regulatory Office
for
Network Industries Regional Statistics Database (2010)
Interviews with experts from KSR and local energy providers
Reference(s) (Wimmer et al. 2012; Wimmer 2011; Lotzmann
and Meyer 2011;
Butka et al. 2011; Lotzmann and Wimmer 2013)

model
Collaborative e-participation platform for scenario generation
and stakeholder engagement using the ALFRESCOa Web
content
management system (wiki for scenario generation, discussion,
polling) DRAMS (Lotzmann and Meyer 2011)—the Declarative
Rule-based Agent Modelling system, Consistent Conceptual
Modelling Tool (CCD) (Scherer et al. 2013)
Source of the model http://www.ocopomo.eu/results/software-
and-models/software-
and-model-artefacts/eclipse-based-tools-and-simulation-models
(last access: 28th July 2014)
Conceptual aspects
Discipline(s) Social Science, Information Systems/E-
Government
Based on theory Agent-based modelling, Model-driven
Architecture, Design
Research, Stakeholder theory
Developed through method Stakeholder engagement through
online deliberation, qualitative
analysis methods such as workshops and interviews, conceptual
modelling using Consistent Conceptual Modelling (CCD),
ontology development
development
Eclipse Modelling Framework (EMF)b , Eclipse Graphical
Modelling Framework (GMF)c , Graphical Editing Framework

(GEF)d , Collaborative participation platform for scenario
generation and stakeholder interaction ALFRESCO (wiki,
discussion, voting), DRAMS—Declarative Rule-based Agent
Modelling System, Consistent Conceptual Modelling (CCD)
Tool
Application domain(s) The simulation model is used for policy
development in the field
of energy with the focus on:
• Energy efficiency
• Decrease of energy consumption (heating) and improved
insulation as well as wise spending of energy
• Utilisation of renewable energy sources
Agent-based modelling is particularly applicable for examining
social behaviour but cannot be the only source for policy-
making.
[email protected]
Metadata
Examples of use
(projects/cases)
Renewable energy and heating in Kosice Self-Governing Region

(KSG), Slovakia Housing policy in London, UK Knowledge
transfer in Campania Region, Italy Parts of the OCOPOMO
simulation environment are also used in the GLODERS projecte
Natural conditions of the Kosice region, such as terrain,
location
of and distance from the renewable energy sources,
concentration
of housing, available infrastructure, influence the output of the
model. Therefore, transferability is restricted, and the use of the
model demands for updating the local and natural conditions of
a
region
simulation model
The simulation model is evidence-based and built around the
descriptions, expectations, interactions and beliefs of
stakeholders in the policy-making process. The modelling
process involved stakeholders who expressed their views and
concerns on a policy via collaborative scenarios and
e-participation tools. They acted as partners and researchers in
the modelling process. A key feature of the OCOPOMO policy
modelling approach is to engage stakeholders and to ensure
traceability from evidence-based input of stakeholders in
narrative text to simulation outputs generated through
agent-based simulation. A lesson from using a declarative rule
programming paradigm as implemented in DRAMS vs. the
imperative paradigm in most ABM tools is that the declarative
way is more difficult to program and less intuitive for

programmers
a http://www.alfresco.com/?pi_ad_id=39517088287 (last access:
28th July 2014).
b https://www.eclipse.org/modeling/emf/ (last access: 28th July
2014).
c http://www.eclipse.org/modeling/gmp/ (last access: 28th July
2014).
d http://www.eclipse.org/gef/ (last access: 28th July 2014).
e http://www.gloders.eu/ (last access: 28th July 2014).
6.3.5 SKIN—Simulating Knowledge Dynamics in Innovation
Networks
Simulating Knowledge Dynamics in Innovation Networks
(SKIN) is an agent-based
model used to understand innovation policy initiatives, which
contain heterogeneous
agents, who act and interact in a large-scale complex and
changing social environ-
ment. The agents represent innovative actors who try to sell
their innovations to other
agents and end users but who also have to buy raw materials or
more sophisticated
inputs from other agents (or material suppliers) to produce their
outputs. This basic
model of a market is extended with a representation of the
knowledge dynamics
in and between the agents. Each agent tries to improve its
innovation performance
and its sales by improving its knowledge base through
adaptation to user needs,
incremental or radical learning, and co-operation and
networking with other agents
(Ahrweiler et al. 2004) (Table 6.6)

[email protected]
simulation model SKIN
Metadata
Name Simulating knowledge dynamics in innovation networks
(SKIN)
Developer Gilbert, Nigel; Ahrweiler, Petra; Pyka, Andreas
Publication date 2001, with continuous updates since
Background documents Literature from Evolutionary
Economics, Economic Sociology,
and Science and Technology Studies (see body of literature
cited
in the references as listed next)
Reference(s) (Gilbert et al. 2001; Ahrweiler et al. 2004; Gilbert
et al. 2007;
Pyka et al. 2007; Scholz et al. 2010; Ahrweiler et al. 2011a,
2011b; Gilbert et al. 2014)
model
NetLogo (versions available in alternative languages such as
Java) http://ccl.northwestern.edu/netlogo/ (last access: 28th July
2014)

Source of the model http://cress.soc.surrey.ac.uk/SKIN/ (last
access: 28th July 2014)
Conceptual aspects
Discipline(s) Economics, sociology, science and technology
studies, policy
research, business studies
Based on theory Evolutionary Economics, Organisational
Theory, Organisational
Learning, Field Theory, Complex Systems Theory, Agent-based
modelling
Developed through method Theory formation, empirical
research, implementing theoretical
concepts and empirical insights, consistent conceptual
modelling, agent-based modelling
development
NetLogo
Application domain(s) Knowledge-intensive industries, EU
framework programmes,
national innovation policies, role of specific actors in
innovation
networks
SKIN is about knowledge and agent networks embedded in a
dynamic environment. Not applicable if domain has nothing to

do with it.
Examples of use
(projects/cases)
EU projects:
Simulating self-organizing innovation networks (SEIN)a ,
1998–2001
Network models, governance, and R&D collaboration networks
(NEMO)b , 2006–2009
Managing emerging technologies for economic impact
(ManETEI)c , 2010–2014
Using network analysis to monitor and track effects resulting
from Changes in policy intervention and instruments, (SMART
2010/0025) 2010–2011
Governance of responsible research and innovation (GREAT)d ,
2013–2016
[email protected]
Metadata
SKIN is a multi-disciplinary initiative (see above Discipline(s))
and is therefore used in various disciplinary contexts. However,
as it is about knowledge and agent networks embedded in a
dynamic environment, it is not applicable if the policy domain

is
not working with knowledge, innovation and agent networks
simulation model
The advantages of using SKIN for policy modelling include:
The experiments can be run many times to find statistically
average behaviour.
Experiments can be used to give an indication of the likely
effects of a wide variety of policy measures
Empirical ‘Un-observables’ such as the amount of knowledge
generated, and the number of proposals started but abandoned
before submission, can be measured by instrumenting the
simulation
The problems include determining:
What are the ultimate policy objectives for the support of
Research and Development?
When were the policies being formulated and by whom?
How can the research be presented so that it is interesting and
comprehensible to a policy-making audience?
a http://ec.europa.eu/research/social-
sciences/projects/097_en.html.
b http://cress.soc.surrey.ac.uk/SKIN/research/projects/nemo.
c http://lubswww.leeds.ac.uk/manetei/home/.
d http://www.great-project.eu/ (last access 28th July 2014).
6.4 Comparison of Simulation Models and Discussion of Added
Value and Limitations of Particular Simulation Models
In Table 6.7, the key elements of the five simulation models
introduced in Sect. 3 are
compared using the comparison framework above, and summed
up on the following

aspects: publication date of the model, key aspects of the
model, tools needed to run
the model, discipline(s), simulation paradigm on which the
model is based, method
through which the model is developed, framework and/or tools
used for the devel-
opment of the model, application domain, constraints of using
the model, examples
of use, model’s transferability to other domains, and limitations
and suggestions.
As elaborated in Sect. 3.1, the VirSim simulation model
examines the effect of
different policies to the problem of influenza spread by using
the SEIR model for
modelling the population on a global (macro-) level. Over time,
a person changes
between the categories and this flow is described with a set of
differential equations
(Fasth et al. 2010). The model applies a system dynamics
paradigm. The other exam-
ple of the same policy domain, the micro-simulation model
MicroSim as introduced
in Sect. 3.2, applies a different modelling approach to the same
problem, where each
person is modelled in many details. The spread of influenza is
therefore determined
by many ‘micro’-level factors.
[email protected]

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)
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D
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2D
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tr
an
sf

or
m
at
io
n
to
ol
A
L
F
R
E
S
C
O
W
C
M
S
N
et
L
og

o
[email protected]
T
ab
le
6.
7
(c
on
ti
nu
ed
)
V
ir
S
im
M
ic
ro

S
im
M
E
L
-C
O
C
O
P
M
O
’s
K
os
ic
e
ca
se
S
K
IN
A
pp

li
ca
ti
on
do
m
ai
n
H
ea
lt
h
an
d
so
ci
al
po
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cy
w
it
h
fo

cu
s
on
pa
nd
em
ic
in
fl
ue
nz
a
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ea
lt
h
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d
so
ci
al
po
li
cy

w
it
h
fo
cu
s
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pa
nd
em
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in
fl
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nz
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ea
lt
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st
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ca
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w
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rg
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cu
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om
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ai
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e
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n-
di
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ti
on
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ip
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ee
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s

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it
h
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fe
ed
ba
ck
C
ha
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ab
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ct
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is
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ly
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nt
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pl
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ro
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xa
m
pl
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ak
in

g
un
de
r
pa
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a
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ed
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in
20
09
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pa
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in
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ut
um
n
of
20
09
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lu
st
ra
ti
ve
ap
pl
ic
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n
to
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ci
al
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te

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in
an
ts
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al
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d-
us
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t
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ne
rg
y

po
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cy
to
ex
pl
or
e
st
ak
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ol
de
r
be
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vi
ou
r
in
K
S
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K
F
ur
th
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m
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d:
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P
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on
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K
K
no
w
le
dg
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T
ra
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ni
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pr
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ra
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ra
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an
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ta
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rl
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P
os
si
bl
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ex
te
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od
el
to
la
te
r
pe
ri
od
s
in
th
e
li

fe
-c
ou
rs
e
an
d
to
ot
he
r
do
m
ai
ns
O
th
er
dy
na
m
ic
so
ci

o-
de
m
og
ra
ph
ic
pr
oc
es
se
s
R
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us
e
li
m
it
ed
to
pa
rt
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ul
ar
po
li
cy
an
d
re
gi
on
al
co
nt
ex
t.
M
od
el
ca
n
se
rv
e
as

a
bl
ue
pr
in
t
fo
r
ne
w
m
od
el
s
S
K
IN
ca
n
be
us
ed
in
va

ri
ou
s
di
sc
ip
li
na
ry
co
nt
ex
ts
w
or
ki
ng
w
it
h
kn
ow
le
dg

e
an
d
ag
en
t
ne
tw
or
ks
em
be
dd
ed
in
dy
na
m
ic
en
vi
ro
nm
en

ts
[email protected]
T
ab
le
6.
7
(c
on
ti
nu
ed
)
V
ir
S
im
M
ic
ro

S
im
M
E
L
-C
O
C
O
P
M
O
’s
K
os
ic
e
ca
se
S
K
IN
L
im

it
at
io
ns
of
m
od
el
s/
su
gg
es
ti
on
s
fo
r
ex
te
ns
io
ns
L
im

it
a
ti
o
n
s:
m
od
el
pa
ra
m
et
er
s
an
d
eq
ua
ti
on
s
ar
e

fi
xe
d
an
d
ca
nn
ot
be
m
od
ifi
ed
S
u
gg
es
ti
o
n
s:
E
xt
en

d
th
e
m
od
el
to
su
pp
or
t
de
fi
ni
ti
on
of
cu
st
om
va
ri
ab
le

s
A
ll
ow
fo
r
m
od
el
li
ng
si
m
il
ar
ph
en
om
en
a
A
ll
ow
fo

r
tr
an
sf
er
ab
il
it
y
to
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he
r
do
m
ai
ns
L
im
it
a
ti
o

n
s:
th
e
m
od
el
is
no
t
ro
bu
st
to
w
ar
ds
de
m
og
ra
ph
ic
ch

an
ge
s.
S
u
gg
es
ti
o
n
:
al
lo
w
fo
r
re
al
is
ti
c
si
m
ul

at
io
n
of
in
fl
ue
nz
a
ou
tb
re
ak
s
L
im
it
a
ti
o
n
s:
re
st

ri
ct
ed
to
a
no
ti
on
al
‘e
vi
de
nc
e-
ba
se
d’
/s
ci
en
ce
-i
nf
or

m
ed
ap
pr
oa
ch
co
nc
ep
tu
al
ly
pr
ed
ic
at
ed
on
th
e
pr
im
ac
y

of
so
ci
al
de
te
rm
in
an
ts
L
im
it
ed
ro
le
of
st
ak
eh
ol
de
rs
.

S
u
gg
es
ti
o
n
s:
C
om
bi
na
ti
on
of
a
re
al
is
ti
c
da
ta
fr

am
ew
or
k
an
d
es
ti
m
at
es
de
ri
ve
d
fr
om
tr
ia
ls
an
d
sy
st

em
at
ic
re
vi
ew
s
U
se
fu
l
fo
r
en
d-
us
er
s
L
im
it
a
ti
o

n
s:
D
ec
la
ra
ti
ve
ru
le
en
gi
ne
no
t
ea
sy
to
pr
og
ra
m
C
on

ce
pt
ua
l
an
d
si
m
ul
at
io
n
m
od
el
no
t
ea
sy
to
un
de
rs
ta

nd
fo
r
en
d-
us
er
s.
S
u
gg
es
ti
o
n
s:
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xt
en
d
th
e
C
C

D
to
ol
an
d
tr
an
sf
or
m
at
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to
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to
co
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ec
t
to
ot
he
r

A
B
M
to
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s
su
ch
as
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ny
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og
ic
,N
et
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og
o
et
c.
E
xt
en

d
th
e
m
od
el
tr
an
sf
or
m
at
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n
to
su
pp
or
t
al
so
ba
ck
w

ar
d
co
ns
is
te
nc
y
fr
om
si
m
ul
at
io
n
m
od
el
to
co
nc
ep
tu

al
m
od
el
as
pr
og
ra
m
m
er
s
m
ay
ch
an
ge
si
m
ul
at
io
n
co

de
ea
si
ly
L
im
it
a
ti
o
n
s:
T
he
ul
ti
m
at
e
po
li
cy
ob
je

ct
iv
es
fo
r
th
e
su
pp
or
t
of
re
se
ar
ch
an
d
de
ve
lo
pm
en
t

ne
ed
ye
t
to
be
de
te
rm
in
ed
S
u
gg
es
ti
o
n
s:
Im
po
rt
an
t

to
de
ve
lo
p
m
et
ho
ds
of
th
e
re
se
ar
ch
pr
es
en
ta
ti
on
s
to

be
in
te
re
st
in
g
an
d
co
m
pr
eh
en
si
bl
e
to
a
po
li
cy
-m
ak

in
g
au
di
en
ce
[email protected]
The main advantage of system dynamics models is that they are
fast to run and
technologically not demanding while providing useful
information about the real-
world processes and insights into possible impacts of different
macro-level policies.
However, these models face a number of restrictions. For
example, VirSim initially
assumes infection probabilities where elderly people (age group
60 and more) have
considerably fewer chances of being infected with influenza
compared to the other
two age groups (Fasth et al. 2010). However, the SEIR model
that was applied
cannot predict this and cannot explain why this occurs. The
authors of VirSim used
this result from the micro-simulation model MicroSim and
assumed this phenomenon
happens because of less social contacts of elderly people or
some prior immunity.

VirSim cannot explain this phenomenon because system
dynamics does not include
modelling of various social interactions and other similar
dependencies between
actors since all variables are averaged over particular groups or
the population in
general - in the case of VirSim within the members of a
particular age group. Apart
from the categories of people based on their age, VirSim cannot
identify fine-grain
groups that have higher probability to be infected. For example,
a student has more
chances to be exposed and therefore infected than a researcher
working in the same
university but more in the closed environment of an office while
students usually
have more frequent social interactions among their groups and
communities. It is
important to identify closed environments that have high risk of
spreading influenza,
for example boarding and nursing homes. From the policy
modelling point of view, it
is important to identify high-risk groups to start the vaccination
from there. One could
define refined categories of actors by defining more variables,
but in general, it would
not be possible to represent relations between subcategories,
such as taxonomies or
ontologies needed to represent social contacts or interactions
among actors, due to
the lack of representation apparatus in system dynamics models.
As Gilbert and Troitzsch argue, due to social complexity and
non-linearity, it
is difficult to describe processes and systems analytically. To be
able to examine

interactions between simulation units, other modelling
techniques such as ABM or
micro-simulation models need to be applied for exploring the
social heterogeneity
and structures (Gilbert and Troitzsch 2005).
Micro-simulation models, usually based on a weighted sum of a
representative
sample of the population, consider characteristics of individuals
and are able to re-
produce social reality (Martini and Trivellato 1997). They are
beneficial in predicting
both, short-term as well as long-term impact of policies (Gilbert
and Troitzsch 2005).
However, micro-simulation models are costly to build and
complex, especially at the
level of data analysis requirements. In the case of MicroSim,
the Swedish population
of approximately nine million people was modelled in many
details (Brouwers et al.
2009). In addition, in ‘simple’ cases, especially in
demographics, a micro-simulation
model produces similar results as a system dynamics-based
model (Gilbert and
Troitzsch, 2005). This proved true in the case of MicroSim and
VirSim: The lat-
ter confirmed the results of the former, although with a greater
difference between
vaccination and non-vaccination results (The National Board of
Health and Wel-
fare 2011). According to Spielauer, micro-simulation is best to
use when population
heterogeneity matters; when there are too many possible
combinations to split the
[email protected]

population into a manageable number of groups; in situations
when the micro level
explains complex macro-behaviours, or when individual history
is important for the
model’s outcomes (Spielauer 2011).
Although agent-based models lack clear predictive possibilities,
they are consid-
ered a highly valuable tool for describing and explaining
complex social interactions
and behaviours, contributing to the understanding of real-world
social systems and to
a better management of different social processes. Schindler
argues that agent-based
simulations are capable of representing real-world systems,
where small changes in
parameter values induce big changes in the model’s outputs.
This property shifts
attention from the importance of predictions of the system’s
future behaviour to
the management of critical (social) processes responsible for the
changes. However,
agent-based simulations alone are not sufficient to model
reality. Another possi-
ble problem is a high degree of freedom in modelling agents,
which amplifies the
importance of a proper validation of a simulation model
(Schindler 2013).
While agent-based and micro-simulation models would be able
to show that an

elderly person has less infection probability, it is questionable
whether they would
be able to answer why an elderly person is less infected by
influenza. Knowing
‘why’ can help in building a successful strategy for protection
against the disease.
It might happen that hidden variables and parameters influence
this age group. For
this reason, in order to model correct probabilities for different
age groups, several
authors suggest that uncertainty models, such as (dynamic)
Bayesian models or
Markov chains could be used. In addition, if the past should be
also considered (for
example, a person has less chances to be infected now because
he/she was infected
in the recent past), then we have to use more complex
probability models, such
as the Dempster–Shafer model (Ronald and Halpern 1991,
Jameson 1996). Gilbert
and Troitzsch argue that statistical models can also be used to
predict values of
some dependent variables. However, statistical models assume
linear relationships
between parameters, which becomes a restrictive assumption in
the case of (complex)
social systems (Gilbert and Troitzsch 2005).
Comparing the three different paradigms of social and policy
modelling explored
in this chapter, the three approaches can be examined according
to the level of gran-
ularity they are focussing on, the complexity of the models, the
demand for the
amount of data needed to generate a valuable simulation model
and whether social

behaviour is modelled. Table 6.8 provides this comparison,
which is adapted from
(Gilbert and Troitzsch 2005). Micro-simulation models
represent particular ontolo-
gies of the population or its representative subset based on
individuals and are most
demanding regarding data needed for developing a model.
Agent-based models are
less data demanding, less complex and well suited for
representing groups of actors
(which can represent individuals, groups as well as a system as
a whole) and their
social behaviour. ABM is the only one of the three paradigms
studied which models
social behaviour. However, social behaviour cannot be the only
source for policy-
making (Gilbert and Troitzsch 2005) Macro-models, in this
chapter represented by
system dynamics, are the least demanding—they model a
situation at a global level
and require the least data. Nevertheless, they are better for the
analysis of short-term
policy impacts than for longer-term perspectives (Astolfi et al.
2012).
[email protected]
Table 6.8 Comparison of simulation modelling theories along
level of granularity, the complexity
of the models, the amount of data needed to generate simulation
models, and the modelling of social

behaviour of agents
Simulation
paradigm
Granularity Complexity Data needed Behavioural
System dynamics Macro-focusing on the
system as a whole
Low Aggregated data No
Micro-simulation Micro-focusing on
individuals
High High amount at
individual level
No
Agent-based
modelling
Micro-macro—
focusing on interaction
of agents (which can
be individuals as well
as a system)
Medium-high Low to moderate
(depending on
the number of
agents and the
policy context)
Yes

The analysis of three different modelling paradigms with the
comparison of five
different simulation models has shown that each of the
modelling approaches has
strengths and weaknesses that constrain their usage in policy-
making. For example,
micro-simulation can be used for representing social structures
while ABM examines
interactions between the agents. Astolfi et al argue that none of
the theories alone is
able to address complex policy interactions (Astolfi et al.
2012). In consequence, a
necessary step in the development of simulation modelling is to
build and explore
ways of maintaining complex simulation models consisting of a
few sub-models
built on different modelling theories, which communicate with
each other by set-
ting up and propagating particular parameters after each
reasoning iteration (Astolfi
et al. 2012). These hybrid models can be considered as
modelling platforms or com-
plex systems consisting of sub-models. Yet, it is necessary to
study methodologies
and possibilities of combining different modelling paradigms in
order to provide
reliable simulation platforms. Current research indicates this
trend, as an example
of micro-macro combination in a Chronic Disease Prevention
Model developed in
Australia shows (Brown et al. 2009). However, more research is
needed to better
understand the implications of combined modelling paradigms,
to develop innova-
tive simulation platforms that support easy adjustment and

development of different
models based on different modelling paradigms and to bring
evangelists of particular
modelling paradigms closer to each other to support mutual
understanding and the
exploration of the added value and benefits of particular
simulation models. Further
recommendations and indications of research needs include, but
are not exhaustively
listed:
• Providing guidelines for how to best choose and arrange a
collection of smaller
(sub) models each describing certain aspects of a given domain
of modelling;
• Finding the junction points of those models of distinct
modelling paradigms with
each other by defining input and output parameters for each of
the sub-models;
• Developing meta-models that reflect the combinatory use of
distinct modelling
approaches;
[email protected]
• Determining the workflow of a simulation process by means
of, e.g. a sequence
and timing of exchanging the input and output parameters
between sub-models
in a combined hybrid meta-model;

• Exploring more extensive engagement of stakeholders in the
policy development9;
• Developing more comprehensive simulation platforms that
enable the combina-
tion of different simulation paradigms in an easy way.
6.5 Conclusions
In this chapter, we have examined and compared five different
simulation mod-
els, which were built on three different modelling paradigms:
system dynamics,
micro-simulation, and ABM. The chapter first provided an
overview of the main char-
acteristics of each of the modelling paradigms and then
described the five simulation
models by outlining them according to a framework elaborated
by eGovPoliNet for
comparative analysis of knowledge assets. The simulation
models are each suitable
for representing different aspects of socio-political and/or
socio-economic phenom-
ena, such as demographic processes (education, social contacts,
spread of diseases,
etc.), innovation processes or natural resource consumptions
(e.g. energy consump-
tion). The comparison has revealed the major differences as
well as added value and
limitations of the different approaches and simulation models.
Some lessons from the
comparative analysis are that the main strengths of using
simulation models in policy-
making are the possibilities of exploring and creating
understanding of real-world

systems and relationships, of experimenting with new situations
and of forecasting
outputs of alternative policy options or situations based on the
given values of pa-
rameters. Another key added value of simulation models in
policy-making is that
simulation models enable the exploration of social processes to
evaluate potential
impacts of alternative policy options on real-world situations
and thus to identify the
most suitable policy option.
Current paradigms of policy modelling using simulation models
are however
constrained by their particular focus. Yet, our real-world
systems and social processes
are complex and require the consideration of parameters at
different levels: macro-
level, micro-level as well as social behaviour and
interconnections between actors.
Accordingly, applying one singular approach to modelling a
real-world problem is
constrained by the particular modelling approach it focuses on:
A simulation model
of system dynamics may therefore lack precision and social
interactions because the
missing factors are not accounted for. While the demand for
meeting the appropriate
level of details included in a model’s description, being not too
complex and also
not too simple, determines the success of a simulation model,
there is a rising need
for integrating and combining different modelling paradigms to
accommodate the
diverse aspects to be considered in complex social world policy
contexts. Unifying

9 A more detailed discussion of stakeholder engagement in
policy making is given in (Helbig et al.
2014).
[email protected]
different modelling theories under an umbrella of
comprehensive policy modelling
platforms is a research need identified in this chapter. Such
research should put
forward a meta-model of how individual simulation paradigms
can be combined, and
suggestions of ‘clever’junctions of individual smaller (and self-
contained) simulation
models dedicated to individual aspects to be modelled.
While this chapter selected three widely used simulation
paradigms for the study, it
does not claim to be exhaustive nor comprehensive. Further
research is needed to ex-
tend the study to involve other important modelling approaches
such as theory-based
macro-economic forecasting for instance Dynamic Stochastic
General Equilibrium
(DSGE) modelling. DSGE is exemplified by the Global
Economy Model (GEM)
which provides support in policy analysis to central banks and
the International
Monetary Fund (IMF) (Bayoumi 2004). This will further add to
understanding the

scope and limitations of different modelling paradigms, as for
example Farmer and
Foley argue, too, that instead of DSGE models, agent-based
models should be used
to model the world economy (Farmer and Foley 2009). Thus, the
authors of this
chapter recognise the need to continue comparative analysis as
carried out in this
contribution and to expand the research to incorporate further
modelling paradigms
as well as other public policy domains. Insights gained will help
build up better
hybrid models of social simulation paradigms that are better
able to cope with the
complexity of our social and dynamic world systems and that
are more reliable as
they are covering the various social, policy and economic
aspects at various levels
of abstraction and giving consideration in a more
comprehensive way. Accordingly,
better social simulation modelling platforms will emerge.
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Chapter 7
A Comparative Analysis of Tools and
Technologies for Policy Making

Eleni Kamateri, Eleni Panopoulou, Efthimios Tambouris,
Konstantinos Tarabanis, Adegboyega Ojo, Deirdre Lee and
David Price
Abstract Latest advancements in information and
communication technologies of-
fer great opportunities for modernising policy making, i.e.
increasing its efficiency,
bringing it closer to all relevant actors, and enhancing its
transparency and acceptance
levels. In this context, this chapter aims to present, analyse, and
discuss emerging
information and communication technologies (ICT) tools and
technologies present-
ing the potential to enhance policy making. The methodological
approach includes
the searching and identification of relevant tools and
technologies, their system-
atic analysis and categorisation, and finally a discussion of
potential usage and
recommendations for enhancing policy making.
E. Kamateri (�) · E. Panopoulou · E. Tambouris · K. Tarabanis
Information Technologies Institute, Centre for Research &
Technology—Hellas,
Thessaloniki, Greece
E. Panopoulou
E. Tambouris · K. Tarabanis
University of Macedonia, Thessaloniki, Greece
e-mail: [email protected], [email protected]
K. Tarabanis
e-mail: [email protected], [email protected]
A. Ojo · D. Lee

INSIGHT Centre for Data Analytics, NUIG, Galway, Ireland
D. Lee
D. Price
Thoughtgraph Ltd, Somerset, UK
10.1007/978-3-319-12784-2_7
[email protected]
126 E. Kamateri et al.
7.1 Introduction
Policy making may be defined as “the process by which
governments translate their
political vision into programmes and actions to deliver
‘outcomes’ desired changes
in the real world” (UK Government 1999). Policy making
encompasses any activity
relevant to discussing political issues, identifying areas of
improvement or solutions,
creating and implementing laws and regulations, monitoring and
evaluating current
policies, etc.
Policy making is a multidisciplinary scientific field referring
mainly to politi-
cal science, but it may also refer to social, economics, statistics,
information, and

computer sciences. These diverse scientific fields are essential
in order to perform
policy making in a more effective and informed manner.
Information and communi-
cation technologies (ICTs), specifically, have supported
decision-making processes
for many years. However, the current ICT advancements and
good practices of-
fer even greater opportunities for modernising policy making,
i.e. increasing its
efficiency, bringing it closer to all relevant actors and
increasing participation, facil-
itating its internal processes (e.g. decision making), and
enhancing its transparency
and acceptance levels.
In this context, this chapter aims to present, analyse, and
discuss emerging ICT
tools and technologies presenting the potential to enhance
policy making. Our ap-
proach includes searching and identification of relevant tools
and technologies, their
systematic analysis and categorisation, and finally a discussion
of potential usage
and recommendations for enhancing policy making. The chapter
is structured in the
following way: Sect. 7.2 describes our methodological
approach, Sect. 7.3 provides
the comparative analysis, and Sect. 7.4 discusses the findings
and concludes the
chapter.
Before proceeding to the rest of the chapter, we should provide
further clarifica-
tions with regard to its scope. First, for work presented in this
chapter, policy making

is considered as a broad and continuous process that commences
from the need to
create a policy and ends when a policy is abandoned or
replaced. In this context, the
policy-making process is usually described with a circular-
staged model called “the
policy cycle”. There are differences in the number, names, and
boundaries of the
stages adopted in each proposed policy cycle (e.g. Jann and
Wegrich 2006; Northern
Ireland Government 2013); however, every policy cycle
includes an initiation stage,
a drafting stage, an implementation stage, and an evaluation
stage. The scope of our
work refers to all these stages of the policy cycle.
Second, we consider all stakeholders relevant to policy making
within the scope
of work presented in this chapter. Obviously, the main actor
involved in policy
making is the government with its different roles, bodies, and
institutions. However,
noninstitutional actors are also involved such as political
parties, political consultants
and lobbyists, the media, nongovernmental organisations, civil
organisations, and
other interested parties depending also on the policy topic at
hand. Last but not least,
individual citizens are also actors of policy making; as the final
policy recipients
and beneficiaries, they should actively participate in policy
making. Hence, in this
[email protected]

7 A Comparative Analysis of Tools and Technologies for Policy
Making 127
chapter, we do not consider policy making as a close, internal
government process,
but rather as an open, deliberative process relevant to the whole
society.
7.2 Methodology
In order to analyse the existing ICT tools and technologies that
can be used to enhance
the policy-making process, we adopted a simple methodology
consisting of four main
steps.
Before introducing the adopted methodology, we provide a short
description with
regard to the difference between ICT tools and technologies.
ICT tools normally in-
clude software applications, web-based environments, and
devices that facilitate the
way we work, communicate, and solve problems. These are
developed by individual
software developers, big software providers, researchers, and
scientists (Phang and
Kankanhalli 2008). Technology, on the other hand, refers to
knowledge and know-
how, skills, processes, tools and/or practices.1 Therefore,
technology not only refers
to tools but also the way we employ them to build new things.
In the current survey,
we organise the findings of our literature analysis based on tool
categories.

Step 1: Identification During this step, we surveyed the current
state of the art to
identify ICT tools and technologies that have been (or have a
clear potential to be)
used to reinforce the policy-making process. These tools have
been collected mainly
from project deliverables, posts, electronic articles, conference
papers, scientific
journals, and own contacts and expertise.
In particular, we searched for tools and technologies that have
been highlighted,
used, or created by existing research and coordination projects
in the area of
e-government and policy modelling, i.e. CROSSOVER2, e-
Policy3, FuturICT4,
OCOPOMO5, COCKPIT6 and UbiPol7, OurSpace8,
PuzzledbyPolicy9, etc. This
investigation resulted in a collection of more than 30 ICT tools
and technolo-
gies mainly coming from project deliverables, posts, electronic
articles, conference
papers, scientific journals, and own contacts and expertise.
Thereafter, we expanded our research on the web to include
additional tools that
were not previously identified. To this end, we tried multiple
searches in the major
research databases of computer science, e.g. Association for
Computing Machin-
ery (ACM) Digital Library and Google Scholar using a
combination of different
1 http://en.wikipedia.org/wiki/Technology.
2 http://crossover-project.eu.
3 http://www.epolicy-project.eu/node.

4 http://www.futurict.eu.
5 http://www.ocopomo.eu.
6 http://www.cockpit-project.eu.
7 http://www.ubipol.eu/.
8 http://www.ep-ourspace.eu/.
9 http://www.puzzledbypolicy.eu/.
[email protected]
keywords such as tools, technologies, policy modelling, online
participation, en-
gagement, government, policy making, decision making, policy
formulation, etc.
The references of the selected papers were checked and
additional papers were
found. Some of the journals that have been reviewed include
Government Infor-
mation Quarterly, International Journal of Electronic
Government Research. In
addition, we surveyed similar initiatives that summarise tools
or/and methods, i.e.
the Participation Compass10 launched by Involve11 (not-for-
profit organisation in
public participation), the ParticipateDB12 by Intellitics13, and
the ReformCompass
by Bertelsmann Stiftung14 (providers of digital engagement
solutions). The final
result of this exercise was a list of 75 tools and technologies.
Step 2: Categorisation Analysing the identified tools and
technologies, it was ev-
ident that most of them fall under a number of categories. We

defined, therefore,
11 categories of tools and technologies for policy making. Each
category has a spe-
cific application focus, e.g. opinion mining, serious games, etc.,
and may be further
divided into one or more subcategories.
We then organised tools and technologies’ analysis according to
the defined cate-
gories. There are few cases, however, where the same tool could
be classified under
more than one category, i.e. in the case of visualisation and
argumentation tools and
in the case of serious games and simulation tools. In the first
case, argumentation
tools represent and structure arguments and debates, and usually
exploit visual means
in order to clearly represent the arguments. However, the main
focus remains the
representation of arguments. On the other hand, the
visualisation tools present, in a
graphical form, any type of input data. Thus, it was selected for
the sake of simplicity
to analyse each tool in one category according to its most
prominent feature. Similar
difficulties in categorisation have also arisen in the case of
simulation tools and seri-
ous games. Serious games are created for educational and
entertainment purposes, or
for helping citizens to further understand some processes by
playing the role of a key
stakeholder. On the other hand, simulation tools are usually
created on a more serious
context (e.g. within a research project, taking into account
accurate real-world data)
in order to help real policy makers or governments to simulate

long-term impacts of
their actions. Therefore, the categorisation of tools in these two
categories was made
based on the context and the goal of the tool.
Step 3: Comparative Analysis A comparative analysis of
identified ICT tools and
technologies per category was then performed. Initially, we
analysed tools’ function-
ality to identify core capabilities per category. Then, we
examined the key features
for each tool. The outcome of this analysis is a comparative
table for each category
10 http://participationcompass.org/.
11 http://www.involve.org.uk.
12 http://participatedb.com/.
13 http://www.intellitics.com/.
14 http://www.reformkompass.de.
[email protected]
Making 129
that shows, at a glance, an overview of different features found
in each tool of the
category.
Step 4: Conceptualisation During this step, we performed an
overall discussion
of the presented tools and technologies and their potential for
enhancing policy
making. To this end, we examined three main aspects for policy

making—the type
of facilitated activities, the type of targeted stakeholders, and
the stages of the policy
cycle. Finally, we drafted overall recommendations and
conclusions.
7.3 Tools and Technologies for Policy Making
Based on the literature survey, we identified 11 main categories
of ICT tools and
technologies that can be used for policy making purposes as
follows:
• Visualisation tools help users better understand data and
provide a more
meaningful view in context, especially by presenting data in a
graphical form.
• Argumentation tools visualise the structure of complex
argumentations and
debates as a graphical network.
• eParticipation tools support the active engagement of citizens
in social and
political processes including, e.g. voting advice applications
and deliberation
tools.
• Opinion mining tools help analyse and make sense of
thousands of public
comments written in different application contexts.
• Simulation tools represent a real-world system or phenomenon
and help users
understand the system and the effects of potential actions in
order to make better

decisions.
• Serious games train users through simulation and virtual
environments.
• Tools specifically developed for policy makers have been
recently developed to
facilitate the design and delivery of policies.
• Persuasive tools aim to change users’ attitudes or behaviours.
• Social network analysis (SNA) tools analyse social
connections and identify
patterns that can be used to predict users’ behaviour.
• Big data analytics tools support the entire big data
exploitation process from
discovering and preparing data sources, to integration,
visualisation, analysis,
and prediction.
• Semantics and linked data tools enable large amounts of data
to become easily
published, linked to other external datasets, and analysed.
We present an analysis of each category of tools and
technologies in the rest of this
section.15
15 All tools mentioned in this section are summarized in the end
of the chapter along with their
links.
[email protected]

7.3.1 Visualisation Tools
Visualisation tools enable large amounts of “raw” data to
become visually represented
in an interpretable form. Moreover, they provide appropriate
means to uncover pat-
terns, relationships, and observations that would not be apparent
from looking at it
in a nonvisual format. Therefore, users can explore, analyse,
and make sense of data
that, otherwise, may be of limited value (Osimo and Mureddu
2012). Today, there are
many data visualisation tools, desktop- or web-based, free or
proprietary, that can be
used to visualise and analyse raw data provided by the user.
Examples include Google
Charts, Visokio Omniscope, R, and Visualize Free. Besides
visual presentation and
exploration of raw data, they provide additional features such as
data annotation (e.g.
Visokio Omniscope), data handling, and other statistical
computations on raw data
(e.g. R).
Over recent years, geovisualisation (shortened form of the term
geographic vi-
sualisation) has gained considerable momentum within the
fields of geographic
information systems (GIS), cartography, and spatial statistics.
Some consider it to
be a branch of data visualisation (Chang 2010). However,
geovisualisation inte-
grates different approaches including data visualisation, such as
cartography, GIS,

image analysis, exploratory data analysis, and dynamic
animations, to provide visual
exploration, analysis, synthesis, and presentation of geospatial
data (MacEachren
and Kraak 2001). Geovisualisation tools have been widely used
to visualise societal
statistics in combination with geographic data.
Several visualisation and geovisualisation tools have been
developed to visualise
and analyse demographic and social statistics in several
countries across the world.
Most tools are used for data coming from the USA. However,
many efforts have been
made, lately, to visualise statistics coming from all over the
world (e.g. Google Public
Data Explorer and World Bank eAtlas). The most important
source of information
for these tools is governmental reports which are made available
by each state.
Most tools support data transparency, mainly for downloading
data and figures,
while uploading of users’ data is available only in few cases.
Visualisation tools are
organised into static and interactive based on a categorisation
proposed for web-
mapping tools (Kraak and Brown 2001). A static tool contains a
figure or a map
displayed as a static image (Mitchell 2005), while interactive
tools allow users to
access a set of functions to have some interaction with the tool
or the map, such as
zooming in and out (Mitchell 2005). Table 7.1 summarises well-
known visualisation
and geovisualisation tools and compares their main
characteristics. In particular,

the table provides information on: (a) the number and subject of
indicators, e.g. if
they deal with demographic, health, environmental, or other
social issues, (b) the
coverage, namely, the countries supported, (c) the period for
which statistics are
available, (d) data transparency, and (e) whether it is a static or
interactive tool.
[email protected]
Making 131
Table 7.1 Visualisation and geovisualisation tools for analyzing
regional statistics
Indicators and
Topic
Coverage
(countries)
Period Data
transparency
Static/
Interactive
Gapminder > 400
Demographics,
social,
economic, en-
vironmental,

health
> 200 Over the past
200 years
Download
and upload
Interactive
Worldmapper ∼ 696 maps
Demographics
All N/A Download
(No custom
maps)
Datasets,
static
Dynamic
Choropleth
Maps
Multiple
social,
economic,
and environ-
mental
USA N/A Download
(free to adjust
the threshold
criteria)
Interactive

DataPlace ∼ 2360
Demographics,
health, arts,
real estate
USA After 1990 N/A Interactive
Data
Visualizer-
World
Bank
∼ 49
Social,
economic,
financial, IT,
and environ-
mental
209 1960–2007 N/A Interactive
World Bank
eAtlas
∼ 175
Development
challenges
200 After 1960 Download
and upload
Interactive
State
Cancer

Profiles
Demographic
data related to
cancer
USA 2006–2010 N/A Interactive
Health
Infoscape
Health
conditions
USA January 2005–
July 2010
N/A Interactive
OECD
eXplorer
∼ 40
Demographics,
economic,
labour
market,
environment,
social, and
innovation
34
(335 large
regions
1679 small
regions)

1990–2005 Download
and upload
Interactive
(time
animation)
storytelling
Other tools investigated, but not included, in the above table
include STATcompiler, Google
Public Data Explorer, NComVA, Social Explorer (USA),
PolicyMap (USA), All-Island Research
Observatory (UK), and China Geo-Explorer II
[email protected]
Demographic, social, environmental, health, and other public
data, provided by
governmental and public authorities, in raw form, can be
transformed and presented
through visualisation and geovisualisation tools into a more
interpretable way. Thus,
information and current trends hidden in this data can easily
become apparent. This
can assist policy stakeholders and decision makers to make
more informed decisions.
In addition, incorporating geographical knowledge into planning
and formation of
social and political policies can help derive more accurate
spatial decisions. Obvious
fields where visualisation and geovisualisation tools can be

applied for policy making
are investment, population, housing, environmental assessment,
public health, etc.
7.3.2 Argumentation Tools
Argumentation tools visualise the structure of complex
arguments and debates as a
graphical network. In particular, they allow a large number of
stakeholders to partici-
pate, discuss, and contribute creative arguments and suggestions
which then become
visualised. This visual representation provides a better and
deeper understanding of
topics discussed. Thus, complex debates can become easily
analysed, refined, or
evaluated, e.g. by pinpointing possible gaps and inconsistencies
or strong and weak
points in the arguments, etc. (Benn and Macintosh 2011).
Table 7.2 summarises well-known argumentation tools and
depicts their main
characteristics (i.e. whether they are open source, whether they
enable import-
ing/exporting data, whether they are Web-based or
collaborative, the argument
framework, whether they support visual representation
argumentation structure mod-
ification and manipulation of layouts). DebateGraph, Rationale,
Cope It!, and
bCisive constitute proprietary solutions, while Cohere,
Araucaria, Compendium,
and Carneades were developed during research studies within
universities and re-
search projects. Most argumentation tools enable users to share
ideas and collaborate

upon “wicked problems”. For example, DebateGraph allows
users to collaboratively
modify the structure and the content of debate maps in the same
way they can
collaboratively edit a wiki. In addition, MindMeister and
Compendium constitute
desktop-based solutions that support collaborative argument
analysis, while Mind-
Meister and bCisive also enable real-time collaboration. Though
most argumentation
tools provide, even partially, a visual representation of
discussions, only few sup-
port an easy layout manipulation; such tools are Compendium,
Araucaria, Cohere,
and DebateGraph. Besides argument analysis, argumentation
tools offer additional
features, such as argument reconstruction, discussion forums,
argument evaluation,
etc. For example, Araucaria and Argunet enable users to
reconstruct and map de-
bates, Cohere enables any content on the web to serve as a node
of information
in the argument map, and Rationale allows users to judge the
strength of an argu-
ment by evaluating its elements. These judgments are also
represented on the map.
Similarly, Carneades allows users to evaluate and compare
arguments as well as to
apply proof standards. Finally, Cope It! supports a threaded
discussion forum, while
[email protected]

Making 133
Table 7.2 Argumentation tools. (Source: Benn and Macintosh
2011)
Tool Open
source
Import/
export
Web-
based
Collab-
orative
Argument
frame-
work
Visual
represen-
tation
Modify
argument
structure
Manipulate
layouts
Araucaria Yes Yes No No Walton,
Toulmin,
Wigmore,
Classical

Partially Yes Partially
Argunet Yes Yes Yes Yes Classical Yes Partially N/A
Carneades Yes Yes Yes No Walton Partially Yes N/A
Cohere Yes Yes Yes Yes IBIS Yes Partially Partially
Compendium Yes Yes No Yes IBIS Yes Partially Partially
Cope_it! N/A No Yes Yes IBIS Yes Partially N/A
DebateGraph No No Yes Yes Multiple
(including
IBIS)
Partially Partially Partially
Rationale No No No No Classical Partially Partially N/A
bCisive No No Yes Yes IBIS Partially Partially N/A
MindMeister No Yes Yes Yes N/A Yes Yes Partially
IBIS Issue-Based Information System
bCisive incorporates group planning, decision making, and team
problem-solving
capabilities.
Argumentation tools facilitate better-informed public debate,
policy deliberation,
and dialogue mapping on the web about complex political
issues. For example,
DebateGraph has been used by the Dutch Foreign Ministry in its

recent consultation
on its human rights policy16, the UK Prime Minister’s
Office17, and the White
House’s Open Government Brainstorming.18 Compendium has
been used in a case
study for consultation on regional planning in southeast
Queensland (Ohl 2008).
Carneades has been developed during the European Estrella
project19 that aims to
help both citizens and government officials to take part, more
effectively, in dialogues
for assessing claims and has been used in several applications.
16 http://debategraph.org/MR
17 http://debategraph.org/No10.
18 http://debategraph.org/WH.
19 http://www.estrellaproject.org/.
[email protected]
7.3.3 eParticipation Tools
eParticipation tools have been specifically developed to involve
citizens in the policy-
making process, i.e. to enable citizens to get informed, to
provide feedback on
different policy issues, and to get actively involved in decision
making (Gramberger
2001). These tools are mainly based on Web 2.0 features
including a variety of social
networking tools such as discussion forums or message boards,
wikis, electronic

surveys or polls, e-petitions, online focus groups, and
webcasting.
eParticipation may entail different types of involvement, which
are supported
by different tools and functionalities, ranging from the
provision of information,
to deliberation, community building and collaboration, active
involvement through
consultations, polling, and decision making. The International
Association for Public
Participation (IAP2) has produced a public participation
spectrum20, which shows
how various techniques may be employed to increase the level
of public impact.
Recently, eParticipation tools have been widely used by
governmental and public
authorities. Through actively engaging citizens, in the planning,
design, and de-
livery process of public policies, they have moved towards
improving democratic
governance, preventing conflicts, and facilitating citizens’
active participation in
the solution of issues affecting their lives. Table 7.3 presents a
set of such recently
developed eParticipation tools.
7.3.4 Opinion Mining Tools
The Web’s widespread use over the past decade has
significantly increased the pos-
sibility for users to express their opinion. The users not only
can post text messages
now but also can see what other users have written about the
same subject in a variety

of communication channels across the Web. Moreover, with the
advent of Twitter
and Facebook, status updates, and posts about any subject have
become the new
norm in social networking. This user-generated content usually
contains relevant
information on the general sentiment of users concerning
different topics including
persons, products, institutions, or even governmental policies.
Thus, an invaluable,
yet scattered, source of public opinion has quickly become
available.
Opinion-mining tools (or otherwise called sentiment analysis
tools) perform a
computational study of large quantities of textual contributions
in order to gather,
identify, extract, and determine the attitude expressed in them.
This attitude may be
users’ judgment or evaluation, their affectual state (that is to
say, the emotional state
of the author when writing), or the intended emotional
communication (that is to say,
the emotional effect the author wishes to have on the reader;
Stylios et al. 2010).
20 Available at:
http://www.iap2.org/associations/4748/files/spectrum.pdf.
[email protected]
Making 135

Table 7.3 eParticipation tools developed to improve people
involvement in government
Typical actions Examples
Citizen Space Consultation and engagement software
Create, organise, and publish public
consultations across the net on complex
policy documents
Share consultation data openly in a
structured way
Provide a way to easily analyse consultation
data (both qualitative and quantitative)
Used by government bodies
to run e-consultations
around the world
Adhocracy.de Participation and voting software
Present and discuss issues
Collaborate (develop and work on texts
together)
Make proposals, gather, and evaluate
proposals
Add polls for decision making
Vote on issues
Used in the Munich Open
Government Day where
citizens could propose
policies, projects, and
actions of the city
MixedInk.com Collaborative writing software
Large groups of people work together to
write texts that express collective opinions

Post ideas
Combine ideas to make new versions
Post comments and rate versions to bring the
best ideas to the top
Used by the White House to
let citizens draft collective
policy recommendations for
the Open Government
Directive
Loomio.org Decision making and collaborative software
Initiate discussions and present proposals
that can then be discussed, modified, and
voted (Agree, Abstain, Disagree, or Block,
along with a brief explanation of why)
Change their position any time
Used by the Wellington City
Council for discussion with
their citizens
CitySourced Mobile civil engagement platform
Identify and report civic issues (graffiti,
trash, potholes, etc.), and comment on
existing ones
Used in San Francisco, Los
Angeles, and several other
cities in California
Puzzledbypolicy Consultation and opinion mapping software
Learn about policy issues concerning
immigration in the European Union (EU)
Give their voice
Graphically compare their views on

immigration with national and EU
immigration policies as well as with the
opinions of relevant stakeholders
Encourage to join discussions on particular
aspects of immigration policy they feel
strongly about
Used by the Athens and
Torino municipalities and
other stakeholders in
Tenerife, Hungary, and
Slovenia
[email protected]
Typical actions Examples
Opinion Space Opinion mapping software
Collect and visualise user opinions on
important issues and policies (rate five
propositions on the chosen topic and type
initial response to a discussion question)
Show in a graphical “map” where user’s
opinions fall next to the opinions of other
participants
Display patterns, trends, and insights
Employ the wisdom of crowds to identify the
most insightful ideas
Used by US State

Department to engage
global online audiences on a
variety of foreign policy
issues
CivicEvolution.org Collaboration platform
Engage citizens in structured dialogue and
deliberation and develop detailed
community-written proposals to make
constructive changes
Used by the City of Greater
Geraldton, in Australia, to
facilitate collaboration and
deliberation among
participants in participatory
budgeting community
panels
UbiPol Mobile civil engagement platform
Identify and report problems or suggestions
Report policy issues
Used by TURKSAT, a
publicly owned but privately
operated company in
affiliation with Ministry of
Transportation in Turkey
OurSpace Youth eParticipation platform
Engage young people in the decision-making
process
Enable collaboration
European and National
Youth Organisations already

using OurSpace
Dialogue App Set up a dialogue
Share, rate, comment, and discuss ideas and
bring the best ideas to the top
Department for
Environment, Food and
Rural Affairs in the UK is
using Dialogue App to get
thoughts, ideas and input on
how to improve and
formulate policy
In social media, opinion mining usually refers to the extraction
of sentiments from
unstructured text. The recognised sentiments are classified as
positive, negative, and
neutral, or of a more fine-grained sentiment classification
scheme. Examples include
Sentiment140, Sentimentor, Repustate, etc. Opinion-mining
tools may also integrate
a broad area of approaches including natural language
processing, computational
linguistics, and text mining. Text mining, for example, can
provide a deeper analysis
of contributions; it summarises contributions, helps highlight
areas of agreement and
disagreement, and identifies participants’ main concerns—the
level of support for
draft proposals or suggestions for action that seem necessary to
address. Opinion
[email protected]

Making 137
mining tools providing such approaches include DiscoverText,
RapidMiner, and
Weka.
Classifying statements is a common problem in opinion mining,
and different
techniques have been used to address this problem. These
techniques follow two
main approaches; those based on lexical resources and neutral-
language processing
(lexicon-based) and those employing machine-learning
algorithms. Lexicon-based
approaches rely on a sentiment lexicon—a collection of known
and precompiled
sentiment/opinion terms. These terms are words that are
commonly used to ex-
press positive or negative sentiments, e.g. “excellent”, “great”,
“poor”, and “bad”.
The method basically counts the number of positive and
negative terms, and de-
cides accordingly the final sentiment. Machine-learning
approaches that make use
of syntactic and/or linguistic features and hybrid techniques are
very common, with
sentiment lexicons playing a key role in the majority of
methods.
Table 7.4 presents several opinion mining tools that have been
recently developed
to analyse public opinions.
Opinion mining tools can help derive different inferences on

quality control, pub-
lic relations, reputation management, policy, strategy, etc.
Therefore, opinion mining
tools can be used to assist policy stakeholders and decision
makers in making more in-
formed decisions. In particular, knowing citizens’ opinion about
public and political
issues, proposed government actions, and interventions or
policies under formation
can ensure more socially acceptable policies and decisions.
Finally, gathering and
analysing public opinion can enable us to understand how a
certain community re-
acts to certain events and even try to discover patterns and
predict their reactions to
upcoming events based on their behaviour history
(Maragoudakis et al. 2011).
7.3.5 Simulation Tools
Simulation tools are based on agent-based modelling. This is a
recent technique
that is used to model and reproduce complex systems. An agent-
based system is
formed by a set of interacting and autonomous “agents” (Macal
and North 2005) that
represent humans. Agents act and interact with their
environment, including other
agents, to achieve their objectives (Onggo 2010). Agents’
behaviour is described by
a set of simple rules. However, agents may also influence each
other, learn from
their experiences, and adapt their behaviour to be better suited
to their environment.
Above all, they operate autonomously, meaning that they decide
whether or not to

perform an operation, taking into account their goals and
priorities, as well as the
known context. The analysis of interactions between agents
results at the creation
of patterns that enable visualising and understanding the system
or the phenomenon
under investigation.
[email protected]
Table 7.4 Opinion mining tools
Purpose Sources Classification
SwiftRiver Aggregate, manage,
filter, and validate
web data
Discover relationship
and trends in data
Twitter, SMS, e-mail,
and RSS feeds
Machine learning
DiscoverText (Text analytics)
Search, filter, collect,
and classify data
Generate insights
E-mail archives,
social media content,

and other document
collections
Machine learning
Repustate Categorise and
visualise social media
data
Extract text sentiment
Predict future trends
Twitter or Facebook
Multiple languages
Machine learning
Opinion observer (Opinion mining)
Discover patterns
Web pages Lexicon-based
(feature category)
AIRC Sentiment
Analyser
Extract text sentiment N/A Lexicon-based
Social Mention Aggregate and analyse
social media data
Discover patterns
Blogs, comments,
social media including
Twitter, Facebook,

Social bookmarks,
microblogging
services, Images,
News, etc.
Lexicon-based
Umigon Sentiment analysis Twitter Lexicon-based
Convey API Sentiment analysis Social media records Machine
learning
Natural-language
processing
Statistical modelling
Sentiment140 Sentiment analysis for
tweets on a subject or
keyword
Twitter Machine learning
Natural-language
processing
Sentimentor Sentiment analysis for
tweets on a subject
Twitter Machine learning
Corpora’s Applied
Linguistics
Document
summarisation and
sentiment analysis
Documents Natural-language

processing in
combination with an
extensive English
language lexicon
Attentio Sentiment analysis Blogs, news, and
discussion forum sites
Lexicon-based
Machine learning
[email protected]
Making 139
Purpose Sources Classification
Opinmind Sentiment analysis of
bloggers opinion
Blogs Not available
ThinkUp Archive and analyse
social media life
Twitter and Facebook Machine learning
In this sense, simulation tools are particularly suited to explore
the complexity of
social systems. A social system consists of a collection of
individuals who interact

directly or through their social environment. These individuals
evolve autonomously
as they are motivated by their own beliefs and personal goals,
as well as the cir-
cumstances of their social environment. Simulating social
systems and analysing the
effects of individuals’ interactions can result in the construction
of social patterns
(e.g. how society responds to a change) that can be used for
policy analysis and
planning as well as for participatory modelling (Bandini et al.
2009).
There are several general-purpose simulation tools. Most of
them are open source
and free to be accessed by anyone. Some of these are specially
designed to focus
on social systems. For example, Multi-Agent Simulation Suite
(MASS) is a soft-
ware package intended to enable modellers to simulate and
study complex social
environments. To this end, it models the individual together
with its imperfections
(e.g. limited cognitive or computational abilities), its
idiosyncrasies, and personal
interactions. Another tool focusing on the development of
flexible models for living
social agents is Repast.
An increasing number of tools for the simulation and analysis of
social inter-
actions has been developed in recent years. These aim to help
policy stakeholders
and decision makers to simulate the long-term impact of policy
decisions. Table 7.5
presents such simulation tools that have been used in the field

of health, environment,
developmental policies, etc.
7.3.6 Serious Games
Agent-based modelling is used also in serious games, providing
the opportunity for
experiential and interactive learning and exploration of large
uncertainties, divergent
values, and complex situations through an engaging, active, and
critical environment
(Raybourn et al. 2005). Serious games enable players to learn
from the accurate rep-
resentations of real-world phenomena and the contextual
information and knowledge
and data embedded in the dynamics of the game. Abt (1987)
defines serious games
as games with “an explicit and carefully thought-out
educational purpose and are not
intended to be played primarily for amusement”.
[email protected]
Table 7.5 Simulation tools simulating the long-term impact of
policy decisions
Purpose Input Interface Scale
Threshold 21 Simulate the
long-term impact of
socioeconomic
development

policies
About 800 variables
concerning
economic, social,
and environment
factors
Flexible Customisable
to suit the
needs of any
sector and
country
GLEaMviz Simulate global
epidemics
Detailed
population,
mobility, and
epidemic–infection
data (real-world
data)
Compartmentalised
disease models
Visual tool
for designing
compartmen-
tal
models
Thirty
countries in 5
different
continents

The Climate
Rapid
Overview and
Decision-
support
Simulator
(C-ROADS)
Simulate long-term
climate impacts of
policy scenarios to
reduce greenhouse
gas emissions (CO2
concentration,
temperature,
sea-level rise)
Sources of
historical data
Flexible
equations are
available and
easily
auditable
Six-region and
15-region
mode
UrbanSim Simulate the
possible long-term
effects of different
policies on urban
development

(land use,
transportation, and
environmental
planning)
Historical data Flexible Any country
Modelling the
Early Life
Course
(MEL-C)
Simulate the effects
of policy making in
the early life course
and issues
concerning children
and young people
Data from existing
longitudinal studies
to quantify the
underlying
determinants of
progress in the
early life course
Flexibly
adapted for
new data and
parameter
inputs
N/A
Global

Buildings
Performance
Network
(GBPN)
Policy
Comparative
Tool
An interactive tool
that enables users to
compare the
world’s best
practice policies for
new buildings
(residential and
commercial)
N/A N/A N/A
[email protected]
Making 141
Purpose Input Interface Scale
CLASP’s
Policy
Analysis
Modeling
System
(PAMS)

Forecast the
impacts of energy
efficiency standards
and labelling
programs
Assess the benefits
of policies, identify
the most attractive
targets for
appliances, and
efficiency levels
N/A N/A Support basic
modelling
inputs for
over 150
countries
Customisable
where
country-
specific data
is available
Scenario
Modelling and
Policy
Assessment
Tool
(EUREAPA)
Model the effects of
policies on
environment,
consumption,
industry, and trade

Detailed carbon,
ecological, and
water footprint
indicators
N/A N/A
Budget
simulator
Budget consultation
platform that
enables to adjust
budget items and
see the
consequences of
their allocations on
council tax and
service areas
N/A Flexible Any country
CLASP Comprehensive, Lightweight Application Security
Process, EUREAPA, MEL-C, C-ROADS
In policy making, serious games provide the opportunity for
players to assume
roles of real-world critical stakeholders whose decisions rely on
extensive data col-
lected from the world around them. In this way, players get
educated on the process
of decision making as well as on the limitations and trade-offs
involved in policy
making. Serious games may be used in fields like defence,
education, scientific ex-

ploration, health care, emergency management, city planning,
engineering, religion,
and politics (Caird-Daley et al. 2007).
Table 7.6 summarises a number of serious games aiming to
tackle different
social and political problems. In some of these, users assume
the role of critical
stakeholders. For example, in 2050 Pathways, users play as if
they were the Energy
and Climate Change Minister of the UK, while, in Democracy,
users act as the
president or the prime minister of a modern country. Other
games enable users to
apply policies/strategies and explore their potential impact.
Such an example is the
Maryland Budget Map Game that gives the option to make cost-
cutting decisions
and consider short-term and long-term budget effects. Serious
games also help users
gain virtual experiences for solving real-world problems. Thus,
such games could be
used to train citizens and public authorities on how to enforce a
policy, e.g. a disas-
ter or crisis management policy. For instance, Breakaway
simulates critical incidents
[email protected]
Table 7.6 Serious games focusing on policy making
Purpose Features Scope

2050 Pathways Users act as the energy and
climate change minister
and explore the complex
choices and trade-offs
which the UK will have to
make to reach the 80 %
emission reduction targets
by 2050, while matching
energy demand and supply
It covers all parts of
the economy and all
greenhouse gas
emissions
Users create their
emission reduction
pathway, and see the
impact using real
scientific data
Scientific
exploration and
engineering
Democracy Users are in the position of
president (or prime
minister) of a modern
country and the objective
of the game is to stay in
power as long as possible
It recreates a modern
political system as
accurately as possible
Users influence the

voters and the
country by putting in
place policies
Education,
political strategy
Maryland Budget
Map Game
Users act as the
administration and general
assembly of a state
Gives the options to make
cost-cutting decisions,
weigh revenue options,
and consider short-term
and long-term budget
effects
It explains how
budgeting decisions
are made
Education,
political strategy
NationStates—
create your own
country
Users build a nation and
run it according to their
political ideals and care
for people

N/A Entertainment,
education
Breakaway(disaster
management—
incident
commander)
Helps incident
commanders and other
public safety personnel
train and plan for how they
might respond to a wide
range of critical incidents
It models acts of
terrorism, school
hostage situations,
and natural disasters
Education,
emergency
management
The Social
Simulator
Trains communications,
policy, and frontline staff
in a variety of sectors
using a number of crisis
scenarios
Users use the language,
tools, and norms of the
social web for crisis
response

It models terrorist
attacks, a leaked
report spreads anger
about a government
policy, etc.
Education,
emergency
management,
political strategy
[email protected]
Making 143
CItyOne Users are poised with a
series of problems
concerning energy, water,
or commercial
investments (such as
banking and payment
systems) and are asked to
address specific challenges
Planner players think
through the sorts of
energy, water, or
commercial

investments that
might be needed for
particular urban
environments in the
years to come
Education,
awareness
World Without
Oil
Engages people concerned
with the world’s
dependence on oil and
both educate and move
them to action and
contribute “collective
imagination”
Risks that oil
extraction poses to
our economy,
climate, and quality
of life
Awareness, public
good
Urgent Evoke Empowers people all over
the world to come up with
creative solutions to the
most urgent social
problems
N/A Awareness

MP For A Week Enables users to learn
about the work of a
member of parliament
(MP) and key features of
democracy in the UK
N/A Education
Budget Hero Allows players to build a
balanced budget
Creates and tests a
budget policy and
sees the effects of
those cuts or
increased expenses
on the federal budget
Education, political
strategy
and risk scenarios and helps players train and plan their
responses. Last, they improve
imaginary thinking by exploring possible futures and sparking
future-changing ac-
tions. For example, Urgent Evoke invites people to come up
with creative solutions
to the most urgent social problems. Other games focusing on a
better world can be
found in World-Changing Game21 and Purposeful Games22;
however, they are not
included in Table 7.6 due to their loose connection with the
policy-making process.
21 http://www.scoop.it/t/world-changing-games.

22 http://purposefulgames.info/.
[email protected]
Table 7.7 Political analysis tools
PolicyMaker Helps users to analyse,
understand, and create
effective strategies to
promote point of view on
any policy question or
political issue
Conduct a stakeholder
analysis
Identify political dynamics
of policy making
Analyse systematically the
supporters, why a policy may
face opposition, and what
strategies might help it be
more effective
Design political strategies to
support a policy
Policy
planning
Oracle Policy
Automation

for Social
Services
Transform complex policies
in human language
Assess impact of policy
changes by enabling what-if
analysis of proposed
amendments
It includes debugging,
regression testing, policy
simulation, and what-if
analysis for policy changes
Policy
delivery
7.3.7 Tools Specifically Developed for Policy Makers
Policy-making tools are designed to facilitate governments,
industry, construction
experts, and other stakeholders design and deliver national
renovation policies and
strategies. We present two illustrative examples of such tools in
Table 7.7.
7.3.8 Persuasive Tools
Persuasive tools aim to change users’ attitudes or behaviours,
such as exercising more
or sticking to medication, by enhancing feedback, persuasion
and social influence,
but not through coercion (Fogg 2002). Persuasive tools can be
applied in policy
making for promoting different political causes and enhancing

policies’ adoption by
the public. The Behavioural Insights Team has published a
paper on fraud, debt,
and error that presents a completely new way of doing policy
based on citizens’
behavioural reactions (Behavioural Insights Teem; BIT 2012).
Until recently, only indirect efforts have been made to persuade
or motivate
citizens adopting a specific policy. For example, the USA23 and
Australia24 have
developed smartphone applications that enable taxpayers to
keep up to date with
their tax affairs. In addition, the Australian Tax Office offers a
“Tax Receipt Log”
app that makes it easier to keep up to date on expenses and tax
receipts by using the
23 http://www.irs.gov/uac/New-IRS2Go-Offers-Three-More-
Features.
24 http://www.taxreceiptlog.com/blog/gst/tax-calculator/.
[email protected]
Making 145
phone camera to take a photo of a receipt, which is then
processed and stored. These
can serve as persuasive tools reminding citizens of their delayed
fees or motivating
them to ask for receipts for their purchases.25
7.3.9 Social Network Analysis Tools

A social network consists of nodes representing individual
actors within the network
and ties which represent relationships between the individuals.
Social network analy-
sis (SNA) tools facilitate the study of social structure, providing
the means (methods)
to determine if there are regular patterns in social relationships,
and how these pat-
terns may be related to attributes or behaviour (Tang et al.
2011). In addition, SNA
could identify and map informal networks around any given
issue. It can be used to
identify who is connected to whom and thus adds value/does not
add value, and who
should be connected to whom to solve the issue at hand. It also
identifies conflicts
and broken links that need attention to facilitate more functional
action-orientated
relationships to achieve goals (Rowena 2010).
SNA can be used in policy making in order to identify a social
network’s patterns
and key actors and try to influence these (and, therefore, their
networks) by applying
appropriate targeted policy interventions. For example, SNA
could be used to think
through and tackle social issues such as unemployment. To do
this, SNA may pinpoint
the most influential key actors relevant to entrepreneurship or
employment (e.g.
pioneering entrepreneurs, venture capitalists, etc.) and target
these for promoting
entrepreneurship or employment policies.
Magus Networker26 was designed to illuminate complex,

informal networks so
that they become understandable. Powerful querying functions
enable key patterns
to be identified quickly, displaying where opportunities for
improving performance
can be developed.
7.3.10 Big Data Analytics Tools
Over the last decade, much information has gradually become
open. Sources of such
information include machine-operated sensors, video, digital
images, e-mail, social
media, and open data from government, research institutes, and
nongovernmental
organisations. The aim of open data movement is to make
information freely avail-
able, without restrictions and in standard machine readable
format (United Nations
Department of Economic 2010).
25 Due to the limited number of the identified tools for this
category and the following ones, we
decided not to summarise them in a table format.
26 http://www.magus-toolbox.com/Networker/.
[email protected]
Open data create significant opportunities for achieving deeper
and faster insights
towards knowledge development, decision making and
interdisciplinary collabora-

tion. However, they have little value if people cannot use them.
Thus, new tools and
technologies were developed lately to address this problem. One
of these technologies
is big data analytics.
Big data analytics tools have emerged due to the increasing
volume and variety
of open data that became available on the web. The term big
data refers to datasets
so large and complex that are difficult to process using
traditional data management
and processing techniques.
Big data analytics tools aim to tackle several technological and
analytical chal-
lenges, such as analysing unstructured data, uncovering hidden
patterns, exploiting
social media, making fast decisions on massive data volumes,
etc. Furthermore, big
data predictive analytics aim to unlock the value of big data and
make predictions
about future, or otherwise unknown events, in a near-real-time
mode (Nyce 2007).
Big data analytics tools can be used by government agencies for
information
purposes, e.g. for understanding what people are saying about
government, and
which policies, services, or providers are attracting negative
opinions and complaints.
Moreover, they can find out what people are concerned about or
looking for, e.g.
from the Google Search application programming interface
(API) or Google Trends,
which record Google’s search patterns of a huge number of

internet users. Based
on analysis of current and “historical” facts, they can develop
accurate models and
forecasts about the future.
In addition, big data can contribute to “smart” cities and
governments and to trans-
formational government. In particular, big and open data can
foster collaboration;
create real-time solutions to tackle challenges in agriculture,
health, transportation,
and more; promote greater openness; and introduce a new era of
policy and decision
making (Bertot et al. 2014).
Several applications utilising the power of big data are already
available. An
example is the case of an insurance company, named The
Climate Corporation,
which examines massive streams of climate data to assess future
risk and current
damage and provide insurance to farmers who can lock in
profits even in the case of
drought, excessive rains, or other adverse weather conditions.
Despite the wide adoption in the private sector, big data still
have limited applica-
tions in policy making. One of the few initiatives is that of New
Zealand, which has
recently expressed their intention to reform and/or create new
governmental services
to improve people, society, and economy. In particular, the
Ministry of Education
in New Zealand is already processing population projections,
building consent data,
and school enrolment data to work out where new schools are

needed. In addition,
using geospatial, population, traffic, and travel-to-work
information, it is possible to
locate the best place for a hospital, school, or community
facility, to serve commu-
nities most at need, or cut travel times. Moreover, Ministry of
Social Development
is using data to better learn which of its services get better
outcomes for individuals
and communities in order to waste less public expenditure on
services that do not
work and invest more on what does work (New Zealand Data
Futures Forum).
[email protected]
Making 147
7.3.11 Semantics and Linked Data Tools
Semantic technology enables users to enrich their documents
and contents with
machine-processable semantics of data that make use of
metadata to enable more
sophisticated data mining (Berners-Lee et al. 1999). The
explicit representation of
the semantics of data is accompanied by domain theories,
namely ontologies. Linked
data is based on semantic web philosophy and technologies, but,
in contrast to the full-
fledged semantic web vision, it is mainly about publishing
structured data in Resource
Description Framework (RDF) using uniform resource

identifiers (URIs) rather than
focusing on the ontological level or inferencing (Hausenblas
2009). Thus, linked
data refer to the ability to link together different pieces of
information published
on the Web and the ability to directly reference to a specific
piece of information
(Cyganiak et al. 2011; Heath and Bizer 2011).
Responding to this trend, traditional content management
systems (CMS) have
been improved to support semantic technology and provide
semantic lifting of the
textual content (Auffret 2001). For example, new CMS enable
users to (collaborative)
elaborate their documents and online texts submitting comments
and annotations
(e.g. Enrycher, Annotea). In other cases, users can define and
store data based on
custom ontologies created by them (e.g. WebNotes).
Furthermore, some CMS have
tried to support linked data techniques such as automatic
detection of entities such as
persons, places, and locations, and their linking to external
sources, e.g. to dbpedia
descriptions of resources (e.g. Apache Stanbol). On the other
hand, several tools have
been created to address collaborative creation of ontologies
(OntoMat-Annotizer,
OntoGen).
Considering the recent shift towards massively offering open
nonpersonal gov-
ernment data, one can easily understand the importance of
linked data in the field
of policy making (Kalampokis et al. 2011). One example of how

Linked Open Data
may be effectively used to inform discussions held by policy
makers and others is the
clean energy information portal, Reegle27. This portal
interprets raw data in order to
provide useful information and context for end users: It
provides high-quality infor-
mation on renewable energy efficiency and climate compatible
development around
the world as easily navigable graphs and tables with a lot of
additional information
at hand too.
7.4 Summary and Discussion
In the previous section, we presented emerging tools and
technologies with the
potential to enhance policy making. In this section, we would
like to provide an
overall discussion of this potential, especially with regard to
three main aspects for
policy making:
27 http://www.w3.org/2012/06/pmod/report.
[email protected]
• The main activities facilitated by each tool and technology.
Previous analysis
showed that each tool category presents a different way for
enhancing policy mak-
ing. For example, some tools focus on providing information in

a user-friendly
manner, other tools promote deliberation, other tools are used to
gauge pub-
lic opinion, etc. Analysing this characteristic, we can draw
conclusions on the
different ways each emerging tool and technology may be used
in policy making.
• The stage of the policy cycle facilitated by each tool and
technology. It was
previously mentioned that the policy making process is
composed of a number of
stages; these stages describe the policy life cycle. Analysing the
“fit” of each tool
and technology in the policy cycle stages promotes
understanding of how each
tool and technology can enhance the policy-making process. We
will consider
four main stages of the policy cycle as they were defined by
Jann and Wegrich
(2006): Agenda setting; policy formulation and decision
making; implementation;
evaluation and termination.
• The stakeholder types that can use each tool and technology.
We categorise the
previously identified stakeholders in policy making as follows:
institutional stake-
holders (i.e. the government), noninstitutional stakeholders (i.e.
political parties,
political consultants, and lobbyists, the media, nongovernmental
organisations,
civil organisations, and other interested parties), and the public.
Analysing who
of these stakeholder groups could use each tool and technology
and in what ways,

promotes understanding of how these tools and technologies can
be adopted in
policy making.
Following this, we examined each category of the identified
tools and technologies
with regard to these three aspects.
Visualisation tools are ideal mainly for information provision,
namely for present-
ing data in a user-friendly, easy-to-grasp representation. These
tools can be used in
any stage of the policy cycle, wherever the need for
demographic, social, or spatial
data representation emerges. For example, they can be used
during the decision-
making stage in order to fine-tune new policies, during the
implementation and
evaluation stage in order to understand whether the application
of a certain policy
brought any changes or even during the agenda-setting stage in
order to identify
problems that should be addressed with policies. All types of
stakeholders may be
potential users of visualisation tools depending on the topic
addressed and due to the
fact that no specialisation is required in order to use and
understand them.
Argumentation tools are ideal for structured deliberation,
namely for discussing
specific issues with the aim to reach a common understanding or
a commonly ac-
cepted decision. As such, these tools can be useful in all stages
of the policy cycle,
whenever a targeted deliberation is needed; maybe they are

more relevant for the
agenda setting, the policy formulation and decision making, and
the evaluation and
termination stages where such discussions are usually
performed. With regard to
potential users, in principle, all stakeholders can use
argumentation tools. However,
previous experience in the field has shown that argumentation
tools require a cer-
tain degree of logic and critical thinking. It is, therefore, not
easy for the general
public to productively use these tools without prior training
(Tambouris et al. 2011
[email protected]
Making 149
and Panopoulou et al. 2012). For this reason, argumentation
tools may be more ef-
fectively used for somewhat “closed” deliberation groups
targeting a specific issue
within a certain policy field.
eParticipation tools are ideal for involving the public in the
policy-making process.
They refer to many different activities such as information
provision, deliberation,
consultation, gauging public opinion, citizen engagement,
community building, etc.
eParticipation tools may be initiated by an institutional
stakeholder (top-down partici-
pation) or a noninstitutional stakeholder or even the public

(bottom-up participation).
Thus, all stakeholder types are potential users of these tools,
although typical usage
refers to interactions between the government and the public.
Due to the wide spec-
trum of supported activities, eParticipation tools may be used in
any stage of the
policy cycle.
Opinion mining tools are ideal for gauging the public’s opinions
and sentiments,
thus, they can be used in any stage of the policy cycle whenever
such a service is
needed. For example, they can be used for gauging the
acceptance potential of a
new policy or for detecting negative evaluations of a policy.
Due to their technical
complexity, opinion mining tools are better suited to be used by
trained institutional
stakeholders or noninstitutional stakeholders, but not the
general public.
Simulation tools are useful in policy making for detecting and
simulating social
interactions and behaviour patterns. For example, they can be
used for simulating
the long-term impact of different policy alternatives and thus
assist in the policy
formulation and decision-making stage. Simulation tools are
technically complex
to implement; therefore, they are mostly suited for usage by a
few specialised
institutional or noninstitutional stakeholders.
Serious games are useful in policy making for educational
purposes. They are

mostly relevant to the policy formulation and decision-making
stage of the policy
cycle, as players may assume a stakeholder’s role in order to
explore different policy
scenarios on a given topic and make relevant decisions. Serious
games can also
be used in the implementation stage of the policy cycle, for
educating citizens on
how to apply a certain state policy, e.g. a health or
environmental policy. The main
stakeholder group of serious games is the wide public.
The two tools included in our analysis that were specifically
developed for policy
makers are relevant to the policy formulation and decision-
making stage and to the
evaluation and termination stage of the policy cycle. Of course,
their user group
includes only institutional or noninstitutional stakeholders.
Persuasive tools can be used by institutional or noninstitutional
stakeholders
for influencing public attitudes and behaviours. Thus, it is
mostly relevant to the
implementation stage of the policy cycle, for strengthening
policy adoption.
Social network analysis tools are useful for identifying key
actors and social
patterns relevant to specific policy areas. These can be used in
the policy formulation
and decision-making stage, and in the implementation stage of
the policy cycle for
deciding alternative policies or for strengthening policies’
implementation. SNA is a
complex process requiring specialised knowledge, thus it can

only be used by trained
institutional or non-institutional stakeholders.
[email protected]
Big data analytics tools can be useful in policy making for
processing huge
amounts of information and, through this, for detecting and
predicting patterns and
trends of the public. These activities are relevant to all stages of
the policy cycle,
maybe less relevant to the implementation stage. Nonetheless,
the users of this tech-
nology can be the government per se or noninstitutional
stakeholders interested in
analysing data for a specific topic.
Semantics and linked data tools can be exploited for enhancing
interoperability of
government data and for creating linkages between open
government data and social
data. Thus, linked data tools can facilitate better understanding
of social data and
public opinion and better prediction of public reactions, e.g. to
different policy alter-
natives. For this reason, semantics and linked data tools seem
relevant to all stages
of the policy cycle. Again, the specialty required for applying
these technologies
means that only institutional or noninstitutional stakeholders
may be the immediate
users of such technologies (Table 7.8).

The table above shows that a policy stakeholder has a number of
different ICT
tools and technologies at hand. From these, they could choose
the most appropriate
ICT mix depending on the targeted activity and policy-making
stage. For example,
we can draw the following conclusions:
• Visualisation tools, argumentation tools, opinion mining tools,
big data, linked
data, and eParticipation tools may be used at any point of the
policy-making
process depending on the activities needed.
• Serious games and persuasive tools are the most appropriate in
order to strengthen
the implementation stage and promote policy adoption.
• The policy formulation and decision-making stage of the
policy cycle is the
most frequently addressed stage. This is not surprising as this
stage involves
multiple and diverse activities such as scenario analysis, policy
drafting, public
consultations, and decision making.
• Visualisation tools, big data analytics tools, and linked data
tools can help enhance
provision and analysis of large amounts of information.
• A number of different technologies have emerged for detecting
opinions, senti-
ments, trends, and other patterns of behaviour: opinion mining,
simulation, social
network analysis, big data analytics tools, and linked data tools.

There is clearly a
trend for using modern ICT towards analysing crowd knowledge
already available
online.
• For exploiting advanced tools and technologies expert skills
are needed that can
only be hired in the context of big (institutional or
noninstitutional) organisations.
• For involving the public, visualisation tools, eParticipation
tools, and serious
games are the most appropriate choices.
Acknowledgments This work is partially funded by the
European Commission within the
7th Framework Programme in the context of the eGovPoliNet
project (http://www.policy-
community.eu/) under grand agreement No. 288136.
[email protected]
http://www.policy-community.eu/
http://www.policy-community.eu/
Making 151
Table 7.8 Potential of emerging tools and technologies for
enhancing policy making
Tools and
technologies
Main activities Policy cycle stages Stakeholder types

Visualisation
tools
Information
provision
All All
Argumentation
tools
Structured
deliberation
All (possibly less in
the implementation
stage)
All (though not easy
for untrained public)
eParticipation
tools
Information
provision,
deliberation,
gauging opinions,
citizen engagement
All All, typically for
interaction between
the public and the
government

Opinion-mining
tools
Gauging opinions
and sentiments
All Institutional or
noninstitutional
stakeholders
Simulation tools Detecting and
simulating social
interactions and
behaviour patterns
Policy formulation
and decision
making
Institutional or
noninstitutional
stakeholders
Serious games Policy education Policy formulation
and decision
making,
implementation
The public
Tools specifically
developed for
policy makers
Policy analysis and
assessment

Policy formulation
and decision
making, evaluation
and termination
Institutional or
noninstitutional
stakeholders
Persuasive tools Influencing public
attitudes and
behaviours
Mostly relevant to
Implementation
Institutional or
noninstitutional
stakeholders
Social network
analysis tools
Identifying key
actors and social
patterns
Policy formulation
and decision
making,
implementation
Institutional or
noninstitutional
stakeholders

Big data
analytics tools
Information
processing,
detecting and
predicting patterns
and trends
All (possibly less in
the implementation
stage)
Institutional or
noninstitutional
stakeholders
Semantics and
linked data tools
Understand
opinions, predict
public reaction
All Institutional or
noninstitutional
stakeholders
[email protected]
Appendix

Visualisation Tools
China Geo – Explorer II http://chinadataonline.org/cge
Data Visualizer-World Bank
http://devdata.worldbank.org/DataVisualizer
DataPlace http://www.dataplace.org
http://devdata.worldbank.org/DataVisualizer
Dynamic Choropleth Maps
http://www.turboperl.com/dcmaps.html
e-Atlas of Global Development–World Bank
http://data.worldbank.org/products/
data-visualization-tools/eatlas
Gapminder http://www.gapminder.org/tag/trendalyzer
Google Charts https://developers.google.com/chart
Google Public Data Explorer
http://www.google.com/publicdata/directory
Health Infoscape http://senseable.mit.edu/healthinfoscape
NComVA http://www.ncomva.com
OECD eXplorer http://stats.oecd.org/OECDregionalstatistics
PolicyMap http://www.policymap.com
R http://www.r-project.org
Social Explorer http://www.socialexplorer.com
STATcompiler http://www.statcompiler.com
State Cancer Profiles
http://statecancerprofiles.cancer.gov/micromaps
Visokio Omniscpoe http://www.visokio.com
Visualize Free http://visualizefree.com
Worldmapper http://www.worldmapper.org
Argumentation Tools
Araucaria http://araucaria.computing.dundee.ac.uk/doku.php
Argunet http://www.argunet.org
bCisive https://www.bcisiveonline.com

Carneades http://carneades.github.io
Cohere http://cohere.open.ac.uk
Compendium http://compendium.open.ac.uk/institute
Cope_it! http://copeit.cti.gr/Login/Default.aspx
DebateGraph http://debategraph.org
MindMeister http://www.mindmeister.com
Rationale http://rationale.austhink.com
eParticipation Tools
Citizen Space https://www.citizenspace.com/info
Adhocracy.de http://code.adhocracy.de/en
[email protected]
http://data.worldbank.org/products/data-visualization-
tools/eatlas
http://data.worldbank.org/products/data-visualization-
tools/eatlas
Making 153
CitySourced https://www.citysourced.com
CivicEvolution.org http://civicevolution.org
Dialogue App http://www.dialogue-app.com/info/
Loomio.org https://www.loomio.org/
MixedInk.com http://www.mixedink.com
Opinion Space http://opinion.berkeley.edu
OurSpace http://www.ep-ourspace.eu/
Puzzledbypolicy http://www.puzzledbypolicy.eu
UbiPol http://www.ubipol.eu/
Opinion Mining Tools

AIRC Sentiment Analyzer http://airc-sentiment.org
Attentio http://www.attentio.com
Convey API https://developer.conveyapi.com
Corpora’s Applied Linguistics
http://www.corporasoftware.com/products/
sentiment.aspx
DiscoverText http://www.discovertext.com
Opinion observer
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.
8899
Opinmind http://www.opinmind.com
Repustate https://www.repustate.com
Sentimentor http://sentimentor.co.uk
Sentiment140 http://www.sentiment140.com
Social Mention http://socialmention.com
SwiftRiver http://www.ushahidi.com/products/swiftriver-
platform
ThinkUp https://www.thinkup.com/
Umigon http://www.umigon.com/
Agent-Based Modelling and Simulation Tools
Budget simulator http://www.budgetsimulator.com/info
C-ROADS http://climateinteractive.org/simulations/C-ROADS
CLASP’s Policy Analysis Modeling System (PAMS)
http://www.clasponline.org/
en/Tools/Tools/PolicyAnalysisModelingSystem
EUREAPA tool https://www.eureapa.net/
GLEaMviz http://www.gleamviz.org/simulator
Global Buildings Performance Network (GBPN) Policy
Comparative Tool
http://www.gbpn.org/databases-tools/purpose-policy-

comparative-tool
MASS http://mass.aitia.ai
MEL-C http://code.google.com/p/jamsim
[email protected]
http://www.corporasoftware.com/products/sentiment.aspx
http://www.corporasoftware.com/products/sentiment.aspx
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.889
9
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.889
9
http://www.clasponline.org/en/Tools/Tools/PolicyAnalysisMode
lingSystem
http://www.clasponline.org/en/Tools/Tools/PolicyAnalysisMode
lingSystem
Repast http://repast.sourceforge.net
Threshold 21 http://www.millennium-
institute.org/integrated_planning/tools/T21
UrbanSim http://www.urbansim.org/Main/WebHome
Serious Games
2050 Pathways https://www.gov.uk/2050-pathways-analysis
Breakaway (Disaster Management-Incident Commander)
http://www.
breakawayltd.com
Budget Hero
http://www.marketplace.org/topics/economy/budget-hero
CItyOne http://www-
01.ibm.com/software/solutions/soa/innov8/cityone/index.

html
Democracy http://www.positech.co.uk/democracy
Maryland Budget Map Game
http://iat.ubalt.edu/MDBudgetGame
MP For A Week http://www.parliament.uk/education/teaching-
resources-lesson-
plans/mp-for-a-week-game/
NationStates—create your own country
http://www.nationstates.net
The Social Simulator http://www.socialsimulator.com
Urgent Evoke http://www.urgentevoke.com
World Without Oil http://worldwithoutoil.org
Policy-Making Tools
Oracle Policy Automation for Social Services
http://www.oracle.com/us/industries/
public-sector/059171.html
PolicyMaker http://www.polimap.com/default.html
Semantics and Linked Data Tools
Annotea http://www.w3.org/2001/Annotea
Apache Stanbol http://stanbol.apache.org
Enrycher http://ailab.ijs.si/tools/enrycher
OntoGen http://ontogen.ijs.si
OntoMat-Annotizer http://annotation.semanticweb.org/ontomat
Reegle http://www.reegle.info
WebNotes http://www.webnotes.net
[email protected]
http://www.millennium-

institute.org/integrated_planning/tools/T21
http://www.breakawayltd.com
http://www.breakawayltd.com
http://www-
01.ibm.com/software/solutions/soa/innov8/cityone/index.html
http://www-
01.ibm.com/software/solutions/soa/innov8/cityone/index.html
http://www.oracle.com/us/industries/public-sector/059171.html
http://www.oracle.com/us/industries/public-sector/059171.html
Making 155
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Chapter 8
Value Sensitive Design of Complex Product
Systems

Andreas Ligtvoet, Geerten van de Kaa, Theo Fens, Cees van
Beers,
Paulier Herder and Jeroen van den Hoven
Abstract We increasingly understand technical artefacts as
components of complex
product systems. These systems are designed, built, maintained,
and deprecated by
stakeholders with different interests. To maintain
interoperability between compo-
nents, standards are being developed. The standardisation
process itself is, however,
also influenced by different stakeholders.
In this chapter, we argue that a full, comprehensive overview of
all relevant
components of a system is increasingly difficult. The natural
response to complex
problems is to delve into details. We suggest that an opposite
move towards a more
abstract approach can be fruitful. We illustrate this by
describing the development of
smart meters in the Netherlands. A more explicit focus on the
values that play a role for
different stakeholders may avoid fruitless detours in the
development of technologies.
Policymakers would do well by not only addressing functional
requirements but also
taking individual and social values into consideration.
8.1 Complex Technology
Modern society is highly dependent on a number of
infrastructures; electricity and
telecommunications infrastructures are considered most critical

(Luiijf and Klaver
2006). The desire to move towards a more sustainable energy
system with a more
decentralised structure, and with a focus on renewable energy
sources such as solar
energy and wind power, requires adjusting the existing,
centralised electricity in-
frastructure. By adding information and communication
technologies (ICT) to the
electricity grid at all levels of the system—from high-voltage
transformers to washing
machines—each node in the network can decentrally respond to
its neighbourhood
while safeguarding the reliability of the whole system. The
concept of such a new
electricity infrastructure is known as the smart grid.
The smart grid concept implies a number of changes at various
system levels
(national transmission grid, local distribution grid, and
residential connections;
A. Ligtvoet (�) · G. van de Kaa · T. Fens · C. van Beers · P.
Herder · J. van den Hoven
10.1007/978-3-319-12784-2_8
[email protected]

158 A. Ligtvoet et al.
Morgan et al. 2009) with a large role for ICTs (Mulder et al.
2012). However,
the precise technological constellation of smart grid systems is
yet unknown, and
a matter of discussion for politicians and policymakers,
(systems) engineers and
standardisation bodies, energy providers and distributors,
knowledge institutes and
telecommunication organisations, and citizen representatives.
Even when limiting ourselves to the residential realm the
number of interrelated
issues is vast. A case in point is the smart meter. This improved
version of an electricity
meter is seen as an important element for electricity grid
optimisation that also allows
for end-user efficiency through insight into consumption
patterns (EC 2011). The
most comprehensive version of such a device provides
information about household
energy consumption (accounting for decentral generation). The
smart meter transmits
this information to energy providers and/or distributors to
improve their systems,
and control of electric devices remotely, for example, to
optimise the load of the
distribution grid, or to switch off consumers who have not paid
their bills. In practice,
however, smart meter deployment is guided by various motives
(e.g. fraud detection
or improved billing) that have different technical requirements
(AlAbdulkarim 2013).
At the same time, it has become clear that the roll-out of smart

meters can only be
successful if the end users in households also recognise their
benefits (Cuijpers and
Koops 2013; Hierzinger et al. 2013). Until recently, this has not
been the case
and citizens have voiced concerns about issues including
privacy (McDaniel and
McLaughlin 2009) and health effects (Verbong et al. 2013; Hess
and Coley 2012).
In this chapter, we take the position that technology
development is driven by the
needs and requirements of a wide range of stakeholders. Among
these, technology
developers and their competitors play an important role in
shaping and standardising
technologies. Other stakeholders, such as households, may have
a less prominent
role in determining the development of technologies, but at
times play a significant
role in the acceptance of the technology (Mitchell et al. 1997).
It is important to
identify all the stakeholders involved, and to understand their
motives and values
so that the technology development can be adjusted in a timely
fashion. The Dutch
smart metering history provides a cautionary tale as the needs
of households end
users, one of the main stakeholders, were not sufficiently taken
into account. This
was one of the main reasons that the Dutch Senate rejected the
proposed Energy Bill
in 2008 (Cuijpers and Koops 2013) which consequently delayed
the roll-out of smart
meters for several years.

We aim to shed light on the development process of the
complex product system
and examine to what extent value-sensitive design (VSD) could
have avoided this
delay. We employ a case study analysis of the standardisation1
of smart metering in the
Netherlands. We add insights from overlapping standards
discussions in household
automation and show that the interlinked nature of ICT,
consumer products, energy
systems, and home automation does not allow a strict
delineation of technological
1 We follow the definition of standardisation as proposed by de
Vries (1999): An activity of estab-
lishing and recording a limited set of solutions to actual or
potential matching problems, directed
at benefits for the party of parties involved, balancing their
needs and intending and expecting that
these solutions will be repeatedly or continuously used, during a
certain period, by a substantial
number of the parties for whom they are meant.
[email protected]
8 Value Sensitive Design of Complex Product Systems 159
2004 2007 2010 2013
Po
lic
y l
ett

er
on
en
erg
y s
ec
uri
ty
Po
lic
y l
ett
er
on
lib
era
lis
ati
on
20
06
/32
/E

C
Gu
ide
lin
e
NT
A
81
30
pu
bli
sh
ed
Re
vis
ed
en
erg
y a
nd
ga
s l
aw
s

Se
na
te
req
ue
sts
am
en
dm
en
t
Pa
rli
am
en
t a
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rov
es
no
ve
lle
Se

na
te
ap
pro
ve
s n
ov
ell
e
Sm
all
-sc
ale
ro
ll-
ou
t
Fig. 8.1 Timeline of the smart meter policy process in the
Netherlands
artefacts, and leads to an oversimplification of the issues at
stake. We discuss to what
extent earlier analysis of this information could have led to
adjustments of standards
such as the Dutch smart metering requirements (DSMR).
8.2 Smart Meters in the Netherlands

Early scholarly mentions of intelligent or smart meters suggest
their (technical)
development took place in the 1980s and 1990s (see e.g. Peddie
1988). As we
are interested in official standardisation, we provide an
overview of Dutch policies
regarding smart meters (see also Fig. 8.1), its standardisation,
and the stakeholders
involved in this process.
Following a letter about security of energy supply from the
Ministry of Economic
Affairs to Dutch Parliament in 2003 (MinEZ 2003),
SenterNovem, a ministry agency,
was requested to investigate the standardisation, stakeholder
involvement, and con-
duct a cost–benefit analysis on the roll-out of a smart meter
infrastructure (Dijkstra
et al. 2005). Demand-side response was seen as a major
contribution to security
of supply during peak electricity consumption. The Dutch
standardisation institute
Nederlands Normalisatie-Instituut (NEN) was commissioned to
formulate and de-
scribe a national standard for smart meters. The societal cost–
benefit analysis proved
to be positive (a net gain of 1.2 billion €) with the citizens as
main beneficiaries of the
roll-out. Interestingly, in the ensuing stakeholder consultation,
consumer represen-
tatives were not heavily involved: “The point of view of the
consumer, individually
as a household, or collective via housing corporations, Home
Owners Association
or Consumers Association was not a key issue” (Dijkstra et al.

2005). The other
stakeholders—energy producers, energy suppliers, grid
operators, metering compa-
nies, telecom, energy regulators—requested the ministry to
clearly identify meter
functionalities, expedite meter roll-out by setting a time frame,
and provide regular
consumption overviews (to make smart meters the only
affordable solution).
Anticipating the EU Directive 2006/32/EC on on energy end use
and energy
services, the Ministry of Economic Affairs provided more
information on smart
meters requirements, citing billing administrative problems and
the energy savings
[email protected]
goals of the Commission as main arguments in favour of smart
meters (MinEZ
2006). In 2007, NEN published the technical agreement NTA
8130, which set out a
minimum set of requirements for smart metering. The
organisation of grid operators
(Netbeheer Nederland) took the lead in specifying these
requirements, which became
DSMR.
In 2008, the Ministry of Economic Affairs revised the
electricity and gas bills that
implemented the European directive. Grid operators became

responsible for meter
deployment, and energy providers were appointed the point of
contact for consumers.
This was supposed to increase clarity for consumers, efficiency,
and create a level
playing field for market parties. Consumers were required to
cooperate in installing
smart meters; not doing so would constitute an economic felony.
After several rounds
of reviews and discussions about privacy, and amendments as a
response to the Dutch
Data Protection Authority (Customs and Border Protection,
CBP), the bills were
passed by the Lower House of Parliament in July 2008. By that
time, the smart meter
and its privacy issues had gained wider public interest.
Technical experts assessed
possible security and privacy breaches of the meter, and legal
experts deemed the
proposed solution irreconcilable with the European Convention
on Human Rights
(Cuijpers and Koops 2013). When the bills were scrutinised by
the Senate in 2009,
it proposed amendments regarding the mandatory character of
smart meters and
revisions concerning consumer privacy.
The smart metering bill was amended into a voluntary roll-out
of smart meters and
reintroduced for political consideration in September 2010. The
customer could now
decline a smart meter and energy suppliers were required to
give customers bimonthly
statements with specific minimum information requirements.
The grid operators set
up uniform authorisation and authentication procedures to

ensure that individual
measurement data were only used for specific purposes and only
after customer
consent. The revised bills passed the Lower House of
Parliament in November 2010
and was approved by the Senate in February 2011 (Hierzinger et
al. 2013).
The Ministry of Economic Affairs agreed on a “small-scale”
deployment of smart
meters in 2012 and 2013. This 2-year period was used to test the
practical implications
of roll-out in approximately 400,000 households and to assess
consumer response. A
midterm review of the roll-out did not identify any major issues,
with only 2–3 % of
households rejecting the smart meter. At the end of 2013, there
is still a political debate
about whether the smart meter should be coupled with the
functionality to switch off
electricity and gas. In other countries, this was the main reason
to install smart meters,
but the Dutch Consumers’Association argued that remote-
controlled switches would
constitute a cyber threat on a nationwide scale. In its latest
consultation round, the
ministry seems to share this view.
Meanwhile, several stakeholders (notably hardware providers)
argue that the
Netherlands with its 8 million households and 750,000 small
and medium enter-
prise connections is not large enough to make a customised
smart meter financially
feasible. They emphasise that the DSMR should be abandoned
in favour of European

standards.
[email protected]
8.3 Smart Meters as Complex Product Systems
A smart meter could be seen as an artefact that, like a pair of
scissors, can be de-
signed or bought on the market in relative isolation. However,
our case study already
indicates that the development of smart meters is contingent
upon developments
on international, national, and (inter)organisational levels.
Literature on technology
management has long conceptualised technological artefacts as
subsystems that are
linked together, as well as being a component of even larger
systems (Clark 1985;
Suarez 2004). Tidd (1995) calls these complex product systems,
that have three
distinctive characteristics:
• Systemic: the systems consist of numerous components and
subsystems.
• Multiple interactions take place across different components,
subsystems, and
levels.
• Nondecomposable: the systems cannot be separated into their
components without
degrading performance.

This means that technologies, components, and interfaces
incorporated in products
are interdependent, and thus rely on standard interfaces, but
also depend on differ-
ent market segments and the range and specificity of
performance criteria within
these markets. This also means that technological designs,
sponsored by different
actors, compete for dominance through a process where
economic, technological,
and sociopolitical factors are intertwined (Rosenkopf and
Tushman 1998). The more
complex the product system, the greater is the number of actors
needing to be aligned
for a technological design, and thus the more complicated the
actual design process
becomes (Suarez 2004).
In the following sections, we indicate that the development of
smart meters and
home energy management systems (HEMS) is influenced by
competing formal and
industry standards (Sect. 8.3.1) and that a whole range of actors
or stakeholders is
involved in the development of these artefacts (Sect. 8.3.2). By
combining these two
analyses, a multifacted picture emerges.
8.3.1 Competing Standards
In the decision about smart meters and HEMS, competing
formal and nonformal
standards play a role. We have attempted to provide an
overview of different standards
that are related to these technologies in Table 8.1. This

overview shows us that there
are many options for the design of smart meters/HEMS
components. Whereas, there
may be some room for consolidation, some of the presented
standards provide unique
solutions to specific problems. As Gallagher (2007) indicates, it
remains extremely
difficult to pick “winners” ex ante.
Whereas smart metering falls under governmental regulation,
the market for
HEMS is not regulated. However, depending on the final
specifications of the
smart meter, some functionalities may overlap. Many different
established and
[email protected]
Table 8.1 Overview of different (competing) standards in the
converging technology realms
Component/Subcomponent Competing standards
Smart meter
Smart meter NTA 8130, DRMS X.X, DLMS, IEC 62056-21,
NEN-EN 13757,
IEEE Std 1901, IEEE P1703, IEEE 1377, DLMS/COSEM
standard (IEC 62056 / EN 13757-1), IEEE 802.15.4, Wired
M-Bus, M-Bus protocol (EN 13757), 6LoWPAN, ANSI C12.18,
IEC 61107

Communication systems
Wired local area networks
(application level)
Arcnet vs ATM vs CEPCA vs Ethernet vs FDDI vs Home plug
and play vs homeplug vs hiperlan2 vs open air vs Passport vs
Powerpacket, Smart Energy Profile 2, Universal Powerline Bus
(UPB), DMX512
Wired local area networks
(infrastructure level)
FRF vs MPLS/Framerelay vs Orthogonal frequency divising
multiplexing vs Salutation vs SSERQ vs Token Bus vs Token
Ring vs UPA
Wireless local area
networks (infrastructure
level)
HomeRF vs IEEE802.16 vs Open air vs IEEE802.11(Wifi),
HiperLAN
Wireless personal area
network
Bluetooth vs IEEE 802.15.3 vs IEEE 802.15.4 vs Irda vs
Zigbee,
Z-wave, 6LoWPAN
Power line communication IEEE Std 1901-2010,
HomePlug,G.hn(G.9960), PRIME,
PLC-G3, IEC-61334 SFSK
Computer networks

(wired)
USB vs Convergence bus vs Firewire vs IRDA
Mobile
telecommunications
3G vs Dect vs GPRS vs GSM vs UMTS
Home automation
systems
Home networks DLNA vs HANA vs HAVi vs HomeAPI vs
HOMAPNA vs Moca
vs UPnP, IEC/TS 62654, NEN-ISO/IEC 15045-1, ISO/IEC
14543-3-7, IEC 61970, IEEE 1905.1, ITU-T G.9960,
CEA-2027-B, CEA-2033, CAN/CSA-ISO/IE, NEN-EN 50090-1,
MultiSpeak, IEC 62457, H950 SystemLink
Home automation (wired
and wireless)
CEA851 vs CEBus vs Echonet vs EHS vs HBS vs HES vs HGI
vs
HomeCNA vs HomeGate vs HPnP vs Lontalk vs Smarthouse,
ISO/IEC 14543KNX, Zigbee, digitalSTROM, ISO/IEC TR
15044, EN 50090 (KNX/EIB)
Building automation BACnet vs BatiBUS vs COBA vs
DALI/IEC 60929 vs FND vs
Instabus vs KNX vs Metasys vs MOCA vs Profibus vs Worldfip
vs X10 vs Zigbee, NPR-CLC/TR 50491-6-3, ISO
16484-5BACnet NEN-EN 13321-1, NEN-EN 15232, ISO
16484-5, ISO 50001, ISO/IEC 18012-1, EnOcean, Modbus,
oBIX

Consumer electronics
Video Displayport vs DVI vs HDMI vs Scart vs VESA vs VGA
[email protected]
Component/Subcomponent Competing standards
Energy systems
Heat/cold storage NEN-EN-IEC 60531, NEN-EN-IEC 60379
Electric stationary storage
batteries
J537, 1679-2010 IEEE
Electric car batteries SAE J2847, SAE J2836/1-3, SAE J2931/5,
SAE J2758, Smart
Energy Profile (SEP 1.1), ASTM D 445, DIN 51 562 (part 1) ,
ISO 3105
Decentral electricity
production systems
Solar photovoltaics IEC 61215, IEC 61646, UL 1703, IEC
60904
Small wind mills DIN EN 61400-25-4, AGMA 6006-A03, NEN
6096

Micro CHPs DIN 4709
newly emerging industries and product markets are involved in
HEMS (den Har-
tog et al. 2004). We observe a convergence between established
industries such as
telecommunications, consumer electronics, and home
automation (domotics) with
new developments in energy industries: photovoltaics, micro
heat and power, mi-
cro wind, storage, and home automation. ICT plays a crucial
role at both national
and local level. In the energy sector, integrating information
technology with oper-
ational technology (IT/operations technology (OT) integration)
is seen as a major
development. In short, ICT is what makes a smart grid smart.
Actors that originate from the different converging industries
develop and pro-
mote standards that enable communication not only for
components within single
industries but also for communication between components that
originate in differ-
ent industries (van de Kaa et al. 2009). van de Kaa et al. (2009)
have performed a
search for standards for home networking and present a
graphical overview of these
standards that originate in different converging industries. We
use that graphical
overview and have adapted it for the situation of HEMS (see
Fig. 8.2).
While some of the standards mentioned in Fig. 8.2 clearly
belong in one industry,

we also see shifts taking place. We have indicated two of these
shifts in the figure.
Whereas Universal Serial Bus (USB) started off in the computer
industry, it became
increasingly used in consumer electronics, e.g. for allowing
MP3 music files to be
played on audio systems. Likewise, Digital Enhanced Cordless
Telecommunications
(DECT) was orginally a wireless telephony protocol, but was
soon used in baby
monitors, and since the development of the Ultra Low Energy
variant in 2011 it is
used in home appliances, security, healthcare, and energy
monitoring applications
that are battery powered.
[email protected]
USB
DECT
SCART
COAX
USB
HDMI
Firewire
WiFi
KNX
ZigBee

Zwave
PLC
M-bus
DECT
Information and communication technology
Consumer
Electronics
Energy
Home automation
UPnP
EchoNet
EHS
HES
COBA
Lonworks
Batibus
EIB
BACnet
X10

Metasys
DALI
PLC
IEC60531
IEC60379
IEC60904
DIN 4709
NEN 6096
TCP/IP
GSM
GPRS
UMTS
IEEE1901/1905
SEP2.0
ISO 9241-11
EN 50491-12
Fig. 8.2 Converging formal and industrial standards in the realm
of home energy management
8.3.2 Actor or Stakeholder Analysis
The fact that industries are converging broadens the number of
stakeholders involved
in technical developments. Although many of the traditional
players in the energy
field still play a role, opportunities have been created for niche
players to take on a

larger role. de Vries et al. (2003) have identified search
directions for stakeholder
identification: production chain, physical systems and their
designers, end users
and related organisations, inspection agencies, regulators,
research and consultancy,
education, representative organizations, and organised groups of
stakeholders. We
take this categorisation as a starting point, but add
standardisation bodies as an
additional category. We have attempted to capture the
interaction of electricity grid
components within a value chain representation in Fig. 8.3.
1. We take the electricity production chain as a starting point.
Important players are
the distribution system operators (DSOs). In the Netherlands,
these include the
largest DSOs Alliander, Delta, Enexis, and Stedin. Other
players are the energy
production companies (e.g. Nuon and RWE/Essent), energy
suppliers (which
may be middlemen between production and consumption), and
the national grid
operator Tennet.
[email protected]
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[email protected]
2. Various technology providers are involved in the
development and design of phys-
ical components that are part of the entire value chain. This
includes companies
such as Cisco Systems, IBM, Philips, Honeywell, and Siemens,
meter companies
such as Landis + Gyr, Echelon, Itron, and Iskra, and data
managers and integra-
tors such as Ferranti. Whereas some companies specifically
focus on one aspect
of the value chain, most have a broader involvement.
3. End users and related organisations are the actual home
owners and tenants that
may be organised in local groups such as local home owners’
associations (e.g.
owners of houses in the same building or street) or national
groups such as Verenig-
ing Eigen Huis (home owners association of the Netherlands).
Expert consultation
revealed that consumer representation in standardisation
committees is rare in
the Netherlands but in some countries such as the UK and Japan
consumers are
involved in standard development.
4. Important inspection agencies with regard to smart meters
include not only Keur-
ing van Elektrotechnische Materialen te Arnhem (KEMA), who

are charged with
organising Dutch meter inspections, but also NMi Certin and
Verispect.
5. Regulators (Autoriteit Consument en Markt (ACM)
Energiekamer) and policy-
makers (Ministry of Economic Affairs (Ministerie van
Economische Zaken, EZ))
are involved heavily in the smart meter component. And also
the European Union’s
policies have effect upon the Dutch smart meters and HEMS.
6. Universities, research institutes, and consultants play an
important and major role
in standardization for HEMS in the Netherlands. Noteworthy in
this context are
KEMA (who have provided several cost-benefit analyses) and
Nederlandse Or-
ganisatie voor Toegepast Natuurwetenschappelijk Onderzoek
(TNO; who have
advised the Ministry and parliament). The Netherlands’ national
metrology in-
stitute Van Swinden Laboratory (VSL) takes a special role in
this category, as
it is charged with certifying the (tools for) inspection agencies.
Other important
stakeholders include IT consultancy firms.
7. The education category is less relevant for the identification
of stakeholders for
HEMS. Although a lot of universities are actively engaged in
research relating to
smart grids, active participation in standardisation is rare.
8. National representative organisations include consumer
organisations such as the

Consumers’ Association (Consumentenbond). But also DSOs are
represented by
Netbeheer Nederland and energy producers are represented by
Energie Neder-
land. At the European level, smart meter providers are
represented by European
Smart Metering Industry Group (ESMIG). For other networks,
see below.
9. Standardisation bodies operate at different levels.
Internationally, there are Inter-
national Organization for Standardization (ISO), International
Electrotechnical
Commission (IEC), and International Telecommunication Union
(ITU), at the
European level Comité Européen de Normalisation (CEN),
Comité Européen
de Normalisation Électrotechnique (CENELEC), and European
Telecommuni-
cations Standards Institute (ETSI), and at the national level
NEN. See Sect. 8.3.3
below.
[email protected]
ISO IEC ITU-TInternational
CEN CENELEC ETSIEuropean
NENDutch
TelecommunicationElectrotechnicalBuilding, gas, water

Fig. 8.4 Standardisation organisations in different technology
fields at (inter)national levels
8.3.3 Networks of Stakeholders
Not only are the stakeholders mentioned in the previous section
active in influencing
policy but also are members of various standards organisations
and consortia.
Noteworthy are international formal standardisation
organisations ISO (gen-
eral standards; related to smart meters buildings, gas, and
water), International
Electrotechnical Commission (IEC; electrotechnical standards),
and International
Telecommunication Union-Telecom Sector (ITU-T;
telecommunication standards).
These organisations also have standardisation organisation
counterparts active on the
European level (CEN, CENELEC, and ETSI), and at the
national level (in the Nether-
lands this is NEN; see Fig. 8.4). Most standardisation
organisations have different
work groups or technical committees that develop standards for
particular product
markets and/or technical areas. Smart meters, for example, are
covered by IEC tech-
nical committee TC13 and its CENELEC counterpart
(conveniently named TC13).
However, some aspects may be covered by other technical
committees, such as TC57
on power systems management and associated information
exchange. Members of
these work groups are to a large extent drawn from industrial

partners or industrial
consortia. The members are, however, deemed to provide their
expertise independent
of their employers.
Next to the formal standardisation organisations, various
consortia exist that de-
velop and/or promote standards for components of HEMS.
These standards may be
based on formal standards, but used in a specific application
area. Several subcom-
ponents are combined to create a set of coherent components,
some of which are not
formal standards. In the field of HEMS, these consortia include:
• KNX Association—promoting a standard for home and
building control
• ZigBee Alliance—promoting a wireless technology designed
to address the
unique needs of low-cost, low-power wireless sensor and
control networks
• Salutation Consortium—promoting a service discovery and
session management
protocol providing information exchange among and between
different wireless
hand-held devices and office automation equipment.
[email protected]
• Echonet Consortium—promoting the development of basic

software and hard-
ware for home networks that can be used for remote control or
monitoring of
home appliances
• The Digital Living Network Alliance—publishing a common
set of industry de-
sign guidelines that allow manufacturers to participate in a
growing marketplace
of networked devices.
• Smart Grids European Technology Platform—a European
forum for the crystalli-
sation of policy and technology research and development
pathways for the smart
grids sector.
8.4 Values in the Design of Technical Artefacts
The vast amount of standards and stakeholders involved makes
an overview of the
possible technological trajectories nearly impossible. We
suggest that a focus on
values and the notion of VSD allows for a more comprehensive
view of smart meter
and HEMS development.
Values are mentioned in a wide array of disciplines (e.g.
philosophy, sociology,
economics) and generally denote what something is worth,
opinions about that worth,
and/or moral principles (Dietz et al. 2005). Values are also
described as “enduring
beliefs that a specific mode of conduct is personally or socially
preferable to an
opposite or converse mode of conduct or end-state of existence”

(Rokeach 1968).
In decision making, values, which can be described as an
abstract set of principles,
allow us to resolve conflicts by suggesting which preferences
are better. They provide
us with criteria to distinguish options (see, e.g. Keeney 1994).
Values also play a role in the design and use of technological
artefacts. Whereas
historically technology may have been considered purely
instrumental and value-free
(Manders-Huits 2011), it has become clear that technological
artefacts exhibit moral
and political choices and consequences (even though the moral
and political dimen-
sion may not be perceived by their designers and users). This
means that the choice
for a specific technology may imply a social and institutional
order without which
the technology might not work. Winner (1980) suggests that in
“societies based on
large, complex technological systems, . . . moral reasons other
than those of practical
necessity appear increasingly obsolete, ‘idealistic,’ and
irrelevant. Whatever claims
one may wish to make on behalf of liberty, justice, or equality
can be immediately
neutralized when confronted with arguments to the effect: ‘Fine,
but that’s no way
to run a railroad’ (or steel mill, or airline, or communications
system, and so on)”.
We would argue that the smart grid is one of those large,
complex technological
systems, for which the same argument holds.
[email protected]

8.4.1 Value-Sensitive Design
A stream of research that focuses on moral and political
dimensions of technology
and technology design is called Value Sensitive Design (VSD)
(Friedman et al. 2002;
Borning and Muller 2012). VSD seeks to be proactive to
influence the design of tech-
nology early in and throughout the design process. It employs
conceptual, empirical,
and technical investigations (Friedman et al. 2008; van de Poel
2009):
• Conceptual investigations aim, for instance, at clarifying the
values at stake, and
at making trade-offs between the various values.
• Empirical investigations involve social scientific research on
the understanding,
contexts, and experiences of the people affected by
technological designs.
• Technical investigations involve analysing current technical
mechanisms and
designs to assess how well they support particular values, and,
conversely, iden-
tifying values, and then identifying and/or developing technical
mechanisms and
designs that can support those values.
Many of the technological examples addressed in VSD literature

relate to ICTs (Fried-
man 1996; van den Hoven 2007; Friedman et al. 2008), which is
why we expect the
approach to be pertinent to smart meter/home/grid technologies.
VSD started from
the recognition that when designing information technologies,
the predominant, tra-
ditional focus of engineers is on functionality, i.e. the
efficiency, reliability, and
affordability of (new) technologies—conform the practical
necessity argument iden-
tified by Winner (1980). Furthermore, the point of reference is
often the designer’s
own experiences, needs, and values. For example, it has been
shown that software
designers (unknowingly) design software that is more aligned
with males than with
females. Friedman (1996) also mentions an example of
educational software that is
geared towards the American competitive education system
which is less successful
in foreign classrooms, where cooperation is considered more
important.
8.4.2 Values in Our Research
Although it is embedded in moral philosophy, VSD uses a broad
sense of values:
values refer to what persons, either singularly or collectively,
consider important
to their lives. However, the 56 personal values that the
Schwartz Value Survey,
commonly used in social sciences (Dietz et al. 2005), defines,
might not relate to
technological artefacts and technology use. For this study, we
therefore focus on a

subset that is often mentioned in VSD literature. Next to the
already mentioned func-
tional values (accountability, correctness, efficiency,
environmental sustainability,
legitimacy, reliability, safety, tractability), we address social
values (cooperation,
courtesy, democracy, freedom from bias, identity, participation,
privacy, trust) and
individual values (autonomy, calmness, economic development,
informed consent,
[email protected]
ownership, universal usability, welfare). Most of these values
are defined in Fried-
man et al. (2008). For the purposes of our research, we have
translated these into
broad definitions which can be found in Table 8.2.
We have attempted to identify the values that played a role in
the development
of smart meters (Ligtvoet et al. in press). Based on expert
elicitation, the five most
important values associated with smart meters are:
1. Privacy: The system allows users to determine which
information about them is
used and communicated.
2. Correctness: The system provides correct data or performs
the correct function.
3. Reliability: The system fulfils its function without the need

to monitor/control it.
4. Informed consent: The system allows its users to voluntarily
agree to its activation,
based on comprehensible information.
5. Economic development: The system is beneficial to its users’
economic or
financial status.
These results very closely match the general impression of the
smart metering debate
in the Netherlands. Privacy is a very important value that was
virtually ignored at
the start of the implementation process. As could be expected
for a device that is
designed to measure, the functional values of correctness and
reliability are also
ranked high. The individual values of informed consent and
economic development
emphasise that end users’ needs should be taken into
consideration.
An interesting and unexpected finding of our expert group
discussion was that
these values depend on the delineation of the system. The
experts indicated that the
important values actually shift when the smart meter is not only
seen as a connected
measuring device but also more as an energy management nexus
for households.
This generates a new ranking of values for HEMS:
1. Economic development: The system is beneficial to its users’
economic or
financial status.

2. Universal usability: The system can easily be operated by all
users.
3. Privacy: The system allows users to determine which
information about them is
used and communicated.
4. Autonomy: The system allows its users to make their own
choices and pursue
their own goals.
5. Reliability: The system fulfils its function without the need
to monitor/control it.
Here, we see a clear shift towards the individual and social
values of the users and
slightly less emphasis on the functional values of the
technology. We believe that
this corresponds with findings of Krishnamurti et al. (2012) and
Balta-Ozkan et al.
(2013). Compared with standalone smart meters, a clearly
higher score was given for
participation and well being, again emphasising the user
experience. Also, the values
legitimacy and freedom from bias became much less important.
In the discussion,
it became clear that HEMS are seen as a commercial consumer
product, for which
consumers are personally responsible.
[email protected]

Table 8.2 Overview of 23 values that we found in the value
sensitive design literature
Value Description
Accountability The system allows for tracing the activities of
individuals or
institutions
Autonomy The system allows for its users to make their own
choices and
choose their own goals
Calmness The system promotes a peaceful and quiet state
Cooperation The systems allows for its users to work together
with others
Correctness The systems processes the right information and
performs the right
actions
Courtesy The system promotes treating people with politeness
and
consideration
Democracy The system promotes the input of stakeholders
Economic development The system is beneficial to the
economic status/finances of its users
Efficiency The system is effective given the inputs
Environmental
sustainability

The system does not burden ecosystems, so that the needs of
current
generations do not hinder future generations
Freedom from bias The system does not promote a select group
of users at the cost of
others
Identity The system allows its users to maintain their identity,
shape it, or
change it if required
Informed consent The systems allows its users to voluntarily
make choices, based on
arguments
Legitimacy The system is deployed on a legal basis or has broad
support
Ownership The system facilitates ownership of an object or of
information and
allows its owner to derive income from it
Participation The system promotes active participation of its
users
Privacy The system allows people to determine which
information about the
is used and communicateda
Reliability The system fulfils its purpose without the need to
control or
maintain it
Safety and health The system does not harm people

Tractability The functioning of the system can be traced
Trust The system promotes trust in itself and in its users
Universal usability The system can be easily used by all
(foreseen) users
Welfare The system promotes physical, psychological, and
material
well-being
a We acknowledge that this is a limited definition of privacy
[email protected]
8.5 Discussion
8.5.1 From Values to Design Requirements
VSD purports to be a holistic approach that combines theory
with empirics (Manders-
Huits 2011). The identification of values should therefore be
linked to the formulation
of design requirements for complex product systems, in our case
smart meters and
HEMS.
The need for (elements of) privacy was addressed in the
NTA8130 standard and
the requirement of an encryption protocol was added.
Functional requirements of
correctness and reliability were already covered by the Dutch

measurement code and
no further requirements were necessary. Informed consent is not
easily addressed
from a technological standpoint, but it did prove important in
the debate in the Senate.
The solution was not technical, but procedural. The end user
was given four options:
no smart meter but an ordinary one, a smart meter that does not
communicate,
low-frequency communication, or high-frequency
communication. The economic
development was addressed by several cost-benefit assessments
and a restriction of
the metering tariff.
Given the nature of HEMS (i.e. more like a consumer product),
the values as-
sociated with it should also be addressed in a slightly different
way. The focus on
economic development suggests a restriction in the price of the
system and a clear
indication of how much can be saved by installing such a
system. Universal us-
ability emphasises the need for easy-to-use interfaces: end users
should not require
an engineering degree to operate the system. Privacy remains an
issue and requires
communication channels to be secured—similar to (mobile)
telecommunication and
computing requirements. Autonomy suggests that the users
should be in charge of
their home energy management and automation: this is closely
linked to ease of use.
And finally, the system should be reliable like other consumer
products.

We acknowledge that the current research has performed an ex
post analysis of
values and identified issues that were already resolved in the
course of history of the
Dutch smart meter (standard and requirements). The proof of
the pudding would be
an ex ante assessment and monitoring of the upcoming issues.
8.5.2 Values Salience
Our research has led us to question the extent to which values
are important and
identified, which one could call “values salience”. Comparing
smart meters to other
technological systems such as communication systems or smart
grids, we find that
the meter has received quite some attention. (On the basis of
our interviews, we
believe that smart meters were initially only considered a
technical issue.) We sug-
gest that values salience relates to the size of the technical
system according to an
inverted U as shown in Fig. 8.5. This means that small technical
components such
as communication protocols attract little attention and the
general public remains
[email protected]
Fig. 8.5 Values salience of a
technical component depends
on its size

Technical component size
V
al
ue
s
sa
lie
nc
e
low
high
small large
System-
of-systems
(e.g. smart
grid)
System
(e.g. smart meter)
Technical
(sub)component
(e.g. RF protocol)
indifferent. The same argument holds for large systems such as
an entire electricity

grid—although the public may still be able to judge some of the
importance of such
an artefact for their own energy supply, they largely remain
uninvolved. However,
the level of the household, thus of the smart meter, is most
visible to people and
therefore their attention and ability to express values is much
greater at this level.
8.5.3 Multidisciplinary Approach
Our research contributes to the literature on innovation
management and standardis-
ation (e.g. Schilling 1998, 2002; Suarez 2004; Sheremata 2004).
Scholars in the area
of innovation management and standardisation have attempted
to explain standard
dominance and draw from various areas of research including
network economics
and institutional economics (van de Kaa et al. 2011). They have
come up with
technology-, firm-, and environmental-level factors that explain
standard dominance
(Suarez 2004). In this chapter, we shed light on another level of
analysis that is
neglected in the literature: the end user. We provide a first
illustration of the notion
that societal acceptance of a platform will grow if a
technological design is modified
to changing user requirements related to ethical and societal
values surrounding the
technology. Privacy is the most salient value for the Dutch
smart meter case, but
informed consent also played an important role. Combining
literature from philoso-
phy and ethics on the one hand and technology management on

the other hand, we
provide a clearer view on the influence of factors relating to the
end user. Future
research could further explore the ex ante translation from
identified values to actual
design requirements.
[email protected]
8.6 Conclusion
Our case study of the development of smart metering and smart
metering standards in
the Netherlands shows the complexity of introducing new
technologies in an existing
sociotechnical system. We believe that our findings can be
generalised, not only to
other components of the smart grid but also to other systems-of-
systems that are
deployed and used by a wide array of stakeholders.
Current technological development is often so complex that
stakeholders are un-
able to fully assess new technologies. Nor are they able to
weigh the input of all
stakeholders. First of all, because not all stakeholders are
always involved and sec-
ondly, because people’s opinions and beliefs change because of
new information,
insights, and experiences. Although the introduction of a new
technical component
may start off from a very functional and technical position,

nontechnical issues
(values) can be introduced by consumers’ associations and other
stakeholders.
It can be argued that a lack of consideration for these values can
lead to a delay
in the roll-out of new technologies. Even though technical
solutions only seem to
address technical problems, they influence society through the
interconnected nature
of modern infrastructures. Especially, when technology is
“visible” at the household
level, consumers or their representatives can be expected to
have an opinion. For pol-
icymakers, it would be wise to foresee such stakeholder
involvement and to address
stakeholder values in an early stage.
The outcome of a values elicitation is a more balanced
representation of the
interests of all stakeholders, including end users: a combination
of functional, social,
and personal values. This focus on values may help designers in
their search for
better technical and functional specifications. However, such a
design process is
complicated by the fact that technical artefacts form an intricate
part of larger systems-
of-systems. As we have shown, depending on the system focus,
the related values are
somewhat different and there still may be some discussion to
what extent an artefact
serves a higher (system level) goal. This is certainly an area in
which VSD could
further develop and provide more guidance.

Acknowledgements This research was supported by Netherlands
Organisation for Scientific
Research (NWO) grant MVI-12-E02 on responsible innovation
(“maatschappelijk verantwoord
innoveren”). We are indebted to our valorisation committee
(Gertjan van den Akker, Theo Borst,
Johan Crols, Michiel Karskens, Gerrit Rietveld, Rick van der
Tol, and Gerritjan Valk) for their
insight and comments. We would also like to thank our
interviewees: Johan Boekema, Coco Geluk,
Tjakko Kruit, Erik Linschoten, Willem Strabbing, Jeike
Wallinga, and Teus de Zwart.
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[email protected]
Chapter 9
Stakeholder Engagement in Policy Development:
Observations and Lessons from International
Experience
Natalie Helbig, Sharon Dawes, Zamira Dzhusupova, Bram
Klievink
and Catherine Gerald Mkude
Abstract This chapter provides a starting point for better
understanding how
different approaches, tools, and technologies can support
effective stakeholder par-
ticipation in policy development. Participatory policy making
involves stakeholders
in various stages of the policy process and can focus on both the
substance of the

policy problem or on improving the tools and processes of
policy development. We
examine five international cases of stakeholder engagement in
policy development to
explore two questions: (1) what types of engagement tools and
processes are useful
for different stakeholders and contexts? And (2) what factors
support the effective use
of particular tools and technologies toward constructive
outcomes? The cases address
e-government strategic planning in a developing country, energy
policy in a transi-
tional economy, development of new technology and policy
innovations in global
trade, exploration of tools for policy-relevant evidence in early
childhood decision
making, and development of indicators for evaluating policy
options in urban plan-
ning. Following a comparison of the cases, we discuss salient
factors of stakeholder
N. Helbig (�) · S. Dawes
Center for Technology in Government, University at Albany,
187 Wolf Road,
Suite 301, 12205 Albany, New York, USA
S. Dawes
Z. Dzhusupova
Department of Public Administration and Development
Management
United Nations Department of Economic and Social Affairs
(UNDESA), New York, USA
B. Klievink
Faculty of Technology, Policy and Management, Delft

Jaffalaan 5, 2628 BX, Delft, The Netherlands
C. G. Mkude
Institute for IS Research, University of Koblenz-Landau,
Universitätsstr. 1,
56070 Koblenz, Germany
10.1007/978-3-319-12784-2_9
[email protected]
178 N. Helbig et al.
selection and representation, stakeholder support and education,
the value of stake-
holder engagement for dealing with complexity, and the
usefulness of third-party
experts for enhancing transparency and improving tools for
engagement.
9.1 Introduction
Complex public problems are shared and dispersed across
multiple organizations and
domains (Kettl 2002). Consider, for example, the array of
concerns associated with
improving air quality or assuring the safety of food products.
The formal governmen-
tal responses to these specific public needs are addressed
through public policies.

Policy might focus on different geographic locations, processes,
or products, or
could specify how certain outcomes are defined, observed, and
assessed. Moreover,
individuals, families, communities, industry, and government
itself are all affected
by policy choices, and they all have interests in both the
decision-making process
and the final decisions (Bryson 2004).
In light of seemingly intractable and complex social problems,
public administra-
tors have shifted toward governance activities that allow
citizens and stakeholders to
have deeper involvement in the policy-making process and the
work of government
(Bingham et al. 2005). Governance models which focus on
quasi-legislative activ-
ities such as participatory budgeting, citizen juries, focus
groups, roundtables, or
town meetings (Bingham et al. 2005; Fishkin 1995) create
opportunities for citizens
and stakeholders to envision their future growth (Myers and
Kitsuse 2000), clarify
their own policy preferences, engage in dialogue on policy
choices, or bring various
groups to consensus on proposals (McAfee 2004). The models
vary based on degree
of involvement by the general population, whether they occur in
public spaces, if the
stakeholders are actually empowered, and whether they lead to
tangible outcomes
(Bingham et al. 2005).
Stakeholder engagement objectives may also vary by their point
of connection

with the policy process (Fung 2006). The policy process is
complex and there are
many different ways to conceptualize how it works. The stages
heuristic of public
policy making is one of the most broadly accepted (Sabatier
1991). Although the
utility of the stages model has limits, and numerous advances in
theories and methods
for understanding the policy process have been made, the stages
heuristic continues
to offer useful conceptualizations (Jenkins-Smith and Sabatier
1993). While specifi-
cation and content of the stages vary somewhat throughout the
literature, however (as
shown in Fig. 9.1), models often comprise some combination of
problem identifica-
tion, agenda setting, formulation, adoption, implementation, and
policy evaluation
(Lasswell 1951; Easton 1965; Jones 1977). More recent
conceptualizations involve
feedback across the various stages.
Research in both the public and private sectors has identified a
number of bene-
fits associated with stakeholder engagement in governance.
Stakeholders’ interests
illuminate the multiplicity of factors that underlie policy
problems, decisions, and
implementation. Direct engagement of stakeholders increases
public understanding
[email protected]
9 Stakeholder Engagement in Policy Development 179

Fig. 9.1 Stages of the policy process
of the issues and the consequences of different choices.
Accordingly, engagement
generates more options for policies or actions. Engagement
brings more information
into the deliberation process from different kinds of
stakeholders so that decisions are
more likely to avoid unintended consequences and fit better into
existing contexts.
Engagement also reveals both conflicts and agreements among
different stakeholder
groups. While taking stakeholders into account is a crucial
aspect of solving public
problems, policy development includes both powerful and
powerless stakeholders
within the process (Bryson 2004). Some stakeholders have the
power, knowledge,
or resources to affect the policy content, while others are
relatively powerless but
nevertheless are affected, sometimes in dramatic ways (Brugha
and Varvasovszky
2000). Thus, open and evenhanded stakeholder engagement,
especially among those
with conflicting viewpoints, can sometimes resolve differences
and build trust in the
policy-making process and help secure public acceptance of
decisions (e.g., Klievink
et al. 2012).
In the past 20 years, specialized technologies, electronic
communication, and
advanced analytical, modeling, and simulation techniques have
been developed to
support governance processes. Administrators, analysts, and

planners must decide
how and when to engage citizens and stakeholders in
governance, particularly during
the different stages of policy making. They must also consider
which mechanisms
[email protected]
to use for managing the relationships (Bryson 2004) and must
select from a variety
of tools and techniques. In this chapter, we begin to explore two
questions: (1)
What types of engagement tools and processes are useful for
different stakeholders
and contexts? And (2) what factors support the effective use of
particular tools and
technologies toward constructive outcomes?
The next sections start by reviewing the foundational elements
of stakeholder the-
ory and its relation to governance, including a summary of tools
and techniques used
to identify stakeholders and analyze stakeholder interests and
ways to classify types
of engagement. We then offer five case stories of stakeholder
engagement in complex
and dynamic settings across the world including e-government
strategic planning in a
developing country, exploring different uses of evidence in
early childhood decision
making, developing technology and policy innovations in global
trade, and involving

citizens in the design of energy policy and transportation
planning. The cases vary in
both policy content and the extent to which newer technologies
were used to deal with
the complexity of the engagement process, their accessibility
and understandability
to outsiders, and the advantages and disadvantages they offer to
expert stakeholders
as compared to laymen. We then compare the cases, discuss
their similarities and
differences, and conclude with a discussion of the usefulness of
different tools and
processes for different stakeholders and contexts and the factors
that support their
effectiveness.
9.2 Foundations of Stakeholder Engagement
Stakeholder engagement, as a concept, originated within
organizational studies as an
approach to managing corporations (Freeman 2010; Bingham et
al. 2005; Donald-
son and Preston 1995; Mitchell et al. 1997). This approach has
since been adapted
for use by public sector organizations to highlight the
importance of stakeholders in
various aspects of the policy-making process (Bingham et al.
2005). Bingham et al.
(2005) situate stakeholders as part of “new governance”
concepts where government
actively involves citizens as stakeholders in decision making
through activities such
as deliberative democracy, participatory budgeting, or
collaborative policy making.
Research on stakeholder inclusion in government processes has
been found to en-

hance accountability, efficiency in decision-making processes,
and good governance
(Ackerman 2004; Flak and Rose 2005; Yetano et al. 2010). The
growing popularity
of stakeholder analysis reflects an increasing recognition of
stakeholder influences
on decision-making processes (Brugha and Varvasovszky 2000).
9.2.1 Defining Stakeholders
The term “stakeholder” is defined differently by different
disciplines. Most defini-
tions mention similar stakeholder categories such as companies
and their employees
[email protected]
or external entities such as suppliers, customers, governments,
or creditors. In the
public sector, the definition of stakeholder emphasizes
categories of citizens defined
by demographic characteristics, life stages, interest groups, or
organizational bound-
aries (Bingham et al. 2005; Ackerman 2004; Yetano et al.
2010). Stakeholders can
be both internal to the government (e.g., the government
organizations responsible
for policy implementation) and external to it (e.g., the
industries, communities, or
individuals to be affected by government actions or rules).
In this chapter, we use Freeman’s (1984) definition of

stakeholder as any group
or individual who can affect or is affected by the achievement
of an organization’s
objectives. In the public sector, “organization” is understood to
mean a government
entity or body with responsibility for public policies or
services. In the simplest terms,
those who can affect or may be affected by a policy can be
considered stakeholders
in that policy. In traditional expert-based approaches to policy
making, the needs of
stakeholders are indirectly addressed by public agencies and
acknowledged experts
(Bijlsma et al. 2011; De Marchi 2003). In these expert-based
approaches, internal
and external stakeholders may be consulted, but in participatory
approaches, stake-
holders are not only consulted but are also involved in a
structured way to influence
problem framing, policy analysis, and decision making. Bijlsma
et al. (2011) define
participatory policy development as the “influence of
stakeholder involvement on the
development of substance in policy development, notably the
framing of the policy
problem, the policy analysis and design, and the creation and
use of knowledge”
(p. 51).
9.2.2 Stakeholder Identification and Analysis
Stakeholder identification and analysis is an important first
phase in stakeholder en-
gagement processes (Freeman 2010). Analysis typically
involves five steps (Kennon
et al. 2009): identifying stakeholders, understanding and

managing stakeholders,
setting goals, identifying the costs of engagement, and
evaluating and revisiting the
analysis. Through these various steps, an analysis helps to
distinguish stakeholders
from non-stakeholders and to identify the ways that
stakeholders need to be engaged
during different parts of the policy cycle. Over time, the mix of
stakeholders in a
particular policy issue is likely to change, as new stakeholders
may join the engage-
ment activities, while others may drop out (Elias et al. 2002) or
shift among different
types. Joining, dropping out, or moving among types thus
dynamically changes the
configuration and analysis of stakeholders over time.
Various techniques for stakeholder identification and analysis
are reviewed in
the literature. These techniques focus attention on the
interrelations of groups or
organizations with respect to their interests in, or impacts on
policies within, a
broader political, economic, and cultural context. These
techniques also provide
ways for analysts to understand stakeholder power, influence,
needs, and conflicts
of interest. Bryson (2004) characterized stakeholder
identification as an iterative
process highlighting the need to determine the purpose of
involving stakeholders
[email protected]

and cautioning that these purposes may change over time. He
describes a stage
approach to selecting stakeholders: someone or a small group
responsible for the
policy analysis develops an initial stakeholder list as a starting
point for thinking
about which stakeholders are missing. Brainstorming and the
use of interviews,
questionnaires, focus groups, or other information-gathering
techniques can be used
to expand the list. Bryson (2004) notes “this staged process
embodies a kind of
technical, political, and ethical rationality” (p. 29). He also lists
a variety of analysis
techniques, such as power and influence grids (Eden and
Ackermann 1998), bases of
power diagrams (Bryson et al. 2002), stakeholder–issue
interrelationship diagrams
(Bryant 2003), problem-frame stakeholder maps (Anderson et
al. 1999), ethical
analysis grids (Lewis 1991), or policy attractiveness versus
stakeholder capability
grids (Bryson et al. 1986). Each of these tools is used in
different situations to help
understand and identify various aspects of stakeholder interests.
9.2.3 Stakeholder Engagement
Stakeholder engagement methods are the means by which
stakeholder views, infor-
mation, and opinions are elicited, or by which stakeholders are
involved in decision
making. Engagement can take various forms. The International
Association for Pub-

lic Participation identified five levels of stakeholder
engagement: (IAP2 2007). At
the simplest level, informing, stakeholders are merely informed,
for example, via
websites, fact sheets, newsletters, or allowing visitors to
observe policy discussions.
The level of engagement in this form is very low and suitable
only to engage those
stakeholders with low urgency, influence, importance, or
interest (Bryson 2004).
Various methods are available for consulting, including
conducting interviews, ad-
ministering surveys to gather information, opening up draft
policy documents for
public comment, or using Web 2.0 tools to gather ideas. The
main goal of this form
of engagement is to elicit the views and interests, as well as the
salient information
that stakeholders have with regard to the policy concern.
Involving stakeholders is a more intensive engagement where
stakeholders work
together during the policy development process. Some tools
used to ensure that ideas,
interests, and concerns are consistently understood and
addressed include scenario
building (Wimmer et al. 2012), engaging panels of experts such
as the Delphi method
(Linstone and Turoff 1975), or group model building that
includes simulating policy
choices, games, or role playing (Andersen et al. 2007; Vennix et
al. 1996). Models,
simulations, or scenarios can be used as boundary objects
(Black and Andersen
2012; Star and Griesemer 1989) to enable diverse sets of
stakeholders to have a

shared experience and to exchange localized or specialized
knowledge in order to
learn, create common understanding, and identify alternative
choices. All these levels
focus on the flow of information among actors, but the direction
and intensity vary.
The most intense engagement is realized through full
collaboration with or even
empowerment of stakeholders. In the IAP2 spectrum of public
participation, collab-
oration means stakeholders’ advice and recommendations will
be incorporated in
[email protected]
the final decisions to a maximum extent (IAP2 2007).
Empowerment means that the
final decision making is actually in the hands of the public.
Realistically, collabora-
tion and empowerment exist within institutional and legal
parameters. For example,
the policy-making body (usually a government agency) will
need to put some con-
straints or boundaries around the policy options that comport
with the limits of its
legal authority. For both levels, consensus-building approaches
are essential. This
can be done through citizen juries (Smith and en Wales 2000),
the enactment of a
stakeholder board (urbanAPI1; Klievink et al. 2012), or by
setting up living labs

(Tan et al. 2011; Higgins and Klein 2011) in which stakeholders
collaboratively
develop, implement, and evaluate solutions within a given
context. All of these
approaches not only assist in incorporating stakeholders’ views
into the policy pro-
cess but also enhance acceptance by stakeholders because they
were part of the
deliberation process (e.g., see Klievink and Lucassen 2013).
9.3 Cases
Below we offer five case stories about stakeholder engagement
in policy making.
The cases were recommended by a diverse set of eGovPoliNet
consortium partners
who shared an interest in tools and techniques to support the
policy process. The
main goal of the case stories is to highlight the roles that
stakeholders can play in
policy development and to discuss how different methods, tools,
and technologies
could be used for engaging stakeholders in the policy process.
Each case describes a
situation where stakeholders were involved in the problem
definition, agenda setting,
and formulation stages of the policy cycle. In all cases, a
trusted third party, generally
university researchers, facilitated the process and applied the
tools. The cases vary in
policy content and in the extent of technology use in the
engagement process. They
represent different policy domains, and governments at different
stages of develop-
ment with different political systems. The first three cases focus
on substantive policy

choices for e-government strategic planning, alternative energy
policy, and global
trade inspection. The last two concentrate on stakeholder
involvement in improving
tools to support the policy-making process. Of those, the first
focuses on connect-
ing policy makers and modelers in building a supportive
framework for assessing
early childhood programs and second involves stakeholders in
defining assessment
indicators to be built into a model that supports urban planning
decisions.
In this section, we describe these diverse situations as the
foundation for the
comparison presented in Sect. 9.4 where we identify similarities
and differences that
suggest approaches, tools, and techniques that are useful and
effective in different
contexts and with different kinds of stakeholders.
For each case below, we present the key characteristics of the
policy-making situ-
ation and assess the purpose of stakeholder engagement. With
respect to stakeholder
1 UrbanAPI is an EC FP7 project focused on interactive
analysis, simulation, and visualization
tools for agile urban policy implementation
http://www.urbanapi.eu/.
[email protected]

identification and analysis, we cover both the identification of
stakeholders (types)
involved and the methods used for identification and analysis.
With respect to stake-
holder engagement (see Sect. 9.2.3), we analyze the engagement
approach followed
in each case, as well as the type of participation and the
methods of stakeholder en-
gagement. We also inventory which tools and technologies were
used and describe
the results and outcomes of each engagement process.
9.3.1 E-Government Strategic Planning in Afghanistan
The EGOV.AF project was a joint initiative of the Afghanistan
Ministry of Commu-
nications and Information Technology (MCIT) and the United
Nations University–
International Institute for Software Technology–Center for
Electronic Governance
(UNU-IIST-EGOV). One goal of EGOV.AF was to develop a
nationally owned
EGOV strategy and program (Dzhusupova et al. 2011). In many
developing countries,
two major challenges to long-term sustainability of e-
government initiatives exist:
(1) too much reliance on donor funding (Ali and Weerakkody
2009) and (2) lack
of understanding regarding citizen demand for e-government
services (Basu 2004).
To mitigate these challenges, a strategy of the EGOV.AF
project was to reach out
to stakeholders in a systematic way before putting together a
national e-government
policy. Afghanistan is one of the poorest countries in the world

(World Bank 2012)
plagued by a recent history of war and conflict, with a
significant digital divide
between rural and urban areas. Thus, identifying important
stakeholders and under-
standing their interests, expectations, capacity, and influence
were very important,
but also very difficult.
In 2011, the UNU-IIST-EGOV team engaged in action research
with the MCIT
through the development of a stakeholder analysis tool and
execution of a series of
stakeholder identification exercises, analyses, and workshops.
The MCIT was the
project owner and lead agency, while the UNU-IIST-EGOV
provided mentorship,
additional experience, expertise to apply stakeholder analysis
tools and engagement
methods, and capacity to facilitate the process.
Historically, standard exercises at the MCIT around e-
government planning had
focused only on consultation with technology stakeholders, such
as consulting com-
panies. Initially, the MCIT did not see the value in involving
citizens, local provinces,
international organizations, academics, or nonprofit
organizations that focus on gov-
ernance. The case was made by UNU to engage people outside
of government to
address several factors: Many of the nonprofit organizations are
advocates for trans-
parency and good governance, donor organizations assert
influence over the process
through special programs and funding, and the provincial

governments work closely
and most directly with citizens.
To expand MCIT’s limited understanding of this broad set of
stakeholders, they
conducted a series of consultation and involvement activities.
The first instance of
engagement with stakeholders was a survey that asked questions
about their inter-
ests, needs, activities, and conditions. The team also collected
additional contextual
[email protected]
information from websites and professional contacts. The
second stage of engage-
ment occurred after the analysis of the survey. Using the
stakeholder analysis tool
developed by UNU, the MCIT identified from the survey results
a set of interested
and relevant stakeholders, defined the roles for major
stakeholders in the policy pro-
cess, and developed communication strategies. Later these
stakeholders were invited
to attend two stakeholder workshops. One workshop was
designed as a “visioning”
exercise and another designed to elicit “strategy development.”
During the work-
shops, MCIT and UNU-IIST-EGOV were able to provide
participants with general
knowledge about approaches and methodologies regarding
strategy development,

provided examples from other countries, and facilitated
discussions focused on e-
government in the local Afghanistan context. Participants in the
workshops were
encouraged to share their ideas and to discuss and prioritize
strategic goals and tasks
for e-government based on the mutual consensus among them.
The last stage of
the stakeholder engagement was to complete a series of face-to-
face meetings and
e-mails in which the MCIT collected suggestions on strategic
actions. Additional
feedback was taken through an e-forum set up on the
government website to collect
comments on a draft national strategy.
The key result of the overall project was the successful
completion of a nationally
owned EGOV vision and strategy agreed upon by most
important stakeholders. The
most critical points of the vision and strategy were to better
respond to Afghan
citizens’ expectations that e-government would bring
convenient public services,
transparency, accountability, and responsiveness and would help
to deter widespread
corruption. The project provided evidence that stakeholder
engagement in national-
level planning processes was possible, and that involving
stakeholders can increase
commitment, build consensus, and demonstrate transparency
and openness in the
strategic e-government planning process.
9.3.2 Renewable Energy Policy for Kosice, Slovakia

The process of developing an energy policy in Kosice self-
governing region (KSR)
in Slovakia is surrounded by political, economic, and
environmental challenges.
High dependency on imported energy from Russia and Ukraine,
presented KSR
with economic and political vulnerabilities. The emergence of
domestic small to
medium enterprises (SMEs) within the energy sector has
provided new opportunities
for employment and new technologies for utilizing local energy
sources. Control
of energy production with respect to emissions also impacted
the policy-making
environment. Any change in the sources of energy would likely
affect the pricing of
energy consumption and directly affect citizens and businesses.
This case not only
is a matter for policy makers and the authorities devising new
energy policies but
also affects the KSR government entities, energy importing
companies, local SMEs,
and citizens. Creating a new policy in such an environment
required considerations
of a wide variety of stakeholders; the goal was to ensure the
new policy would be
realistic, supported, and agreed upon.
[email protected]
This case describes a pilot of the Open Collaboration for Policy
Modeling

(OCOPOMO) project.2 The main objective of the OCOPOMO
project was to de-
velop an online environment for, and information and
communications technology
(ICT) tools for, policy modeling in collaboration with
stakeholders (Wimmer et al.
2012). Presenting complex information on policy choices for
renewable energy re-
quires some technical expertise and is influenced by individual
beliefs. The pilot
project in Kosice focused on capturing stakeholders’ views on
alternative renewable
sources of energy versus traditional energy production and
consumption. It provided
an understanding of various choices in relation to different
policies for promoting the
use of renewable energy, the perceived market potential for
different energy sources,
barriers hindering different kinds of energy generation in the
region, and the moti-
vating factors leading citizens and companies towards
renewable energy sources. It
also provided an early understanding of employment, financial,
and environmental
impacts of any potential policy (Furdík et al. 2010). This pilot
was the first time that
Kosice used advanced ICTs in policy making and the first time
the region involved
a range of stakeholders other than policy makers, experts, and
key representatives
from private heat producers and distribution companies.
The project team met with regional government committees and
identified and
analyzed relevant stakeholders ranging from heating producers
to distribution compa-

nies, building construction experts to technology experts, to
household associations,
citizens, and city employees. Desk research and surveys were
used to identify the
stakeholders, their roles, and expectations in the engagement
process. The local au-
thorities were mainly responsible for identifying the
stakeholders. The project team
and the local government applied action research to engage
these stakeholders in
the process and involvement was by invitation only. Several
methods of engagement
were used. Workshops were used to clarify tasks and
expectations of stakeholders in
the engagement process. Collaborative scenario development
enabled stakeholders to
provide evidence documents and to generate scenarios related to
the policy problem.
This method also allowed stakeholders to collaborate among
themselves by exchang-
ing views and concerns about the policy problem and possible
solutions. Conceptual
modeling transformed stakeholder-generated scenarios and
evidences into formal
policy models for simulation and then transformed the model-
based scenarios into
narrative scenarios to enable understanding of simulation results
to stakeholders and
steer further collaboration on the results. This process was
iterative as new scenarios
emerging from the discussions of results could be evaluated and
simulated again.
The stakeholders first met with the project team and were given
a tutorial of how
the OCOPOMO online platform is used and they were free to

use the platform for
about 1 month. The online platform provided background and
supporting materials
to inform stakeholders of the different policy options available.
After reviewing
existing options, stakeholders could propose several scenarios—
for example, they
could propose a type of renewable energy and discuss what
should be done from
the stakeholder’s own perspective. Scenarios, based on these
stakeholder proposals,
2 http://www.ocopomo.eu/in-a-nutshell/piloting-cases/kosice-
self-governing-region-slovakia.
[email protected]
were later turned into formal policy models for simulation. The
consistent conceptual
description (CCD) tool was used to perform this task.
The next phase began almost 1 year later with another face-to-
face meeting to
inform stakeholders of the purpose of the second iteration.
Given the length of
time between the first exercise and the second, some
stakeholders were involved
in the first face-to-face one but not the second, and some started
in the second. In
the second iteration, stakeholders were presented with
simulation results of their
policy choices. Additional background documents were

provided to help educate
them such as a return on investment (ROI) of different energy
sources proposed.
Stakeholders, particularly policy owners, provided comments on
the model-based
scenarios and then published one new evidence-based scenario.
The topics which
were most discussed leading to the new scenario included
detailed technical pros
and cons of a local versus central heating system, ROIs,
legislation proposed by
heat producers that would affect customers who decided to
disconnect from the
central heating system, and financial tools for investments in
building renovation or
installation of new heat sources.
The project was successful in highlighting the need for and
usefulness of more
innovative approaches to policy development processes. These
innovative approaches
proved to be particularly important with diverse stakeholders
with different interests
in an existing problem and potential solutions (Wimmer et al.
2012). The added
value of OCOPOMO to traditional approaches is the added
confidence for policy
makers about the expected outcomes of a policy in respect to
stakeholders involved.
Moreover, the stakeholder engagement process in Kosice was
positively viewed by
the stakeholders themselves. It enabled better understanding of
the policy problem
through background documents provided in the platform, and it
also provided a tool
where different stakeholders’ views and expectations could be

explicitly captured.
9.3.3 Redesigning the European Union’s Inspection Capability
for International Trade
The European Union (EU) is implementing a risk-based
approach (RBA) policy
to government supervision of international trade lanes. As part
of this approach,
the risk posed by cargo entering and leaving the EU is analyzed
on the basis of
cargo information submitted electronically in a single
declaration by operators prior
to departure or arrival. However, this policy can only be
effective if the data that
circulate among the supply chain partners are accurate, timely,
and of sufficient
quality to be relied upon, which is currently not the case
(Hesketh 2010). This case
draws from two projects: Extended Single Window (ESW):
Information Gateway
to Europe, funded by the Dutch Institute for Advanced Logistics
(DINALOG), and
common assessment and analysis of risk in global supply chains
(CASSANDRA),
funded by the 7th Framework Program of the European
Commission. The goal of
both projects was to improve supply chain visibility.
[email protected]
Transparency is important for both government and commercial

interests; it re-
lates to having access to the transaction data necessary to know
what is actually
happening in the supply chain. However, major challenges exist
in today’s global
supply chains, including lack of trust and understanding
between public and private
entities and among private entities (Klievink et al. 2012) about
existing laws and
ways of working among EU countries and other countries.
Without the involvement
of international trading businesses and other stakeholders, and
without their active
contribution to data sharing solutions that enable the RBA
policy, the policy will not
lead to the intended results for government and may lead to
unnecessary increases
in the administrative burden of legitimate traders.
To overcome these challenges, the project team assembled an
international consor-
tium of government bodies that included multiple European
customs organizations,
in addition to universities, IT providers, logistics operators, and
standardization bod-
ies. The project team conducted desk research and a survey
based on Bryson (2004)
to elicit stakeholders’ interests, urgency, influence, and
importance. The total number
of entities involved in international supply chains is so large
that it was necessary to
choose stakeholders that would reasonably represent the range
of actors. Therefore,
selection was based on criticality and representativeness. For
example, the con-
sortium involved representatives of a several very large and

medium-sized freight
forwarders. This was done to ensure different perspectives
within this stakeholder
group without having to involve the hundreds of parties that can
be involved with the
cargo on any single ship. Stakeholders were also grouped
according to trade lanes.
This approach limited the total number of actors by using the
trade lane as a boundary.
To ensure diversity in interests, ten different global trade lanes
were modeled, in-
cluding lanes between Shenzhen (China) and Felixstowe (UK),
Penang (Malaysia)
and Rotterdam (the Netherlands), Alexandria (Egypt) and
Barcelona (Spain), and
Bremerhaven (Germany) and Charleston (USA). Using this
method, the stakehold-
ers were able to see the common themes across trade lanes that
are important for
each of the key stakeholder groups.
In order to engage stakeholders to innovate within a real-life
setting, a living lab
approach was used. Tan et al. (2011) describe a living lab
methodology as bringing
together multiple stakeholders, across multiple locations, and
seeing stakeholders
as co-innovators. A living lab methodology is suitable for
situations where a neutral
party, often academics, acts as honest brokers to bring the
different stakeholders to
consensus. Each living lab group used real trade lanes to model
the physical flow
of data, information system landscape, and administrative
burden in order to config-
ure, demonstrate, and refine the entire system with the

stakeholders. The consortium
team created visual models and data-flow diagrams of the
existing and to-be situa-
tions to enable the stakeholders to sort out the policy and data-
sharing issues among
themselves. Another goal was for stakeholders to come to
common understanding of
their respective situations, ultimately joining up different
systems of different stake-
holders in order to capture the data they collectively needed.
The overall dataset was
visualized in a dashboard with role-based access. The dashboard
enabled discussion
of how the system would impact the day-to-day processes of the
various businesses
and inspection authorities.
[email protected]
Involving stakeholders early helped increase commitment and
consensus to this
initiative. However, decision making remained relatively slow
due to the consider-
able time it takes to design technical tools, models, and
diagrams, and to constantly
update them to reflect the feedback from stakeholders’ advice
and recommendations.
By providing a comprehensive overview of the roles, the data
sources, and the work
processes using them, parties came to an understanding of how
the innovations were
used. Through this, they over time build trust towards those

potential vulnerabilities
that the innovation might bring, would not be exploited. This
facilitated acceptance
and uptake by the various stakeholder groups. In addition, not
all of the potential
answers the living lab groups provided are also enabled by
existing European legis-
lation. Alignment between the business stakeholder groups,
national governments,
and European bodies is still needed. One of the outcomes of the
project is therefore
a consensus-based agenda for further policy development.
9.3.4 Understanding Child Health Outcomes in New Zealand
The next case examines the Modelling the Early Life-Course
(MEL-C) project in
New Zealand, which was supported by a public good research
grant provided to
researchers at the University of Auckland, New Zealand (Milne
et al. 2014). Life-
course studies examine “the biological, behavioral and
psychosocial pathways that
operate across an individual’s life course, as well as across
generations, to influence
the development of chronic diseases” (Ben-Shlomo and Kuh
2002). An abundance
of research evidence can be found about the early life course of
children and the
determinants of health. The goal of the project was to develop a
decision support
software tool for policy makers to test different policy scenarios
against realistic
data and to consider this evidence alongside other policy-
relevant information such
as politics, other evaluations, or expert consultations. The main

purpose was not to
develop a specific policy but to develop a process and tool for
better identification
and use of data in this policy domain.
In an environment where a great deal of information about a
policy exists, the tool
is meant to help bridge the research–policy translation gap
(Milne et al. 2014). The
lack of research evidence uptake by policy makers is well
documented (Lomas 2007;
Van Egmond et al. 2011). One main factor is the lack of uptake
in the “translation
gap”—characterized as the mismatch between the knowledge
that research produces
and the knowledge that policy makers want (Milne et al. 2014).
Milne et al. (2014)
identify two solutions to bridge the gap—knowledge brokers
(Frost et al. 2012;
Knight and Lightowler 2010; Lomas 2007) and research–policy
partnerships (Best
and Holmes 2010; Van Egmond et al. 2011). Knowledge brokers
act as translators,
turning the research evidence into information that is easily
understood and usable
by policy makers. Research–policy partnerships involve a more
intense interaction
between both groups, where they work together to produce the
evidence needed for
policy purposes. Previous work focused on database
interventions aimed at knowl-
edge translation where all relevant documents synthesizing
research results could be
[email protected]

found (Milne et al. 2014). However, with the online databases
the onus is still on
policy makers to search for relevant papers, assess their content
for relevance, and
evaluate their importance for the policy question under
consideration. The MEL-C
project took a different approach with a decision-support tool
“where the evidence
is embedded in a working model and can be interrogated to
address specific policy
questions” (p. 8).
Using a micro-simulation model, the tool incorporates
longitudinal data to
determine the normal transition of children through their life
course and the im-
pact of policy interventions on their outcomes. Two
representatives each from
four New Zealand government ministries—Health, Education,
Justice, and Social
Development—formed a “policy reference group” for the
project (Milne et al. 2014).
The representatives were selected because they represented
people who could under-
stand the aims of the project and were data and technology
savvy. Thus, the boundary
for engagement was limited to the translation gap, and did not
extend to the behavior
of the children modeled within the system. The main strategy
for involving policy
makers was to hold regular, face-to-face meetings for almost 2
years to discuss the

development of the MEL-C tool, including the simulation model
and graphical user
interface. The discussions were facilitated and documented by
the task leader for
end-user engagement.
The simulation model was shown to stakeholders who then
provided feedback and
became collaborators in the development of user interfaces and
the types of key policy
questions that the model needed to be able to address. The
results of this specialized
form of stakeholder engagement included a much more useful
decision-support tool
than might have been developed otherwise, an ongoing process
of collaborative
refinement, and a set of potential users and advocates for the
tool.
Results of the model are beginning to be explored. For example,
for child health
service use outcomes it was found that appreciable improvement
was only effected
by modifying multiple determinants; structural determinants
(e.g., ethnicity, family
structure) were relatively more important than intermediary
determinants (e.g., over-
crowding, parental smoking) as potential policy levers; there
was a social gradient
of effect; and interventions bestowed the greatest benefit to the
most disadvantaged
groups with a corresponding reduction in disparities between
the worst-off and the
best-off (Lay-Yee et al. 2014).
9.3.5 Transportation and Urban Planning Indicator Development

in the USA
Understanding how choices today will impact life in the future
is a major concern
for policy making in any area. In transportation and urban
planning, it is even more
important because the infrastructure created is not easily
changed, once roads and
buildings are built and patterns of living start to evolve around
them. The urban plan-
ning context is fraught with different stakeholders who often
have fundamentally
opposing beliefs and value systems (Pace 1990; Borning et al.
2005). They embody
[email protected]
widely divergent opinions regarding urban development and
land use. Each stake-
holder group is likely to have their own philosophies about
different forms of land
use in urban environments, and different views about how long-
term planning should
occur, what situations constitute problematic conditions, what
solutions should be
sought for those problems, and what constitutes successful
outcomes.
Under these contentious conditions, advanced computer
simulation tools that
show the long-term potential effects of different choices can
contribute to legitimation

of the policy process as well as to well-considered decisions.
However, in order to
achieve this, the model itself must be considered legitimate. In
other words, its
structure, inputs, processes, and outputs must be transparent and
understandable to
all stakeholders. Our last case, UrbanSim, is a land-use
modeling system, developed
over the past 20 years, that helps policy makers and
stakeholders understand the
20–30-year impacts of different choices regarding land use and
transportation on
community outcomes including effects on the economy and the
environment. It has
been used widely in the USA and Europe and is of growing
interest globally. The
system not only estimates the direct effects of different
infrastructure and policy
choices but also estimates how individual and group responses
to those choices will
affect the outcomes (Borning et al. 2005; Borning et al. 2008).
UrbanSim simulation results are mainly presented to users as
indicators. These
indicators are variables that convey information about an
attribute of the system at a
given time. Indicators in UrbanSim include such variables as
the population density
in different neighborhoods, the ratio of car trips to bus trips for
the region, and
the projected cost of land per acre in different parts of the
region. These and other
indicators are presented under different possible scenarios over
the course of the
full simulation, generally 30 years. Indicator values are
presented in tables, graphs,

charts, or maps (Friedman et al. 2008). These indicators allow
stakeholders to assess
and compare the results of different policy scenarios on a
consistent set of dimensions.
For example, if a city has the goal of supporting more walkable
densely populated
urban neighborhoods as an alternative to sprawl surrounding the
city center, then
changes in the “population density” indicator in different
neighborhoods could be
used to assess the simulated outcomes of different policies over
time (Borning et al.
2005).
In recent years, enhancements to UrbanSim have concentrated
on making the
model more realistic and meaningful to stakeholders by
expanding, categorizing,
and differentiating the stakeholder values represented by the
indicators. The Urban-
Sim team had two goals: to make advocacy for different views
more explicit and
contextualized, and to improve the overall legitimacy of the
system by incorporating
these values in a wider range of indicators in the simulations.
The involvement of
stakeholders, essentially a process of codevelopment of the
model, was guided by
an overarching theory of value sensitive design (Friedman
1997). A key feature of
value sensitive design is designing technology that accounts for
human values with
an emphasis on representing direct and indirect stakeholders
(Borning et al. 2005).
The UrbanSim team partnered with three local organizations in

the Seattle, Wash-
ington, region to develop and test new ways of expressing their
values to model users
through the choice of indicators and related technical
information. The partners (a
[email protected]
government agency, a business association, and an
environmental group) were se-
lected to represent a range of known issues and stakeholder
views about development
in the region. The goal was to create for each group a narrative
value indicator per-
spective that explained the values of most importance to that
group and to select,
define, and incorporate key indicators representing those views
in the model. Stake-
holders were convened in separate groups so that they could
work independently to
formulate their indicator perspectives. This was an important
design choice because
the goal was to present each group’s values and desires by
essentially telling a story
advocating particular values and criteria for evaluating policy
outcomes (Borning
et al 2005). The team engaged each stakeholder group through a
series of face-
to-face meetings and semi-structured interviews to help them
craft and write both
narratives and descriptions of indicators that closely matched
their core values and

views.
To assess the extent to which these approaches enhanced the
legitimation of the
model, a separate group of citizen evaluators reviewed each
grouping of stakeholder-
selected indicators and along with associated technical
documentation as well as the
indicators in the system as a whole. They considered coherence,
informativeness,
usefulness for supporting diverse opinions, usefulness for
advocating for differing
views and values, and usefulness for supporting the democratic
process. The evalu-
ation showed positive scores on all measures and also produced
additional findings
about the usefulness of different kinds of information (technical
compared to advo-
cacy), the importance of explicitly presenting and balancing
diverse views, and the
overall perception of transparency and lack of bias in the
modeling system itself.
9.4 Case Comparison
Table 9.1 presents key elements of each case story based on the
following points
of comparison: (a) situation and approach, (b) types of
stakeholders and type of
participation, (c) methods for stakeholder identification, (d)
methods for stakeholder
engagement, (e) tools and technologies used, and (f) results.
[email protected]

T
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pi
ng
D

ev
el
op
ed
D
ev
el
op
ed
L
ev
el
of
go
ve
rn
m
en
t
N
at
io
na
l

M
un
ic
ip
al
M
ul
ti
na
ti
on
al
N
at
io
na
l
R
eg
io
na
l
A
pp

ro
ac
h
A
ct
io
n
re
se
a
rc
h
—
in
vo
lv
in
g
tr
us
te
d
3r
d

pa
rt
y
fa
ci
li
ta
te
s
ne
w
co
nn
ec
ti
on
s
be
tw
ee
n
st
ak
eh

ol
de
rs
an
d
go
ve
rn
m
en
t
A
ct
io
n
re
se
a
rc
h
—
in
vo

lv
in
g
co
ll
ab
or
at
iv
e
sc
en
ar
io
bu
il
di
ng
th
ro
ug
h
an
on

li
ne
to
ol
,
su
pp
le
m
en
te
d
w
it
h
in
-p
er
so
n
m
ee
ti
ng

s
A
ct
io
n
re
se
a
rc
h
—
cr
ea
ti
on
of
a
li
vi
ng
la
b
w
he

re
st
ak
eh
ol
de
rs
th
em
se
lv
es
,f
ac
il
it
at
ed
by
3r
d
pa
rt
ie

s,
de
ve
lo
pe
d
so
lu
ti
on
s
an
d
im
pl
em
en
t
th
em
R
es
ea
rc

h
–
p
ra
ct
ic
e
p
a
rt
n
er
sh
ip
—
re
se
ar
ch
er
s
an
d
po

li
cy
m
ak
er
s
w
or
ke
d
to
ge
th
er
th
ro
ug
h
it
er
at
iv
e
di

sc
us
si
on
,
de
m
on
st
ra
ti
on
,a
nd
en
ha
nc
em
en
ts
A
ct
io
n

re
se
a
rc
h
—
us
in
g
va
lu
e-
se
ns
it
iv
e
de
si
gn
w
he
re
st

ak
eh
ol
de
r
va
lu
es
ar
e
m
ad
e
ex
pl
ic
it
in
th
e
co
de
ve
lo

pm
en
t
of
en
ha
nc
em
en
ts
to
th
e
te
ch
no
lo
gy
an
d
m
od
el
sy

st
em
P
ur
po
se
of
st
ak
eh
ol
de
r
en
ga
ge
m
en
t
E
ns
ur
e

ow
ne
rs
hi
p,
co
m
m
it
m
en
t,
an
d
tr
an
sp
ar
en
cy
,i
n
pu
rs

ui
t
of
b
a
la
n
ci
n
g
st
a
ke
h
o
ld
er
s’
in
te
re
st
s
B

u
il
d
co
n
se
n
su
s
to
su
pp
or
t
a
re
al
is
ti
c
po
li
cy
th

at
w
ou
ld
be
w
id
el
y
ac
ce
pt
ed
A
tt
un
e
th
e
sy
st
em
to
w

ar
ds
th
e
in
te
re
st
s
an
d
ex
is
ti
ng
pr
ac
ti
ce
s
of
th
e
st

ak
eh
ol
de
rs
,t
he
re
by
bu
il
d
in
g
co
m
m
it
m
en
t
a
n
d

su
p
p
o
rt
in
g
co
n
se
n
su
s
a
m
o
n
g
st
a
ke
h
o
ld

er
s
to
n
ew
p
o
li
ci
es
F
a
ci
li
ta
te
sy
n
th
es
is
o
f
re

se
a
rc
h
fi
n
d
in
g
s
a
n
d
im
p
ro
ve
th
e
u
se
fu
ln
es

s
an
d
u
sa
b
il
it
y
o
f
a
d
ec
is
io
n
-s
u
p
p
o
rt
to

o
l
fo
r
po
li
cy
m
ak
er
s
E
n
h
a
n
ce
th
e
le
g
it
im

a
cy
o
f
a
m
o
d
el
in
g
sy
st
em
us
ed
in
co
nt
en
ti
ou
s
po

li
cy
ar
ea
s
[email protected]
T
ab
le
9.
1
(c
on
ti
nu
ed
) C
as
e
1
C

as
e
2
C
as
e
3
C
as
e
4
C
as
e
5
S
ta
ke
ho
ld
er
ty
pe
s

in
vo
lv
ed
R
ep
re
se
nt
at
iv
es
fr
om
ce
nt
ra
l
go
ve
rn
m
en
t,

lo
ca
l
go
ve
rn
m
en
ts
,
pu
bl
ic
se
rv
ic
e
pr
ov
id
er
s,
IT
an

d
co
ns
ul
ti
ng
fi
rm
s,
N
G
O
s,
un
iv
er
si
ti
es
,
th
in
k
ta

nk
s,
re
so
ur
ce
ce
nt
er
s;
in
te
rn
at
io
na
l
or
ga
ni
za
ti
on
s

(d
on
or
s
an
d
sp
on
so
rs
)
P
ol
ic
y
m
ak
er
s,
re
pr
es
en
ta

ti
ve
s
fr
om
en
er
gy
-r
el
at
ed
co
m
pa
ni
es
,e
xp
er
t
gr
ou
ps

,r
ep
re
se
nt
at
iv
es
fr
om
ci
ti
ze
ns
an
d
ho
us
in
g
as
so
ci
at

io
ns
In
vo
lv
em
en
t
of
“e
xe
m
pl
ar
y”
ac
to
rs
fr
om
m
ai
n
st

ak
eh
ol
de
r
gr
ou
ps
:
go
ve
rn
m
en
t,
in
te
rn
at
io
na
l
tr
ad

er
s,
IT
so
lu
ti
on
pr
ov
id
er
s,
st
an
da
rd
s
or
ga
ni
za
ti
on
s

E
xp
er
t
gr
ou
p
dr
aw
n
fr
om
pu
bl
ic
ag
en
ci
es
re
sp
on
si
bl

e
fo
r
ch
il
dr
en
’s
he
al
th
R
ep
re
se
nt
at
iv
es
of
se
le
ct

ed
no
np
ro
fi
t,
go
ve
rn
m
en
t,
an
d
bu
si
ne
ss
in
te
re
st
s
kn

ow
n
to
ha
ve
st
ro
ng
vi
ew
s
of
de
ve
lo
pm
en
t
in
th
e
re
gi
on

M
et
ho
d
fo
r
id
en
ti
fy
in
g
st
ak
eh
ol
de
rs
O
nl
in
e
su
rv

ey
s;
in
te
rv
ie
w
s;
an
al
ys
is
of
in
te
re
st
s,
ne
ed
s,
an
d
ca

pa
bi
li
ti
es
D
es
k
re
se
ar
ch
,s
ur
ve
y
re
se
ar
ch
,q
ua
li
ta

ti
ve
an
d
qu
an
ti
ta
ti
ve
da
ta
an
al
ys
is
.F
ac
e-
to
-f
ac
e
m

ee
ti
ng
s
D
et
ai
le
d
st
ak
eh
ol
de
r
m
ap
fo
r
sp
ec
ifi
c
tr

ad
e
la
ne
s
(i
nc
lu
di
ng
co
m
m
er
ci
al
,
go
ve
rn
m
en
t,
lo

gi
st
ic
s,
an
d
in
fo
rm
at
io
n
fu
nc
ti
on
s)
C
on
ve
ni
en
ce

sa
m
pl
e
of
po
li
cy
m
ak
er
s
in
th
e
do
m
ai
n
kn
ow
n
to
th

e
de
ve
lo
pe
rs
C
on
ve
ni
en
ce
sa
m
pl
e
of
or
ga
ni
za
ti
on
s

kn
ow
n
to
re
pr
es
en
t
a
ra
ng
e
of
vi
ew
s
ab
ou
t
ur
ba
n
de

ve
lo
pm
en
t
in
th
e
re
gi
on
T
yp
e
of
pa
rt
ic
ip
at
io
n
In
vo

lv
in
g
In
vo
lv
in
g
In
vo
lv
in
g/
co
ll
ab
or
at
io
n
In
vo
lv
in

g
In
vo
lv
in
g
M
et
ho
d
of
st
ak
eh
ol
de
r
en
ga
ge
m
en
t

F
ac
e-
to
-f
ac
e
w
or
ks
ho
ps
F
ac
e-
to
-f
ac
e
w
or
ks
ho
ps

;
co
ll
ab
or
at
iv
e
sc
en
ar
io
bu
il
di
ng
F
ac
e-
to
-f
ac
e
m

ee
ti
ng
s;
co
ns
en
su
s-
bu
il
di
ng
w
or
ks
ho
ps
;
in
te
rv
ie
w

s,
jo
in
t
sp
ec
ifi
ca
ti
on
of
tr
ad
e
la
ne
an
d
of
so
lu
ti
on
F

ac
e-
to
-f
ac
e
m
ee
ti
ng
s
be
tw
ee
n
de
ve
lo
pe
rs
an
d
po
li

cy
m
ak
er
/u
se
rs
S
ep
ar
at
e
fa
ce
-t
o-
fa
ce
m
ee
ti
ng
s,
in

te
rv
ie
w
s,
jo
in
t
do
cu
m
en
t
pr
ep
ar
at
io
n
w
it
h
ea
ch

st
ak
eh
ol
de
r
T
oo
ls
an
d
te
ch
no
lo
gi
es
us
ed
S
ta
ke
ho
ld

er
an
al
ys
is
to
ol
;
on
li
ne
fo
ru
m
;
e-
m
ai
l
O
C
O
P

O
M
O
pl
at
fo
rm
an
d
co
ns
is
te
nt
co
nc
ep
tu
al
de
sc
ri
pt
io

n
(C
C
D
)
V
is
ua
l
m
od
el
s;
da
ta
-fl
ow
di
ag
ra
m
s;
lo
gi

st
ic
s-
fl
ow
di
ag
ra
m
s;
ga
m
es
M
ic
ro
-s
im
ul
at
io
n
m
od

el
in
g
S
im
ul
at
io
n
m
od
el
[email protected]
T
ab
le
9.
1
(c
on
ti

nu
ed
) C
as
e
1
C
as
e
2
C
as
e
3
C
as
e
4
C
as
e
5
R
es

ul
ts
/o
ut
co
m
es
of
en
ga
ge
m
en
t
pr
oc
es
s
In
cr
ea
se
d
co

m
m
it
m
en
t
an
d
co
ns
en
su
s
am
on
g
ke
y
st
ak
eh
ol
de
rs

T
he
st
ak
eh
ol
de
r
en
ga
ge
m
en
t
pr
oc
es
s
w
as
pe
rc
ei
ve

d
am
on
g
st
ak
eh
ol
de
rs
as
a
us
ef
ul
an
d
an
im
po
rt
an
t
pr

oc
es
s
in
po
li
cy
an
al
ys
is
A
de
di
ca
te
d
gr
ou
p
in
no
va

ti
on
se
tt
in
g
en
ab
le
d
th
e
st
ak
eh
ol
de
rs
to
be
tt
er
un
de

rs
ta
nd
th
e
ne
ed
s
be
tw
ee
n
th
em
,
w
hi
ch
en
ab
le
s
“t
ru

st
”
an
d
pr
op
ag
at
e
so
lu
ti
on
s
th
at
w
er
e
no
t
po
ss
ib

le
a
ye
ar
ag
o
T
he
en
ga
ge
m
en
t
fa
ci
li
ta
te
d
th
e
de
ve

lo
pm
en
t
of
a
de
ci
si
on
-s
up
po
rt
to
ol
fo
r
po
li
cy
m
ak
in

g
A
fr
am
ew
or
k
an
d
te
m
pl
at
e
fo
r
de
fi
ni
ng
,
pr
es
en

ti
ng
,a
nd
in
co
rp
or
at
in
g
va
lu
e-
ba
se
d
in
di
ca
to
rs
in
th

e
m
od
el
In
cr
ea
se
d
tr
an
sp
ar
en
cy
an
d
op
en
ne
ss
of
th
e

st
ra
te
gi
c
pl
an
ni
ng
pr
oc
es
s
E
ng
ag
em
en
t
en
ab
le
d

un
de
rs
ta
nd
in
g
of
th
e
po
li
cy
ca
se
am
on
g
st
ak
eh
ol
de
rs

,a
nd
th
e
to
ol
fa
ci
li
ta
te
d
th
e
sh
ar
in
g
of
vi
ew
s
to
su

pp
or
t
st
ak
eh
ol
de
rs
’
vi
ew
s
in
a
ne
w
po
li
cy
M
ak
in
g

st
ak
eh
ol
de
rs
pa
rt
of
th
e
fa
ct
-fi
nd
in
g
an
d
so
lu
ti
on
-d

ev
el
op
m
en
t
pr
oc
es
s
su
pp
or
te
d
co
m
m
it
m
en
t
of
st

ak
eh
ol
de
rs
to
th
e
so
lu
ti
on
T
hi
s
en
ga
ge
m
en
t
al
so
es

ta
bl
is
he
d
a
gr
ou
p
w
ho
w
er
e
ab
le
to
be
ea
rl
y
ad
op
te

rs
of
th
e
de
ci
si
on
-s
up
po
rt
to
ol
,
an
d
w
ho
ar
e
ab
le
to

ad
vo
ca
te
fo
r
it
A
m
et
ho
d
th
at
al
lo
w
s
di
ff
er
en
t
st

ak
eh
ol
de
rs
to
ad
vo
ca
te
fo
r
di
ff
er
en
t
va
lu
es
,b
ut
fo
r

al
l
st
ak
eh
ol
de
rs
to
vi
ew
th
e
im
pl
ic
at
io
ns
of
th
os
e
va

lu
es
in
a
st
an
da
rd
se
t
of
ag
re
ed
-u
po
n
in
di
ca
to
rs
th
at

m
ea
su
re
th
ei
r
lo
ng
-t
er
m
ef
fe
ct
s
Jo
in
t
pr
oc
es
s
su

pp
or
ts
co
ns
en
su
s
am
on
g
st
ak
eh
ol
de
rs
(a
t
le
as
t
in
th

e
sa
m
e
tr
ad
e
la
ne
)
[email protected]
9.5 Discussion
In this section, we return to our two guiding questions: What
types of engagement
tools and processes are useful for different stakeholders and
contexts? And what
factors support the effective use of particular tools and
technologies toward con-
structive outcomes? The extant literature reveals a rich history
of examining the role
of participation in democratic theory and complex governance
(Fung 2006; Fung
et al. 2007). Various analytical tools in the literature address
participant selection,

modes of communication, and involvement and many of these
were present in the
cases. The cases confirm previous research regarding the
importance of stakeholders
and the need for careful and goal-oriented stakeholder selection
and engagement.
The cases also demonstrate the importance of support and
education for participants
and the role of trusted facilitators, contributing to the
knowledge in this field. This
section presents the key findings of our case comparison.
Identifying and Representing Relevant Stakeholders New
governance means bring-
ing in stakeholders who are not traditionally part of the policy-
making process. Fung
(2006) describes a continuum of types of stakeholders in new
governance, including
state representatives (described as expert administrators or
elected representatives)
and mini-publics (described as professional and lay stakeholders
with organized
interests). Professionals are paid participants (such as lobbyists)
or not-for-profit
organizations. Lay stakeholders are those who volunteer their
services such as in-
dividuals serving on school councils or neighborhood
associations. The cases show
that effective stakeholder engagement requires a nuanced
understanding of who are
the relevant stakeholders with respect to the specific goal of the
engagement. Each
case represents a complex policy area where the different
stakeholders selected or
invited to engage in the policy process represented particular
aspects or viewpoints

about a complex problem. Our study confirms that stakeholder
analysis helps pol-
icy makers understand differences in stakeholder behavior,
intentions, preferences,
interrelations, and interests. It also helps them assess the
influence and resources
different stakeholders bring to decision-making or
implementation processes (Var-
vasovszky and Brugha 2000). We found that ordinary citizens
were seldom involved
in these cases. Despite the common rhetoric of “citizen”
participation, the cases show
how it is often impractical to engage members of the public or
representatives of the
full range of relevant stakeholders. In these situations, policy
modelers and policy
makers needed to appreciate the limitations of stakeholder
engagement and aim for
results that take advantage of less-than-complete stakeholder
participation.
For example, in the UrbanSim case, only three organizations
participated in the
codevelopment of new indicators. The modelers did not treat
these stakeholder views
as complete or definitive but rather they used this limited
experience to create a
value-based indicator framework to guide further development
of new indicators
and future applications of the UrbanSim model. In the
international trade case, the
main stakeholder groups were each represented by up to four
“exemplary” actors.
In this way, the key positions of these groups were reasonably
well represented
in the various activities in the project. These representative

actors also served as a
[email protected]
starting point to identify specific trade lanes where innovations
could take place,
and thereby also created awareness of other stakeholders that
play a role in those
trade lanes. In the Kosice energy policy case, stakeholder
identification was done
using a technique similar to that proposed by Bryson (2004).
The local government
was mainly responsible for identifying relevant stakeholders
that were invited to the
engagement process. Other complementary techniques such as
surveys were used to
assess stakeholders’ roles and expectations. In the international
trade case, similar
techniques were applied.
Providing for Participant Support and Education In order to
participate in mean-
ingful ways, stakeholders in our cases needed to be educated
regarding the purpose
of the engagement, the processes and tools to be used, and the
ways in which stake-
holder input would be considered. For all the cases presented,
stakeholders, including
those that were often not directly involved in policy making
(e.g., citizens, smaller
companies), were made aware of the policy problem in some
depth, presented with

opportunities to deliberate the different policy choices, and
presented with the in-
formation necessary to understand the expected outcome from
implementation of
different policy options.
In the case of EGOV Afghanistan, stakeholders were provided
with the results
of an EGOV readiness assessment exercise for them to
understand the crucial prob-
lems to be solved through the implementation of a national e-
government policy.
Workshops offered them general knowledge about approaches
and methodologies
for strategy development. In Kosice, participants were provided
with the energy pol-
icy problem and background documents for additional
information about the policy
such as the energy conceptions proposed for various cities in
the region and studies
of ROI for various combinations of heat energy sources. The
descriptive scenarios
and background documents were important for stakeholders to
understand the policy
issue, its boundaries, and its challenges. In UrbanSim, the
stakeholders were guided
through the process of creating narrative value statements as
well as ways to describe
and document indicators in accurate, neutral language. All of
these education and
support activities made the stakeholders’ deliberations and input
more usable and
more relevant to the problem at hand.
Using Stakeholder Engagement Methods to Reveal and Explain
Complex Policy

Problems and Contexts Our cases illustrated that stakeholder
engagement is an im-
portant process in policy development as evidenced in the
literature reviewed in
Sect. 9.2.3. Engagement helped in all cases to assure that policy
processes and pol-
icy decisions were well grounded and responsive to both social
values and practical
needs. Action research and living labs helped assure that
involvement was not based
on an oversimplified view of the policy problem, Different tools
acted as boundary
objects to facilitate knowledge sharing, consensus building,
listening, and negotiat-
ing. Models of many kinds were used to break down complex
processes and revise
mental models.
In very intractable public problems like trade lanes, in order to
understand how
various actors would be affected by different policy options, it
was important to un-
derstand how information flowed between actors. The
specificity of the models used,
[email protected]
as well as their comprehensiveness in representing the actual
situation, facilitated a
focused debate between businesses and government agencies,
forcing each party to
be clear about their precise activities and relevant policy

concerns. As a result, no
stakeholder could hide behind a policy that allegedly forced or
blocked a certain so-
lution, and the consensus process could focus on the policy
options that were feasible
in practice. The Kosice energy policy problem required a
balance of diverse interests
of stakeholders both supplying and consuming energy. This
presented policy mak-
ers with challenges in identifying and engaging those interests
that will affect the
implementation of the new policy. Collaborative scenario
building engaged both cat-
egories of stakeholders. This method was particularly important
for policy makers to
increase the level of certainty of the policy choice by
understanding the intersecting
interests of these stakeholders. Formal policy modeling and
simulation were also
important to inform all stakeholders and policy makers of the
different possible out-
comes of their scenarios. In the child health case, stakeholders
were educated about
the concepts and assumptions underlying the policy-modeling
tool being developed.
They also learned from each other about the policy questions of
greatest importance
to child health and development. The methods used in these
cases are similar to
those identified in literature (Andersen et al. 2007; Vennix et al.
1996) and can be
employed to contribute to many different policy development
efforts.
Using Trusted Third Parties to Enhance Transparency of the
Process and Improve

the Tools of Engagement Negotiating, brokering, and
collaboration skills and exper-
tise with engagement tools are all essential for achieving new
forms of governance
(Bingham et al. 2005). The tools and technologies used in our
cases have different
characteristics that affect choice and suitability, including
available expertise and fi-
nancial resources, level of participation, type of policy problem,
and the geographic
location or dispersion of stakeholders. The cases also address a
factor that is less
often critically addressed, namely the ways that “trusted” third
parties, such as re-
searchers, are used in stakeholder engagement. In these
situations, researchers were
not only doing academic research on engagement but also
crafting, testing, and im-
proving meaningful tools toward practical outcomes. As
“brokers” in the process,
researchers and the tools and technologies they use can inhibit
or promote better
models of engagement in policy making and governance.
In the case of EGOV Afghanistan, the use of online surveys by
the UNU-IIST
team solved the issue of trying to reach a distributed set of
stakeholders separated by
geography and also provided a confidential way to gather
information about stake-
holder interests, while the stakeholder analysis tool provided by
UNU-IIST helped
MCIT to understand stakeholder preferences and concerns and
to assess their po-
tential to influence the policy process. The technology tools
used were not intended

to “socialize” the interests of stakeholders but to gather
intelligence by a trusted
third party that could be used in the strategic planning process.
By comparison, the
intention of the online OCOPOMO platform used in the Kosice
case was to bring
the stakeholders themselves into a virtual meeting place where
they could see the
interests of other stakeholders. This technology choice,
implemented by expert re-
searchers, was intended to facilitate knowledge sharing in a
multidirectional way.
[email protected]
In the UrbanSim case, the stakeholders’ values and interests
were intentionally de-
veloped in isolation from one another because the goal was to
represent the distinct
values of each stakeholder type within the model. The
simulation mechanism, built
by the academic experts, could then model and report indicators
showing how these
different interests might interact over time. In the international
trade case, a neutral
party designed the modeling approach and helped the
stakeholder groups in each
trade lane model their own existing situations. This approach
facilitated joint prob-
lem identification and solution development. In the New
Zealand child health case,
researchers helped policy makers discover policy-relevant

material while the pol-
icy makers helped the researchers understand what formats and
other factors made
that material relevant and usable. Each example demonstrates
the role of trusted,
independent experts who can select technology options, tools,
and techniques that
introduce transparency into the process and are technically and
practically suitable
to the situation. The researchers/modelers were trusted
independent brokers who
gathered data, facilitated engagement, and built models or
systems to transparently
reflect the reality of the stakeholders.
9.6 Conclusion
All of the cases we reviewed above used an active approach,
assisted by third-party
experts, to bring stakeholders together in workshops, through a
collaboration plat-
form, or in living labs to support interaction in problem
identification, codevelopment
of solutions, and foundations for gaining commitment or
consensus by different types
of stakeholders. These experiences go well beyond eliciting
stakeholders’ positions
and requirements, leaving the interpretation and balancing to be
done by the policy
maker independently. The approaches used in these cases
supported the stakeholders
directly in gaining a shared understanding of the problem,
providing some insight into
the position and reasoning of other stakeholders, laying the
groundwork for potential
negotiation or other ways to find common ground with respect

to the policy issue, and
in some cases establishing or reinforcing trust among different
stakeholders as well
as trust in the participation process. In line with the literature
on this topic, the cases
also illustrate some of the cautions and limitations of
stakeholder engagement, with
particular emphasis on the realistic limits of involvement and
representation, and the
consequent necessity to match stakeholder selection and
engagement methods to a
well-defined goal within the larger policy process.
We find that a careful identification of stakeholders is required,
and the selec-
tion depends on the goals of engaging stakeholders. The
appropriate selection of
stakeholders to involve can evolve over time, the identification
and engagement of
stakeholders is a continuous process, as Bryson (2004) suggests.
To illustrate this in
one of the cases, in the international trade case, the process
started with a set of stake-
holders needed to identify and initiate the demonstration trade
lanes. These provided
grounds for further identifying other stakeholders that play a
role in those trade lanes
or that were relevant to the initial set of stakeholders. These
needed to be engaged
[email protected]

also in order to meet the goals of engaging stakeholders. The
goals themselves can
also evolve along the changing stakeholder involvement. In this
case, especially in
the beginning, stakeholders were involved to elicit their views
and interests in the
matter, whereas during the process this shifted toward engaging
stakeholders to en-
sure commitment and to facilitate building consensus among the
stakeholders. There
are similarities among the cases such as the use of surveys and
convenience sam-
pling as methods to identify stakeholders, face-to-face
meetings, and workshops as
methods of engagement and use of modeling techniques as tools
and technologies.
Although the literature provides various available methods and
techniques used in
stakeholder engagement processes, the cases illustrate that the
approaches, tools,
and technologies selected in each case are highly influenced by
the purposes and
expected outcomes of the engagement effort. Therefore, we
emphasize that every
stakeholder engagement needs to be tailored with well-selected
processes and tools
that suit the overall purpose and expected outcomes.
As frequently highlighted in the literature, stakeholders’
involvement in policy
processes can help build consensus by balancing stakeholder
interests and pref-
erences, increasing their commitment for policy
implementation, and ensuring
transparency and openness of the process. Often, these
advantages of stakeholder

engagement are linked to the idea of empowering stakeholders
as much as possi-
ble (i.e., stakeholders make key decisions). However, our study
shows that all of
these advantages can also be gained by involving stakeholders,
with less emphasis
on empowerment. We posit that these benefits can be realized
when stakeholders
understand their roles and the objectives of their engagement,
enabling them to bring
their own interests to the table while also gaining an
understanding of other interests
and factors that influence decisions and results. Therefore, our
findings on the im-
portance of offering support and education for participants in
order to enable them
to understand their role and the engagement process are an
important contribution
to the literature. In a similar vein, the role that trusted (third-
party) facilitators could
play in the engagement process is often underestimated in the
literature, but is clearly
an important ingredient in the cases presented in this chapter.
Tools can take many different forms, some using technology
and some not—the
important factor is to match the tool to the objective and the
capabilities of the stake-
holders involved. Making this match requires an understanding
of the capabilities
of the stakeholders to use such tools and technologies,
sometimes also in a spe-
cific country context. Furthermore, as the UrbanSim and child
health case shows,
stakeholders can not only contribute to policy analysis and
choices but also make

significant contributions to improving the effectiveness of
policy processes, and the
validity and usability of models, and other tools.
Based on these findings, our study offers some practical
insights for policy mak-
ers (and researchers) that want to engage stakeholders for policy
development. The
first critical step is identification of salient stakeholders or
stakeholder types. The
literature reviewed in this chapter as well as the five cases offer
various approaches
to identify stakeholders. As concluded above, the method used
to identify stake-
holders is closely related to the intended purpose of stakeholder
engagement. For
[email protected]
example, when aiming to learn from stakeholders about a
specific domain, a con-
venience sample of relevant actors is a suitable method.
However, if the goal is to
ensure commitment or to build consensus, the methods
employed need to be rigor-
ous in identifying all key stakeholder groups. Desk research,
surveys, interviews,
and stakeholder or interests mapping tools are useful
approaches to do this. Iterative
stakeholder identification often helps create a more complete
array of relevant stake-
holders. Our research in combination with the relevant literature

also shows other
purposes for stakeholder engagement that guide the selection of
stakeholder types.
For example, transparency of the process, facilitating adoption,
improving useful-
ness and usability of tools, and enhancing legitimacy are
purposes of stakeholder
engagement we found in the cases.
Once the relevant stakeholders have been identified and the
objective of involving
them is clear, the approach to stakeholder engagement needs to
be selected. Whereas
the literature presents various options, all the cases we covered
were in an advanced
stage and almost all employed some form of action research, in
which stakeholders
(especially practitioners and policy makers) worked closely
with each other and with
researchers in a collaborative way. This was found in all cases,
as all cases were fo-
cused on involving stakeholders. In case the objective is to
primarily inform or consult
stakeholders, other approaches are more suitable, and some
suggestions have been
provided in the background section. When involving
stakeholders, policy makers
and researchers will have to carefully consider what role the
engaged stakeholders
will have; involving stakeholders to work in real-world
complexity as much as pos-
sible will benefit from action research or living labs, but
requires that the material,
objectives, activities, etc. be carefully prepared and designed,
as stakeholders do not
always have a clear idea of what their involvement should look

like or contribute to.
On the other hand, complexity can also be broken down to make
the matter more
comprehensible for stakeholders. For this, modeling tools and
simulations can be
used for both purposes. In either case, tools and models can
function as boundary
objects that stakeholders can view, discuss, or manipulate to
better understand how
a particular decision might play out. However, the conceptual
capacity stakehold-
ers that will need to have affects the kind and amount of work
that should go into
preparing the engagement.
While much remains to be learned about stakeholder
engagement in policy mod-
eling, this chapter provides a starting point for better
understanding how different
approaches, tools, and technologies can support effective
stakeholder participation
toward better policy choices and outcomes. The cases presented
here demonstrate
that stakeholder engagement processes, tools, and technologies
are versatile and use-
ful to both policy makers and the stakeholders themselves. With
careful selection and
application, they can work in a wide variety of situations
including different policy
domains and kinds of problems, different political systems, and
different levels of
social and economic development.
Acknowledgment This comparison and analysis was conducted
as a collaborative activity of
the eGovPoliNet Project, funded through the European

Commission Framework 7 Program as
agreement FP7-ICT-2011–288136, and supported by US
National Science Foundation (NSF) grant
[email protected]
IIS-0540069 to explore policy modeling and governance
through an international consortium of
research institutions. Ideas and opinions expressed by the
authors do not necessarily represent those
of all eGovPoliNet partners.
We also gratefully acknowledge the information and reference
material provided by Peter Davis
and Barry Milne of the COMPASS Center at the University of
Auckland regarding the New Zealand
case, and Alan Borning at the University of Washington
regarding the UrbanSim case.
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[email protected]

Chapter 10
Values in Computational Models Revalued
The Influence of Designing Computational Models
on Public Decision-Making Processes
Rebecca Moody and Lasse Gerrits
Abstract This chapter aims to add to the technology debate in
the sense that it aims
to research the role of values and trust in computational models
in the policy process.
Six case studies in which a computational model was used
within a complex policy
context were research for the role values play within these
models. Conclusions
deal with the role of the designer of the model, the number of
different actors, the
amount of trust already present, and the question of agency by
humans or technology.
Additionally, margins of error within the model are discussed as
well as authority by
one actor over others concerning the model.
10.1 Introduction
Policy makers are tasked with making decisions on issues
characterized as wicked
problems because of controversies, unknown relationships
between causes and con-
sequences, and (consequently) uncertain futures. From this
perspective, it would be
desirable to map the decisions and their possible outcomes prior
to the actual deci-

sion making because that would generate certainty in ambiguous
situations. Broadly
speaking, this provides the motive for using computational
modeling for policy
making as expressed in, e.g., policy informatics. Although there
are computational
models that are ready off-the-shelf, it is more common to work
with models “mod-
ded off-the-shelf” (MOTS) or even tailor-made models to suit
specific questions and
conditions. As such, the model itself becomes part of the
decision-making process
during the acquisition.
We observe that this phase, during which scope, functionality,
and deployment
are determined by commissioning actors and designers, is
essential to the way the
models influence policy making. Although it may be assumed
that such models are
neutral or value-free, they are not because of the changes that
designer and client
R. Moody (�) · L. Gerrits
Department of Public Administration, Erasmus University
Rotterdam,
P.O. Box 1738, 3000 DR Rotterdam, The Netherlands
e-mail:[email protected]
L. Gerrits
e-mail:[email protected]
10.1007/978-3-319-12784-2_10
[email protected]

206 R. Moody and L. Gerrits
introduce to the original model. This chapter aims to shed light
on the relationships
between computational models and policy making by looking at
the role values
play in commissioning, designing, and using such models in
policy making. We
will rely on the notions set forward in the technology debate in
order to understand
the way technical design can be perceived by actors and used in
policy making.
These notions will also be used in our analysis in order to
understand the way actors
within the policy process reach conclusions based on the
models. We will primarily
look at the perception of the models in terms of values on which
we will elaborate
below. We carried out a secondary analysis of case study data
we collected for
other research (Gerrits 2008; Moody 2010). The case studies
concern: (1) predicting
effects of deepening operations in rivers in Belgium and the
Netherlands and (2) in
Germany; (3) determining flood risk prediction in Germany and
the Netherlands; (4)
determining the implementation of congestion charging in the
UK; (5) predicting and
containing the outbreak of live stock diseases in Germany; (6)
predicting particular
matter concentrations in the Netherlands. The chapter is
structured as follows. We
will first discuss the theoretical background of our analysis by

looking at autonomy
of technology and technology as being deterministic, blending
notions from the
technology debate with notions from public administration and
public policy in
Sect. 10.2. The methodological approach is discussed in Sect.
10.3, the case studies
in Sect. 10.4, the analysis in Sect. 10.5, and the conclusions in
Sect. 10.6.
10.2 Technological Perceptions: The Debate
To understand the implications of the design of computational
models it is necessary
to understand the underlying assumptions of the design process.
The way modelers
design different models can be viewed from different
viewpoints as pointed out in the
technology debate. This is an ongoing debate in philosophy of
science as well as in
sociology and technical studies. The technology debate revolves
around technology
and humans, technology and society, and technology itself. It
reflects on questions
of who drives technology: Are humans the drivers of technology
or does technology
drive humans? Does technology possess any values of its own
and are these values
given to technology by humans or does technology have no
values whatsoever and
is it completely neutral? What is the relationship between
technology and society,
does technology constitute society or is it the other way around?
A large number of authors have described the technology debate
and placed their

opinion (see: Smith and Marx 1994; Scharff and Dusek 2003;
Kaplan 2004). In the
technology debate, several issues are discussed. A central issue
is who masters the
other, do humans master over technology, or does technology
control humans? An-
other key theme is the question whether technology is
autonomous and determines
its own causality. Another key feature is whether technology
incorporates values or
should be seen as neutral. Finally, the relationship between
technology and society
[email protected]
10 Values in Computational Models Revalued 207
is important—which drives the other? A number of standpoints
within the technol-
ogy debate can be identified. For the sake of briefness, we will
only look at social
constructivism and technological determinism and technological
instrumentalism.
Within technological determinism, it is believed that technology
is not neutral or
value-free. Technology can be good or bad or a mixture of
both—this goes for
effects as well as consequences. These consequences may not be
dependent on
the desired goal but are dependent on the technology.
Technological development,
therefore, does not depend primarily on the intention of the user
but is fixed within

the technology itself, it is inevitable and cannot be steered or
controlled by humans.
Agency here is not given to the human user but is attributed to
technology. It is argued
that certain political and social norms and values are hidden
inside the technology.
Therefore, the technology will bring about consequences
according to these norms
and values (Ellul 1954, 1990; Zuboff 1988; Heilbroner 1967,
1994; Winner 1977,
1980, 1983, 1993).
In social construction of technology, the viewpoint held is that
choices need to be
made in the design and the direction of technology. Economy,
society, institutions,
and culture shape the direction and scope of technological
development, the form of
technology, the practice, and the outcome of technological
change. Agency in this
approach is given back to humans. Technology is neither seen
as autonomous nor
does it have a fixed outcome with inevitable consequences. All
technology is seen as
a human construct and is thus shaped, or made by humans
(Bijker 1993, 1995; Hoff
2000).
What is very important in understanding the approach of social
construction of
technology is the technological frame. This technological frame
consists of goals,
problems, problem-solving strategies, requirements to be met by
problem solutions,
current theories, tacit knowledge, testing procedures, design
methods and criteria,

users practice perceived substitution function, and exemplary
artifacts (Bijker 1995).
The technological frame is thus the set of rules, ideas, and
meanings within a group
and it determines the interaction between the members of a
group. This means the
technological frame determines which meaning a group will
attribute to a technology
(Bijker 1995).
Within technological instrumentalism, technology is seen as a
neutral and value-
free tool. This means a number of things. Firstly, that the
technology can be used to
any end. Secondly, this means that technology is indifferent to
politics. The technol-
ogy can simply be used in any social or political context since it
is not intertwined
with any context. Thirdly, technology is viewed as being
rational. It is based on causal
propositions; it can therefore be transferred into any other
context as well. Finally,
technology is seen as universal, it stands under the same norm
of efficiency in any and
every context (Feenberg 1991). Within the approach of
technological instrumental-
ism, technology is not attributed with any agency. This means
that technology itself
cannot account for any form of causality; humans cause this
causality. Technological
progress, therefore, is viewed as desired progress since it is the
human actor who
pursues it (Bekkers et al. 2005). Technology is developed and
implemented with the
purpose of achieving one’s goal and the technology serves as a
means to achieve this

goal.
[email protected]
Authors within all positions agree, however, to the point that in
computer technol-
ogy it becomes very difficult to model the real world. Reality is
composed of infinite
variables and relationships that pose practical limits to what
data can be processed.
Computer models, their designers, and users all are bound in the
degree of ratio-
nality they can display. Among others, Simon (1976, 1957),
Dror (1968); Lindblom
(1959), and March and Simon (1993) recognized that public
decision makers limit
the number of options they consider because of this cognitive
limitation. While with
computer models it is assumed by many that this bound in
rationality can be lifted,
it can also be argued that this is not necessarily the case
(Moody 2010).
10.3 Technology and Public Decision Making
The argument above means that synoptic decision making where
the model maps
decision outcomes, to be followed up by the actual decision, its
implementation
and possible feedback, is too optimistic an approach. It assumes
that a model would
deliver (nonbiased) data, which is judged by decision makers to

generate alternatives,
of which the best alternative is chosen and consequently carried
out (March and
Simon 1993; Winner 1977; Beniger 1986; Goodhue et al. 1992;
Chen 2005). It
is then assumed that a computational model is a value-free tool
that will provide
a neutral oversight of all available alternatives with their
consequences. Therefore,
it is believed by some that these models will decrease the
bounds in rationality
that decision makers face and that public policy making will
become a more rational
process in which all consequences are foreseen prior to decision
making (Ware 2000;
Moody 2010; Beniger 1986; Goodhue et al. 1992).
This line of reasoning corresponds with the technological
instrumentalist view-
point. However, while public decision making is also a political
process in practice,
we see that computational models, next to not being able to
include all variables
needed for complete consequences, also suffer from limits on
the side of political
values. It must be noted that the designer of the model is not a
neutral object either
and becomes able to influence the model (Winner 1977; Chen
2005; Ware 2000;
Wright 2008). Known margins of error can be manipulated
toward political values
and the necessary choice which needs to be made on which
variables to include in
the model is value-driven as well.
The question we need to ask ourselves here is not only whether

computational
models are a value-free or neutral tool, but moreover who or
what determines the
values within these models. Technological determinists would
argue that values are
inherent for the models themselves, and the outcomes of the
model are fixed before
use. Social constructivists would argue that the models would
be attributed with
value through a process of using the model. We want to take
this reasoning a step
further, without taking position in the debate, and look at who
designs the model, who
decides which variables should be put into the model, and which
variables should be
excluded. Who decides what the functionality of the model is—
what is it able to do
and what not—and how do policy makers react to this?
[email protected]
While the above demonstrates that the topic on values in a
computational model is
a very loaded and complex topic to begin with, we find that next
to the complexity in
the model itself in terms of values, the process of policy making
deals with additional
values and its own complicatedness. We have to also address
the complexity in the
actual decision-making process because of the multiple actors
with diverging norms,
beliefs, and interests. Following the previously stated and

driving on outcomes and
hypotheses of previous research (Gerrits 2008; Moody 2010) we
find that in dealing
with values in computational models and public policy, some
core characteristics
can be identified:
1. The values attributed to the data on which the computational
model is based.
These values can be subdivided into:
a. The dominant ideas actors hold on these data, for example,
are the data correct,
are they trustworthy? (Trust)
b. The margin of error in the data and how this margin of error
is communicated
to policy makers. Are they aware of the correct margin of error,
do they under-
stand what this implies, do they feel this is an acceptable
margin? (Margins
of error)
2. The values attributed to the model itself, this can be
subdivided into:
a. The organization that owns or commissions the model. Is
there one organiza-
tion who owns the model, or are there clusters of organizations
owning the
model, if so, do they share the same values? (Ownership)
b. Perceptions and values toward the model itself by designers,
owners, and other
actors. Do they trust the model, do they feel the outcomes the
model produces

are correct? (Beliefs)
3. The values within the decision-making process, this can be
subdivided into:
a. Who is the organization which makes the final decision on
policy? Are they
codependent on other organizations in order to be able to make
the decision
or do they have sole authority? (Authority)
b. Are there other actors involved in the policy making? These
actors do not
necessarily need to have the authority to make the decisions but
are present
in a policy arena or community affecting the decision or the
reception of this
decision. Are there many of such actors? (Multiactors)
In the analysis of the cases, these are the core characteristics to
be analyzed.
10.4 Methodology
As mentioned above, we carried out a secondary analysis on
original case studies by
us. This was done to change the perspective of our original
analysis, which dealt more
with outcomes instead of process. The selected cases share three
basic characteristics.
Firstly, they all featured new tailor-made computational models
that were deployed
for the first time in the case. Secondly, all cases concern policy
issues with the natural
or built environment. Thirdly, all cases concern highly complex
and controversial

issues.
[email protected]
10.5 Case Studies
The case studies are presented in this section. Each case has a
brief introduction. The
main characteristics are presented in Table 10.1. An overview
of the stakeholders in
each case can be found in Table 10.2.
Case 1: Morphological Predictions in the Westerschelde
(Belgium and the
Netherlands) The Westerschelde estuary runs from the Belgian
port of Antwer-
pen through the Netherlands before ending at the North Sea
coast. The estuary has
a limited depth and the Antwerpen port authorities were seeking
ways to deepen
the main channel in the estuary to facilitate larger ships and
thus promote economic
growth. However, the estuary is Dutch territory and the Dutch
authorities are reluc-
tant to facilitate the wishes of their Belgian counterparts. They
regard the estuary
as a fragile complex system that has a high ecological value and
fear that the ecol-
ogy could be destroyed by yet another deepening operation. The
estuary consists
of multiple channels through which the tide flows. Those
channels are considered

pivotal to the very specific and rare estuarine ecology of which
very few remain
across Europe. Negotiations starting in the early 2000s included
the extensive use
of computational models to assess the extent to which a
deepening would harm the
multichannel morphology of the riverbed and with that the
ecological value. Re-
search was jointly commissioned by the Dutch and Belgian
authorities. A Dutch
research institute, Deltares (formerly WL-Delft Hydraulics),
was the main contrac-
tor, with a small number of subcontractors. It deployed two
computational models:
Sobek, which is modified off-the-shelf; and Delft3d, which was
a brand-new model
and considered the more advanced but less tested model of the
two. Both models
were used to simulate the consequences of dredging operations.
The results of Sobek
seemed more robust but were considered relatively crude, while
the results of Delft3d
appeared more advanced but featured more model and outcome
uncertainty.
Case 2: Morphological Predictions in the Unterelbe (Germany)
Like the West-
erschelde, the German Unterelbe is also an estuary that gives
access to a major
European seaport. It runs from the port of Hamburg through the
federal states
Niedersachsen and Schleswig–Holstein before flowing into the
North Sea. Similar
to the first case, the port authorities are seeking for a deepening
of the main channel
to facilitate larger ships. Such a deepening was carried out in

the 1990s but had
resulted in severe (partly) unforeseen and unwanted changes to
the estuary that many
people felt had harmed the ecological state. Here it appears as if
the desire to deepen
the estuary had influenced the outcomes of the computational
model. The ensuing
societal and political protests, from both nongovernmental
organizations (NGOs)
and the federal states except Hamburg itself, led to a different
approach when
considering a new deepening in early 2000. The Hamburg port
authorities and the
Hamburg Senate commissioned the research to the federal
research institute Bunde-
sanstalt für Wasserbau (BAW). This institute collected the
relevant data and built its
own model in-house to generate directions for dredging and
ecological development.
[email protected]
T
ab
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[email protected]

Table 10.2 Overview of relevant stakeholders per case
Case no. Actors involved, including societal stakeholders
1 Bureau Getijdenwateren, Havenbedrijf Antwerpen, Ministerie
van Verkeer en
Waterstaat, Office BeNeLux, Provincie Zeeland, Port of
Antwerp Expert Team,
ProSes, Rijksinstituut voor Kust en Zee, Rijkswaterstaat
Directie Zeeland,
Waterschap Zeeuwse Eilanden, WL Borgerhout, WL Delft
Hydraulics, Zeeuwse
Milieufederatie
2 ARGE-Elbe, BUND Hamburg, Bundesanstalt für
Stadtentwicklung und Umwelt,
Bundesanstalt für Wasserbau und Schifffahrt, Hamburg Hafen
und Logistik AG,
Hamburg Port Authority, Handelskammer Hamburg, Landkreis
Stade, NABU
Hamburg, Rettet die Elbe, Senat Hamburg
3 Ministerie van Verkeer en Waterstaat, Rijkswaterstaat,
Taskforce Management
Overstromingen, Municipalities Netherlands, Gemeenten
Municipalities
Germany, Waterboards, Citizens, Royal Haskoning, Provinces
Germany
4 City of London, Transport for London, Citizens, National
Alliance against Tolls
5 Friedrich Loeffler Institute, Bundeslanden, Universities,
Veterinarians, Citizens

6 Provinces Netherlands, Municipalities Netherlands,Rijks
Instituut
Volksgezondheid en Milieu, Landelijk Meetnet Luchtkwaliteit,
environmental
organizations
Because of the belief that it should be seen as neutral in the
controversial debate,
BAW took the unusual decision to make its data and the model
parameters available
to any third party interested to replicate the results or to
develop different models.
Consequently, NGOs used the data to model their own particular
version of the
dredging works and their consequences. They arrived at
different conclusions, which
meant that the commissioning actors were obliged to engage in
a dialogue about the
future of the estuary. This slowed down and altered the original
plans.
Case 3: Flood-Risk Prediction (Germany and the Netherlands)
In the last 2 years,
it was decided to run one application in the Netherlands and
Germany with the goal of
predicting and managing floods from rivers. Before this,
applications and authorities
were divided on the subject. The application named FLood
Information and WArn-
ing System (FLIWAS) was to integrate different applications
and organizations to
make sure water management and flood prediction could be
done more efficiently.
FLIWAS was developed and the application will predict on the
basis of weather con-

ditions, satellite data, past results, and the height of the water
whether a flood will
occur and what the damage would be in terms of economics,
damage to landscape
and lives. Also, the application is able to calculate proper
evacuation routes. The
implementation of the application has resulted in the water
sector becoming more
integrated and being able to communicate to policy makers what
the result of certain
actions are. It is now more the case than before that water
management professionals
are invited to the negotiation table in matters of urban planning,
where they are able,
[email protected]
on the basis of predictions and scenario sketching to convince
governments that some
plans might not be wise.
Case 4: Determining the Implementation of Congestion
Charging in London
(UK) The city of London has had a large problem with
congestion. In order to
find a solution to this congestion problem the local government
has come up with a
plan to reduce congestion by imposing a charge on all vehicles
that enter the zone in
which the congestion is worst. A computational model was used
to determine where
this zone should be so the location of the zone would be most

effective in not only
reducing congestion but also gaining the government enough
money to reinvest in
public transportation and cycling facilities. On the basis of
traffic data, alternative
routes, and public transportation plans the organization Traffic
for London had de-
cided on a zone in which the measures are implemented. The
application to do so
finds its basis in scenario sketching so different alternatives of
the location of the
zone could be viewed with their effects.
Case 5: Predicting and Containing the Outbreak of Livestock
Diseases
(Germany) Due to European regulations and after the outbreak
of mouth and foot
disease in the 1990s which caused significant financial damage,
the German govern-
ment decided to centralize all information on contagious
livestock diseases into one
application, TSN (TierSeuchenNachrichten). The application
holds information on
farms and animals. Further, the application will make scenarios
on how to contain
and prevent outbreaks of contagious diseases. On the basis of
the contagiousness
of the disease, the estimated health of animals, natural borders,
wind and weather
conditions, and the location of farms, a decision can be taken on
what measures to
take. These measures include the killing of the animals,
vaccination of the animals,
or installing a buffer zone in which no traffic is allowed. The
German government
appointed the Friedrich Loeffler Institute with the task to

develop and manage the
application.
Case 6: Predicting Particular Matter Concentrations (the
Netherlands) Particulate
matter in recent years has become an issue more and more prone
to attention. Due
to European regulations, the countries in the EU are to make
sure the concentration
of particulate matter in the air does not exceed a set norm.
Therefore, whether
buildings and roads can be built becomes dependent on this
norm, not only for
the effect on air quality by the building process but also for the
effect of the plans
once in use. Applications have been made to predict the
potential concentrations
of particulate matter after implementation of building plans, the
outcome of the
prediction determines whether a building can be built. The
problem in this case lies in
the fact that the way to calculate particulate matter to begin
with is unclear, scientists
are not sure of the calculation as of now, the health effects are
not clear as well, just
like the prediction itself. Furthermore, other NGOs have made
their own application
to predict concentrations, in which mostly the outcome differs
significantly from
the applications local governments use. This causes each
building process to be
reevaluated for their legitimacy, and this causes a lot of
distrust.
[email protected]

10.6 Analysis
When analyzing our empirical data on the basis of the core
characteristics earlier
established we find a number of things. These will be discussed
below.
Values in Data We have distinguished in trust and margin of
error and there are
some trends to be discovered in our six cases. First, we find that
the trust and the
reliance on the data in the model are large in the cases 1–4 and
low in the cases 5 and
6. In order to explain this we must realize that this in fact has
no causal relation to the
data itself but to the cases in specific and the actors involved.
What we find is that in
cases 1–4 the actors involved in the cases share a common goal
and common values,
in the cases 5 and 6 there are several groups of actors with
different goals. The trust
an actor has in the data can, therefore, be explained by the trust
and common values
and goals he has with other actors. When there are different
goals among actors or
groups of actors, we find that the trust in the data itself in the
model decreases. When
relating this back to our theoretical notions we find that a
determinist viewpoint
would be difficult to hold since the trust in the data does not
depend on the manner of
collection of data or on the model itself but on the diversity of

the goals and values
of actors involved. It must be noted however, that each of the
actors, both in the
cases in which there is trust in the data as well as those cases
without trust in the
data themselves do hold a deterministic viewpoint. They feel
that the data will lead
to better solutions in cases 1–4 and in cases 5–6 they believe
the data will only lead
to a politically motivated outcome serving another actor.
When looking at the margins of error we find that the margins
of error are relatively
high in all cases. This can be explained by the large number of
variables within the
cases and their complex interrelation. In all cases, those actors
involved acknowledge
these errors but also realize that politicians want to hear a
nominal “yes” or “no”
answer. Therefore, these margins of errors disappear in the
communication between
the experts and policy makers as the experts simplify the
presentation of their results.
While the trust in the data and the reliance on the model is high
in most of our cases,
we still find a high margin of error which is only acknowledged
by actors in cases in
which the trust in the data is low. This shows us that the
objective margin of error will
only be perceived as a “problem” or an “issue that needs to be
taken into account,”
when there is little trust in data. Not only are these margins
emphasized but also on
the basis of these margins actors accuse each other of
manipulation of the model and
the data for their own political goal. Taking this into account it

can be concluded that
for both the trust in the data and the margins of error, the group
of actors and their
goals, are determinant for the course of the process. When
actors agree on goals the
trust in the data is high and margins of error are neither
communicated, nor seen as
a problem. When actors do not agree on the political goal, trust
is low, the margins
of error are emphasized, and the manipulability of the data is
communicated very
frequently.
Values in the Model When we look at the values in the model
itself, we have
distinguished between ownership and beliefs. In terms of
ownership, it can be found
[email protected]
that most models, except for the case 6 are owned and
developed by one organization.
Therefore, it is the case that they have a monopoly on the
information generated by
the model, which grants them the power to use this monopoly in
terms of decision
making. Only in cases 2 and 6 we see that other actors use the
data and the information
to build their own model. In both cases, this has led to conflict.
We also find that in
case 5 some issues of ownership have occurred, but these issues
were only addressed

by actors not sharing the same political goal as the actors who
owned the application,
they were not granted access, while other actors who did agree
with the political goals
of the owner were granted access. What this leads us to
conclude is that ownership
by actors who share different political goals will lead to a
conflictuous process of
policy making. The manner of ownership or monopolization of a
model by a (group
of) actor(s) with the same political goal, therefore, does
influence the outcome of the
policy-making process as well as the process itself.
In terms of beliefs or trust in the model, we find the same
results as for trust in the
data, which would appear logical. The explanation of why some
models are trusted
and others are not, is the same explanation as for the trust in the
data. In cases with
actors with different goals, the trust in the model is generally
low, the situation is
conflictuous, and those opposed do not trust the model and
accuse the owner of the
model of distrustfulness, using the application for their own
political, motivation,
and manipulation of the model so their preferred outcome will
prevail. Here as well
we find that actors individually hold a fairly deterministic
viewpoint regarding the
model in question. Additionally, it shows us that data in the
model and the model
itself cannot be seen separate from each other in terms of trust.
Values in the Decision-Making Process When we look at the
values of the decision-

making process we have distinguished between authority and
multiactor setting. We
can find that in terms of authority an interesting situation
exists. In some cases, cases
1, 4, and 5, there is a clear line of authority. In these cases, it is
agreed upon who
should provide the data, the model, and the results on the basis
of which policy
should be made. In most cases, this is institutionally arranged,
by legally making
one organization responsible. In some cases, this is arranged by
a code of conduct
in which all agree this to be the organization dealing with this
topic. In the cases
2, 3, and 6, we find that there is no clear agreement on who
holds authority. This
can be explained by the idea that more than one organization is
using the same data
but reaches different policy conclusions based on this data
which eroded authority
(cases 2 and 6) or by monopolization issues, in which one
organization used to hold
authority over policy decisions but because of the emergence of
the model and the
monopolization of this information authority has become
blurred (case 3). The lack
of clear lines of authority accounts for a situation of conflict,
different actors are
trying to use the outcomes of the models for their own political
goals. A very social
constructivist situation in which technological frames of actors
create a situation in
which they believe the outcome of the model supports their
claims, policy solutions,
and goals.

A final factor is the number of actors and their relation with one
another. Natu-
rally, a number of different interests can be found in each case
and a clear trend on
[email protected]
this variable is not to be found, it seems to be rather case
specific. In case 1, we see
that there is a high number of actors involved in the decision-
making process and
that the diversity of the actors regarding their political goals
and convictions is also
high. This complicates the decision-making process. Case 2
shows that the number
of actors involved is somewhat low in the first case but the
main authorities share
the same convictions, which ostensibly simplifies the decision-
making process. The
fact that opponents have organized themselves efficiently and
have had access to the
same data but with different results means that in the end the
decision making was
as tiresome as in the first case. Cases 3 and 5 provide us with
insight on how a group
of actors can become very powerful in the decision-making
process because they
have the monopoly on the information. In case 4, it becomes
clear that institutional
arrangements can reduce complexity since only one
organization has formal author-
ity. Finally, case 6 tells us that the lack of trust, the enormous

difference in political
opinion, and the lack of one owner and authority make decision
making so complex
that a decision that is seen as legitimate by all actors becomes
impossible. In general,
taking the previous part of the analysis into account it shows
that the actual number
of different actors has no influence, it is the number of different
goals they hold.
10.7 Conclusions
This chapter aims to answer the question how the designing and
using models and the
communication between designer and policy maker influences
the process of public
decision making in terms of values. Analysis of the six cases
shows that this influence
is considerable. We find that a large diversity exists within the
cases on the different
values we have evaluated and that the impact of these values,
perceptions, and beliefs
is very important for the process of policy making. This is
because when actors think
and believe the same things, they tend to think that their work
encompasses all possi-
ble variety. In other words, being of the same mindset triggers
unintentional selective
blindness. Consequently, the models are not under close
scrutiny and decisions made
using a certain model reflect the biases that were
unintentionally programmed into
the model. For example, in the case of predicting the outbreak
of livestock diseases,
it appeared that the option “clearing of animals” could never be
a feasible outcome

of the model, whereas in reality it could be a possible answer.
A high diversity in actors raises a situation of conflict as
multiple actors bring
forward their own perspectives that are in many cases only
partly convergent and
downright contradictory in some cases. In other words, higher
diversity leads to
more obvious clashes of goals, beliefs, and values. The models
that are used and
the results that the models generate are being questioned more
explicitly and openly,
consequently leading to a higher perceived complexity as it
becomes much more dif-
ficult to reach a quick conclusion. Diversity or lack, thereof, is
partly a design feature
of the institutional dimension, partly an unintentional process
between actors who
trust and believe each other. As a design feature it emerges
when the commissioning,
developing, and using models are clustered around one or a
limited set of tightly
[email protected]
coupled actors. Such a concentration of power, where both the
research and the final
decision are strongly linked, causes the actors to develop a bias
towards their own
ideas. Whether this link is institutionally determined or not,
does not influence this.
The models are consequently used as such. When such close

links are contested or
absent, the diversity raises because of the possibility of
questioning current ideas and
beliefs. As an unintentional process, it emerges when actors
develop relationships of
trust and belief. Although actors are not aware of it, such
relationships still promote
convergence of thinking, thus decreasing contradictory ideas.
If anything, the current research shows that models and data
never speak for
itself. On the contrary, they are heavily influenced by the social
dynamics of the
context they are developed in. Not only, as social
constructivists claim, because of
the values attributed to the models while they were used in their
own political and
institutional context but also as the technological determinists
argue, because of the
values in the model itself. They have been put there by the
designers’ choice, often
unintentionally; however, public policy makers are unaware of
these choices. This
leads us to conclude that computational models have a very
large influence on the
decisions that are made, as our case study shows. Following this
we can argue that
the potential power of these models within public decision
making is substantial.
Even though throughout this chapter we have argued that
humans do have agency
over technology our case studies show that this agency at some
points is limited
to the designer of the application and not to the public decision
maker using the
application in order to come to a decision. This raises questions

for the future in
which we ask ourselves that when computational models are
normative because they
cannot mimic full reality and instead reflect the developers’ and
users’ ideas, what this
means for those elected officials using the computational model
for decision making.
We observed often that belief in the model as the right
descriptor and predictor of
reality was almost absolute at the level of policy makers. “If it
has a number it must
be true.” We argue that this number is as much a reflection of
the developers’ ideas
as it reflects reality.
Furthermore, we can conclude that not only the designer of the
model is able to
place values into the model but these values are also
incorporated by the users of
the application. Not because through a technological frame,
they attribute a certain
meaning to a technology but because the data in the model itself
is not flawless. This
means that is possible that models used by different actors
generate entirely different
outcomes. It is not as much a design flaw of the model but
rather a consequence of
the complexity of values of data and models. However, public
decision makers are
often unaware of this and regard the model as being neutral and
value-free. Designers
often in their communication with public decision makers are
trying to simplify their
message and are trying to hide the normative biases.
Concluding, we can state that while the technology debate

remains an ongoing
debate, in terms of computational models and public decision
making it is also
important to research the relationship between the designer of
the model and the
public policy maker, the role of the designer and its interaction
with end users and
policy makers should be further researched. The nature of this
relationship accounts
partly for a more deterministic or more social constructivist
view on the side of the
[email protected]
public policy maker regarding technology. Therefore, this
chapter cannot conclude
whether technology should be viewed in either a social
constructivist manner or a
deterministic manner, but can conclude that different actors
view the same technology
in different epistemological manners.
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[email protected]
Chapter 11
The Psychological Drivers of Bureaucracy:
Protecting the Societal Goals of an Organization
Tjeerd C. Andringa
Bureaucracy is the art of making the possible impossible
–Javier Pascual Salcedo
A democracy which makes or even effectively prepares for
modern, scientific war must necessarily cease to be democratic.
No country can be really well prepared for modern war unless it
is governed by a tyrant, at the head of a highly trained and
perfectly obedient bureaucracy.
–Aldous Huxley
Whether the mask is labeled fascism, democracy, or dictatorship
of the proletariat, our great adversary remains the
apparatus—the bureaucracy, the police, the military. Not the
one facing us across the frontier of the battle lines, which is not
so much our enemy as our brothers’ enemy, but the one that
calls
itself our protector and makes us its slaves. No matter what the
circumstances, the worst betrayal will always be to subordinate
ourselves to this apparatus and to trample underfoot, in its
service, all human values in ourselves and in others.
–Simone Weil
Abstract This chapter addresses the psychological enablers of
bureaucracy and

ways to protect bureaucrats and society from its adverse effects.
All organizations
benefit from formalization, but a bureaucracy is defined by the
dominance of coer-
cive formalization. Since bureaucrats are not bureaucratic
among friends, one might
ask what changes someone at work into a bureaucrat and why do
bureaucrats and
bureaucratic organizations exhibit their characteristic
behaviors?
The pattern of behavior arises from fundamental psychology and
in particular
(1) our capacity for habitual behavior, (2) the difference
between intelligence as
manifestation of the coping mode of cognition and
understanding as manifestation
of the pervasive optimization mode, and (3) the phenomenon of
authoritarianism
as the need for external authority through a lack of
understanding of one’s living
environment. The combination of these phenomena leads to a
formal definition, the
“Bureaucratic Dynamic,” in which the prevalence of coercive
formalization scales
T. C. Andringa (�)
University College Groningen, Institute of Artificial
Intelligence and Cognitive Engineering
(ALICE), University of Groningen, Broerstraat 5, 9700 AB,
Groningen, the Netherlands
10.1007/978-3-319-12784-2_11

[email protected]
222 T. C. Andringa
with “institutional ignorance” (as measure of how well workers
understand the con-
sequence of their own (in)actions, both within the organization
as well on the wider
society) and “worker cost of failure.”
Modern organizational theory has become progressively more
aware of the inef-
ficiencies and dangers of bureaucracy. The framework
developed in this paper can
be applied to protect society, organizations, and workers from
the adverse effects of
bureaucracy. Yet while non-bureaucratic organizations can
produce excellence, they
also rely on it and are therefore somewhat fragile. Improved
protective measures can
be developed using the framework developed in this chapter.
11.1 Introduction
In 2005 a Dutch insurance company aired a television
commercial1 in which they
showed a mother and daughter trying to collect their “purple
crocodile” at a lost-
and-found department. The clerk reaches for the missing object
form—just next to
the huge purple crocodile—and hands it to the mother to be
filled in. After a few
attempts the form is filled-in to the clerk’s satisfaction and he

instructs the family to
collect the missing object the next morning between 9 and 10
a.m. “But it’s there”
the mother remarks. “Yes it is there” the clerk responds with an
empty expression to
this completely irrelevant remark.
Clearly, the original societal role of this lost-and-found
department was replaced
by a new goal: procedural correctness, irrespective of the state
of the world and the
implications of following procedure. The commercial ended
with the remark that
less bureaucracy is preferable.
We all know these blatant examples of bureaucracy, where form
and proce-
dure have become stultifying, any genuine empathy and human
decency is absent,
and the organization is no longer serving its original purpose
efficiently. Yet the
most shocking, albeit not normally acknowledged, aspect of
these examples is that
bureaucrats—outside the direct working environment—are just
regular law-abiding
individuals who might do volunteer work and who will gladly
return something
without insisting on a form to fill in first: among friends no-one
is a bureaucrat.
I consider bureaucracy and bureaucratic mindsets as suboptimal
or even patho-
logical for the organization because it has adopted self-serving
goals in favor of its
original societal goal and for the bureaucrat because he or she is
reduced—at work—

to a shadow of his or her full human potential. This paper
addresses the psychological
reasoning on which this opinion is based.
Administration is not necessarily bureaucratic. And
formalization—the extent
of written rules, procedures, and instructions—can both help
and hinder the overall
functioning of the organization. In this chapter, I define
bureaucracy as the dominance
of coercive formalization within professional organizations.
Coercive formalization
1 https://www.youtube.com/watch?v = 2Rw27vcTHRw
[email protected]
11 The Psychological Drivers of Bureaucracy 223
takes away autonomy and changes a worker into the direction of
an automaton:
someone who can be easily replaced by information technology
or a robot.
All human activities benefit from some form of formalization.
Formalization
allows automating routine tasks, to agree on how to collaborate,
determine when
and how tasks should be executed, and when they are finished.
As such, procedures
should not be changed too often so that they become and remain
a stable basis
for organizational functioning. Yet procedures should also not
be too static and too

strictly adhered to so that they lead to stultification, suboptimal
task execution, and,
above all, to loosing track of the societal goals of an
organization. These are all signs
of bureaucracy.
The bulk of this chapter comprises the formulation of a
psychological framework
that explains the phenomenology of bureaucratic and non-
bureaucratic organizations.
This framework is based on the two cognitive modes—the
coping mode and the per-
vasive optimization mode—that we defined in an earlier paper
on Learning Autonomy
(Andringa et al. 2013). Since bureaucracy has a lot to do with
preventing worker au-
tonomy, it is not surprising that our paper contains relevant
ideas. What I found quite
surprising, and highly relevant, was how well the “coping mode
of cognition” fitted
with the bureaucracy literature (Adler and Borys 1996; Weber
1978). In Learning
Autonomy we had addressed the phenomenon of
authoritarianism: the need for and
acceptance of centralized or group authority. In this chapter I
show that bureaucracy
is a manifestation of authoritarianism in the context of
professional organizations.
Based on the defining characteristics of authoritarianism, I
predict the incentives for
coercive formalization, and with that the incentive for
bureaucracy, as follows:
Incentive for coercive formalization = Institutional ignorance ×
Worker cost of failure
I call this the Bureaucratic Dynamic. Maximizing “institutional

ignorance” and
“Worker cost of failure” leads, via psychological mechanisms
outlined below, in-
evitably to more bureaucracy. Fortunately, minimizing these
will reduce bureaucracy.
I predict that this formula can be used as an effective means to
improve our under-
standing of the phenomenon, to improve effective anti-
bureaucracy measures, and
to expose ineffective ones.
This chapter provides a transdisciplinary approach of
bureaucracy. Transdisci-
plinarity entails that I will ignore traditional (and often quite
arbitrary) disciplinary
boundaries and I will address multiple description levels; in
particular a number of
subdisciplines of psychology (fundamental science level),
organizational research
(applied science level), policy (normative level), and ethical
considerations (value
level) (Max-Neef 2005).
I start in Sect. 11.1, with an interdisciplinary analysis
addressing how the diversity
of bureaucracy can be understood through the degree and the
type of coercive and
enabling formalization. This analysis outlines many
manifestations of bureaucracy
that, together with the observation that no one is a bureaucrat
among friends, demand
a psychological explanation.
Section 11.2, forms the fundamental science bulk of this
chapter. In it, I start
with habits as effective and goal realizing activities that require

only a minimal
involvement of the higher faculties of mind because the
behavior originates from
[email protected]
224 T. C. Andringa
and is guided by the (work) environment. This is followed by
the observation that
the two modes of thought we have defined in our earlier paper
on Learning Autonomy
(Andringa et al. 2013) match bureaucratic and non-bureaucratic
strategies. The two
modes differ in the locus of authority: external for bureaucracy
and internalized
for non-bureaucratic approaches. The centrality of the concept
of authority becomes
even clearer when I change the perspective to political
psychology and in particular to
the opposition authoritarians–libertarians. These groups of
people differ in whether
or not they (unconsciously) consider the complexity of the
world too high to act
adequately and feel comfortable. The shared feelings of
inadequacy motivate them
to instill order through coercive formalization and group or
centralized authority: a
phenomenon known as the “Authoritarian Dynamic.” This
dynamic, in this chapter,
applied in the context of professional organizations, drives the
growth or demise of
bureaucracy according to the “Bureaucratic Dynamic.” Section
11.2 closes with a

short reflection on the (serious) detrimental effects of
bureaucracy might have on
bureaucrats (value level).
This chapter closes with a shorter section on how three modern
management
paradigms (applied science and policy level) can be classified
according to the
prevalence of the coping or the pervasive optimization mode.
This entails, in some
sense, that experiential evidence has already discovered what I
argue from a psy-
chologically informed perspective. Yet this perspective
complements and enriches
the experientially acquired understanding. I then direct attention
to non-bureaucratic
or “libertarian” organizations. One crucial aspect of these is
that they not only are
able to deliver pervasive optimization of all organizational
roles, they also depend on
it. This entails that they are fragile and easily wrecked by
workers with insufficient
institutional understanding. I give examples of how this
degradation process typi-
cally occurs and indicate a number of “red flags.” I end the
chapter with a number
of conclusions and observations.
11.2 Characteristics of Bureaucracy
This section is based on the analysis of organizations with
different types, levels
and forms of bureaucracy by Adler and Borys (1996). They
provide an insightful and
fairly comprehensive analysis of bureaucracy and its diverse
forms. In addition Adler

and Borys propose a structured typology of organizations that
matches very well with
our recent paper on open-ended (lifespan) development and in
particularly with the
development of bounded or full autonomy (Andringa et al.
2013). Taken together,
these two articles provide an interesting generalized perspective
on bureaucracy
and, in general, on some foundational perspectives on human
autonomy and human
organizations.
Adler and Borys address the issue of worker autonomy in many
different examples
and remark “that much of the literature on the sociology of
scientists and engineers
asserts that employees in these occupations typically aspire to
high levels of auton-
omy in their work and that bureaucratic formalization
undermines their commitment
[email protected]
and innovation effectiveness.” Yet other employees might
benefit from bureaucratic
formalization. Consequently:
Organizational research presents two conflicting views of the
human attitudinal or outcomes
of bureaucracy. According to the negative view, the
bureaucratic form of organization stifles
creativity, fosters dissatisfaction, and demotivates employees.

According to the positive
view, it provides needed guidance and clarifies responsibilities,
thereby easing role stress
and helping individuals be and feel more effective.
In terms of autonomy, it seems that the bureaucratic form of
organization stultifies the
functioning of highly autonomous and motivated employees,
while it actually pro-
vides the less autonomous employees guidance and
effectiveness in roles in which
they would otherwise not be able to function. So bureaucracy
constrains the au-
tonomous employees, but enables the less autonomous to
contribute more effectively.
Accordingly, Adler and Borys conclude that the study “of the
functions and effects
of bureaucracy has split correspondingly with one branch
focused on its power to
enforce compliance from employees assumed to be recalcitrant
or irresponsible and
the other branch focused on bureaucracy’s technical efficiency.”
Based on this observation and a number of examples, Adler and
Borys propose
two structural dimensions for organizations: the type of
formalization, spanning a
continuum from coercive to enabling, and the degree of
formalization from low to
high. This leads to a two-dimensional representation with four
quadrants resulting
from the intersection of the axes as depicted in Fig. 11.1. The
degree of bureaucracy
is represented by the diagonal connecting a high degree of
coercive formalization—
characteristic of a highly bureaucratic or “mechanistic”

organization—to a low
degree of enabling formalization in the non-bureaucratic, or
“organic,” organization.
The other diagonal corresponds to a highly centralized, or
“autocratic,” organization
or a decentralized “enabling bureaucracy.”
The key component of this organizational typology is
formalization and Adler
and Borys describe many different aspects of formalization. A
number of these are
summarized in Table 11.1.
It will be clear from Table 11.1 that some degree of suitable
formalization is
highly beneficial, and probably defining for any organization
and as such is broadly
supported. Yet, too much formalization or formalization of an
unsuitable kind will
be detrimental for the employees and the way the organization
realizes its societal
mission and as such enacts its raison d’être.
Adler and Borys couple the two types of formalization—
coercive and enabling—
to perspectives on the organization. The “enabling approach”
considers workers as
sources of skill and intelligence to be activated. This works, of
course, for workers
who enjoy to be challenged, who aspire to develop their skills,
and who feel a personal
or shared pride regarding the work they are performing. In the
“coercive approach”
workers are treated as sources of problems to be eliminated. In
this approach the
opportunism and autonomy of workers (skilled or not) is to be

feared and it leads
almost inevitably to a deskilling approach. Deskilling is, of
course, resented by those
who consider work autonomy and skill-development essential
for personal growth,
but for the less skilled and probably more insecure workers,
who know they will not be
able to contribute effectively without strict and firm guidance,
the coercive approach
[email protected]
226 T. C. Andringa
Fig. 11.1 Types of organization. (Based on Fig. 1 in Adler and
Borys (1996))
is a way to contribute on a higher professional level than they
would otherwise be
able to achieve. Adler and Borys provide many properties of
coercive and enabling
formalization, which are summarized in Table 11.2.
As Table 11.2 shows, the basic logic of the coercive approach is
to curtail the scope
of behavioral options of workers through centralized and/or
(corrective) group au-
thority. In contrast, the basic logic of the enabling approach is
to use diversity of
insights and independent judgment of all employees to improve
all aspects of the orga-
nization (in the context of all its roles and obligations). As such
the enabling approach
relies on a combination of group authority and individual

authority. But note that the
role of group authority differs between the two approaches: in
the coercive approach
it is to signal and correct any deviant behavior, while in the
enabling approach it is
a means to aggregate organizational understanding in a common
mode of working.
Asymmetries in power, of course, promote the coercive
approach, but the same
holds for ignoring or actively suppressing the skills and
knowledge of the workers
since this almost inevitably impoverishes the understanding of
the organization and
as such it leads to organizations that progressively become out-
of-sync with reality:
instead the organization creates its own peculiar realities based
on whatever pleases
the power structure, which progressively makes it more difficult
to apply the ob-
servations, knowledge, and insights of the workers for the
proper execution of the
organization’s societal role.
[email protected]
Table 11.1 Positive and negative aspects of formalization.
(Based on Adler and Borys (1996))
Negative effects of formalization: Positive effects of
formalization:

Higher absences Formalization can increase efficiency
Propensity to leave organization Embrace of well-designed
procedure facilitates task
Physical and psychological stress Performance and pride on
workmanship
Reduced innovation Reduction of role conflict and role
ambiguity
Reduced job satisfaction Increased work satisfaction
Reduced commitment to the organization Reduction of feelings
of alienation and stress
Can help innovation if it capture lessons of prior
Lower motivation Experience or help coordination of larger-
scale projects
broad preference and benefits for routine tasks
Formalization is disfavored if: Formalization is favored if:
Rules benefit managers: especially when
rules are also used to sanction
Work is considered as a cooperative endeavor rather than
the abrogation of autonomy
Bad rules/procedures: Good rules/procedures:
Resented Taken for granted
If possible ignored or avoided Hardly noticed

Adler and Borys couple the motivations (Deci and Ryan 1987)
to participate in
the organization to the type of formalization. The coercive
formalization corresponds
to external (authority enforced, fear of punishment, rule
compliance) or introjected
motivation (internal or esteem-based pressures to avoid harm)
because it does not
tap into whatever is intrinsically motivating for the employees.
The enabling for-
malization does just that: it allows motivation based on
identification with personal
importance or compliance with personal goals. It might even
allow intrinsic moti-
vation in the form of completely unconstrained and self-
determined activities that
involve highly enjoyable states like flow and play.
These motivations—in this order—have been coupled to the
perceived locus of
causality (PLOC), which reflects the degree the individual or
some external author-
ity or influence originates the behavior (Ryan and Connell
1989). It is a measure of
autonomy and agency. The more autonomous the behavior, the
more it is endorsed
by the whole self and is experienced as action for which one is
responsible (Deci and
Ryan 1987). In particular for activities with an external PLOC
individuals do not re-
ally feel a personal responsibility and probably no moral
responsibility as well. This
then suggests that it is possible to realize highly unethical goals
by promoting the
coercive form of formalization: the workers will not feel any

sense of responsibility.
This explains why bureaucracies (or more general hierarchical
organizations subject
to coercive formalization, such as the military, intelligence
agencies, or some multi-
nationals) are so often involved in atrocities. Aldous Huxley’s
quote at the beginning
of this chapter acknowledges this as well.
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228 T. C. Andringa
Table 11.2 Properties of coercive and enabling formalization
Coercive formalization Enabling formalization
Basic attitude Basic attitude
Workers as sources of problems to be eliminated.
Opportunism of workers to be feared: deskilling
approach
Workers as a source of skill and intelligence to
be activated
Key properties: Key properties:
The formal system (e.g., organogram) is leading,
workers exist to serve their role
The formal system exists to enable and support
the workers in executing the societal function of
the organization

Deviation from the protocol is suspect Deviations from
procedure decided by the work-
ers
Procedures often non-transparent to keep knowl-
edge about the organization from the employees
to prevent “creative interaction”
Deviations from the protocol signals the need for
better procedures or methods and are a learning
opportunity
Procedures as assertions of duties (not to help) Procedures help
to explain key components and
codifying best practices
“Global transparency” highly asymmetric, with
procedures that, for example, help to real-
ize a panopticon (so that employees know that
superiors can monitor them at any time)
Procedures to provide insight into personal per-
formance
Global transparency of the organization is a
source of employee initiative and as such a risk
to be minimized
Global transparency provides insight in the role
of processes in the broader context of the orga-
nization as necessary source of innovation and
improvement for the whole organization
Procedures define, in detail, a sequence of steps
to be followed and force the employee to ask

approval for any deviation of the protocol (such
as skipping unnecessary steps)
Forces promoting the coercive formalization: Forces promoting
the enabling formalization:
Asymmetries in power Societal preference for enabling
formalization
Absence of reality checks associated with an in-
ward focus in which local conflicts become more
important than organizational goals
A necessity of a very complex task environment
(such as in times of competitive pressure)
The results of automation (whatever ICT pro-
duces) needs to be communicated and followed-
up to the letter
Automation first replaces routine operations
(their formalization become part of the ICT) and
leads to a demand for more skilled employees
Motivation type: Motivation type:
External (authority enforced, fear of punish-
ment, rule compliance)
Intrinsic motivation (completely
self-determined activities)
Introjected motivation (internal or esteem-based
pressures to avoid harm)
Identified (with personal importance) or inte-

grated (compliance with personal goals)
[email protected]
In this section I have outlined a number of properties of
bureaucracies for which
I will propose the psychological underpinnings in Sect. 11.3.
This section will focus
on why the phenomena outlined before, emerge inevitably from
basic psychology.
11.3 Psychological Roots of Bureaucracy
11.3.1 Habits
Since the formalization, and therefore automation, of behavior
is an integral part
of bureaucracy it makes sense to address the topic of habits and
habitual behavior
because the psychological term “habit” refers to an automatic
response to a specific
situation (Ouellette and Wood 2003; Wood and Neal 2009). The
ability to behave ha-
bitually is a wonderful thing, because it means that we have
learned to do something so
efficiently that our minds are kept free for other things. Habits
can be nested so that for
example, the habit of driving can be part of daily, weekly,
monthly, or yearly routines.
Habits are well-trained perception–action relations that are
efficiently combined

so that they address our daily affairs with minimal mental effort
and their combination
may lead to an endless variety of effective, while still
seemingly effortless, behaviors.
During the execution of a habit it is the environment that
determines your actions:
if there is a door on your path, you open it. You will not
normally initiate the door
opening behavior without a door. You can of course, willingly,
try to activate door-
opening behavior in the middle of a lawn. But nothing in the
lawn-environment will
activate this particular behavior. This holds for steak cutting,
hair combing, wall
painting, and turning the page of a newspaper: you can do it
whenever you want,
but it is only productive (and looks less silly) if you let the
environment activate the
desired behavior. That is the reason why each habit is activated
in situations that
provide the affordances to activate the behavior.
The way we respond to social or work situations is also for a
large part habitual.
In particular we find “that mental content activated in the
course of perceiving one’s
social environment automatically creates behavioral tendencies”
(Bargh 2010). The
first time we encounter some situation we might not know what
to do and to give it
all our attention to decide on appropriate behavior, but after a
few times practice, the
situation is neither novel nor challenging and we respond
habitually and according to,
for example, the stereotypes activated by the environment.
Because of the flexibility

of habitual components and because of the minimal mental
effort it costs to combine
them adaptively, most of our daily activities are habitual, which
is good because
during habit execution we are left with ample opportunities to
direct our attention to
interesting, useful, or important things.
William James, one of the first and still one of the greatest
psychologists, had much
to say on habits. In fact he addresses the topic of habits as one
of the foundations
of psychology. And what is relevant for this chapter, he
explicitly defined habit, 125
years ago, as the flywheel that keeps society (and the
organizations that constitute
it) stable (James 1890, p 16–17).
Habit is thus the enormous flywheel of society, its most
precious conservative agent. It alone
is what keeps us all within the bounds of ordinance, and saves
the children of fortune from the
[email protected]
230 T. C. Andringa
envious uprisings of the poor. It alone prevents the hardest and
the most repulsive walks of
life from being deserted by those who are brought up to tread
therein. It keeps the fisherman
and the deckhand at sea through the winter; it holds the miner in
its darkness, and nails the
countryman to its log-cabin and its lonely farm through all the

months of snow; it protects
us from invasion by the natives of the desert and the frozen
zone. It dooms us all to fight out
the battle of life upon the lines of our nurture or our early
choice, and to make the best of a
pursuit that disagrees, because there is no other for which we
are fitted, and it is too late to
begin again.
So habits do not only free our minds for more important things
they also keep us
within the bounds of the status quo or pursuits once started.
Habits are not a genetic
inevitability, but are the result of the way we are raised,
educated, and introduced
in our professional lives. James defines the role of education
therefore in terms of
acquiring habits.
The great thing, then, in all education, is to make our nervous
system our ally instead of
our enemy. It is to fund and capitalize our acquisitions, and live
at ease upon the interest
of the fund. For this we must make automatic and habitual, as
early as possible, as many
useful actions as we can, and guard against the growing into
ways that are likely to be
disadvantageous to us, as we should guard against the plague.
The more of the details of
our daily life we can hand over to the effortless custody of
automatism, the more our higher
powers of mind will be set free for their own proper work.
The original text had an emphasis in italic, here I have added an
emphasis in bold to
focus on the fact that habits may not necessarily be beneficial to

us, they might in
fact be more beneficial to whoever has defined the status quo
and now benefits from
the habitual continuation of that status quo. This status quo can
typically be some
sort of working or living environment that has not been
designed by the individual
himself, but results from some reasoning that is predominantly
or wholly beyond
the individual’s understanding. In a situation like this we have,
as far as work is
concerned, no “opportunities to direct our attention to
interesting, useful, or important
things.” In these conditions habitual behavior dominates the
work floor and very little
of the behavior that characterizes the individual in the rest of its
life is visible.
This already explains part of the bureaucratic syndrome by
answering, at least par-
tially, the question “What shuts down so much of a bureaucrat’s
mental capabilities?”
The partial answer is that a difficult to understand environment
that effectively acti-
vates habitual behavior leads to the activation of habitual
behavior while denying the
bureaucrat self-selected opportunities of intrinsic interest,
usefulness, or importance.
Consequently, absent the understanding of their significance in
the bigger scheme
of things, the true bureaucrat has no real responsibilities other
than maintaining the
conditions in which habitual functioning is facilitated, which is
exactly what I saw
in the introductory example.

The conclusion that the bureaucrat’s single or main—self-
imposed—
responsibility is to uphold the conditions for its own habitual
functioning explains
to a large degree the stability of bureaucracies. But note that
this is especially the
case for work environments that exceed the scope of
understanding of workers and
management: only here they have no choice but to uphold the
conditions in which
they function habitually. With sufficient organizational
understanding, workers and
management can break this cycle. We will return to this topic in
the subsection on
“Authoritarianism” (Sect. 11.3).
[email protected]
11.3.2 Two Modes of Thought
The previous subsection already separated a habitual mode of
thought, which requires
very little attentional control, and forms of cognition that are
not (yet) habitual be-
cause they do require highly focused attention, for example
because they are new,
ever changing, or otherwise engaging or challenging. This
opposition arises from two
large families of cognitive phenomena that McGilchrist (2010)
(with extensive justi-
fication and highly compelling historical support) couples to the
left and right brain

hemispheres. In a recent paper (Andringa et al. 2013) we
generalized McGilchrist’s
interpretations as two complementary modes of cognition: the
coping mode and the
pervasive optimization mode.2
The coping mode is concerned with control: with preventing
things (the whole
world actually) from spinning out of control. Problem solving
and the suppression of
interfering diversity are central concepts for this mode. The
pervasive optimization
mode on the other hand is, as the name suggests, concerned with
the optimization of
all processes in the context of everything else. Where the
coping mode is concerned
with the problems of the here and the now, the pervasive
optimization mode is
concerned with promoting the likeliness of beneficial states in
the near and distant
future; both here and elsewhere, and for yourself (body and
mind) as well as the rest
of the world (family and friends, and the natural and social
environment). Where
the coping mode is highly focused and aims at tangible results
in a structured and
predictable way, the pervasive optimization mode is much more
diffuse; it has no
sequential demands and does not necessarily lead to directly
tangible results. It does
however set-up, in a statistical sense, the conditions for an
unproblematic future. The
coping mode relies on situational control and intelligent
problem solving skills. The
pervasive optimization mode relies on a broad understanding of
the world and its

dynamics in combination with the skills to relate to and work
with these dynamics
(Andringa et al. 2013).
The concept of “intelligence,” especially as conceptualized and
measured in an
IQ-test, summarizes the coping mode because it measures one’s
ability to produce
standardized and expected answers to self-contained problems.
Intelligence is proven
through the ability to solve problems posed by others. The
minimal capacity to do
this is simply by reproducing and applying appropriate formal
operations without
understanding neither the problem nor the situation that gave
rise to it. This rule-
application ability—apparent as formalization—is capitalized
on in a stereotypical
bureaucracy.
This can be contrasted to the concept of “understanding”—
according to the New
Oxford Dictionary “the ability to perceive the significance,
explanation, or cause of
2 The term pervasive-optimization mode has been introduced in
this paper. In Andringa et al. (2013)
we did not use a single term and we described this mode as
cognition for exploration, disorder,
or possibility. In a recent paper “Cognition From Life”
(Andringa et al. 2015) we introduced the
term cocreation mode of cognition. We decided to use the term
pervasive-optimization mode in this
paper since the term co-creation mode requires additional
explanation.

[email protected]
232 T. C. Andringa
(something)”—which captures strengths of the pervasive
optimization mode. If you
understand something you can use it not only reproductively or
in a scripted way,
but you know how to apply it in novel and open application
domains. Consequently
you can prove your depth and breadth of understanding through
realizing novel or
nonstandard results in the world. Conversely you proof your
lack of understanding by
making a mess of your live (indicating the utter failure of
pervasive optimization).
Another way to proof your lack of understanding is by reducing
your life to an
existence where very little novel or nonstandard happens (e.g.,
the extension of a
bureaucratic attitude to the rest of life). In positive terms, the
discovery of relations
(between everything) and the detection of possibilities (in
oneself, in others, at work,
or in the whole of the environment) is strength of the pervasive
optimization mode.
Returning to the example I started with. A bureaucrat is
unlikely to act bureau-
cratically when not at work and especially not while among
friends. The pervasive
optimization mode seems, therefore, the default mode, while the
coping mode is
a fall-back mode that shines when the pervasive optimization

mode was unable
to prevent immanent or pressing problems. Interpreted as such,
a bureaucracy is
a working environment that forces (coerces) employees into a
problem-solving,
problem-preventing, or problem-control mode: the coping mode.
As outlined in our earlier paper on Learning Autonomy
(Andringa et al. 2013), the
pervasive optimization mode assumes autonomous participation
in an open, dynamic,
and infinite world of nested processes that form dynamically
stable and continually
evolving entities: the real continually developing and never
fully graspable world.
For the pervasive optimization mode of being, truth is defined
as accordance with
reality, which is to be tested by acting in the world; as such
understanding and
experiences are essentially subjective. This mode of being is
particularly effective in
situations where new aspects of the dynamics of the world are
to be investigated to
expand one’s thought-action repertoire (Fredrickson and
Branigan 2005) and where
novel and creative solutions are appropriate.
In contrast, the coping mode assumes a closed, static, and self-
contained (and
therefore finite) world, in which entities are symbolic, discrete,
and abstract and in
which perfect solutions may be possible. It is also a mode in
which one is an “objec-
tive” observer instead of a participant. It is the world as
represented in a computer
program: highly functional, perfectly repeatable, and subject to

rational consider-
ations, but ultimately devoid of life. In this mode of being, truth
is defined as the
result of consistent reasoning and consensually agreed on
linguistically shared and
presented facts. This mode of being is particularly effective in
situations in which
(immediate) problems have to be solved or addressed in a
detached, rational, stan-
dardized, and communicable way. Bureaucracies, but also
scientific communication,
are typical examples of this.
Because the coping mode assumes a closed, static, and self-
contained (and there-
fore finite) world it needs an external influence to maintain the
conditions in which
it can function in the first place. As we argued in Learning
Autonomy, authorities—
defined as processes or agents that create, maintain, and
influence the conditions
in which agents exist—fulfill this role. The authority for the left
hemispheric coping
mode is either its own right hemisphere or some external
authority such as parents,
[email protected]
leaders, governments, or cultural influences in the broadest
possible sense. In prac-
tice, it is a combination of internal and external authority, and it
defaults to external

authority whenever the right hemisphere is unable to act as
reliable authority. Put
differently, when the right hemisphere is unable to generate a
sufficient level of un-
derstanding of the situation, it cannot remain in the lead and the
left hemisphere
becomes dominant at the cost of surrendering autonomy to some
(actually any) ex-
ternal authority. Importantly, this switch is subconscious. Still
we can become aware
of it through metacognition (like observing a change of
emotions and/or a change
in attitude or strategy). We will return to the role of
understanding in the section on
authoritarianism.
Table 11.3 provides a summary of properties ascribed to the
coping and the per-
vasive optimization mode. It is based on Table 11.1 of Andringa
et al. (2013), which
in turn is based on Chap. 1 of McGilchrist (2010). The remarks
in italic are examples
of a bureaucratic (for the coping mode) and a non-bureaucratic
(for the pervasive
optimization mode) interpretation of these properties. It will be
clear from Table 11.3
that the strengths of the coping mode can be used to illustrate
typical and/or extreme
bureaucratic functioning, while the pervasive optimization mode
can be used to il-
lustrate a non-bureaucratic alternative. Note that the original
table was intended as a
summary of left and right hemispheric strengths to be used in a
quite different context:
that it can be used to illustrate typical properties of bureaucratic
and non-bureaucratic

organizations, is a serendipitous observation that I consider
highly meaningful.
11.3.3 Authoritarianism
At the end of the last subsection, authority was defined as the
ability to create,
maintain, influence, or exploit a living environment (Andringa
et al. 2013). This
entails that whenever individuals do not know how to self-
maintain proper living
conditions, they must rely on some sort of “authority” to keep
living conditions
within manageable bounds. This need for authority scales
inversely with the scope
of inadequacy: the more pervasive the inadequacy, the greater
the need for and role
of authority. Conversely, the better individuals cope with and
maintain their own
living environment—the more they have internalized
authority—the less they need
external authorities. This essential (and existential) need for
authority is the defining
characteristic of the concept of authoritarianism.
Within the domain of political psychology people with a strong
need for au-
thority are known as authoritarians and those who do not as
libertarians (Stenner
2005, 2009, 2009). Authoritarians prefer (centralized) group
authority and unifor-
mity, while libertarians prefer (decentralized) individual
authority and diversity. The
structure and properties of authoritarian behavior have been
studied in detail in “The
Authoritarian Dynamic” by Princeton researcher Karen Stenner

(2005). Authoritar-
ianism is characterized by a strong tendency to maximize
oneness (via centralized
or group control) and sameness (via common standards),
especially in conditions
where the things that make us one and the same—common
authority and shared
values—appear to be under threat.
[email protected]
234 T. C. Andringa
T
ab
le
11
.3
C
og
ni
ti
ve
m
od
es
de

fi
ne
or
ga
ni
za
ti
on
s.
C
om
pa
ri
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pr
op
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ti
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of
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e
co
pi

ng
m
od
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co
gn
it
io
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(a
tt
ri
bu
te
d
to
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e
le
ft
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m
is

ph
er
e)
an
d
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pe
rv
as
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op
ti
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iz
at
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m
od
e
(a
tt

ri
bu
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to
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gh
t
he
m
is
ph
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e)
.
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ss
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it
h
ea
ch
to
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c,
in
it
al
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,
a
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cr
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ti
on
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r
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m
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bu
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cr
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te
rp
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ta
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on
fo
r
th
e
pe
rv
as
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e
op
ti
m
iz
at
io
n
m
od

e.
(B
as
ed
on
A
nd
ri
ng
a
et
al
.
20
13
)
T
op
ic
C
op
in
g
m

od
e
of
co
gn
it
io
n
(L
ef
t
he
m
is
ph
er
e)
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u
re
a
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cr
a

ti
c
in
te
rp
re
ta
ti
o
n
P
er
va
si
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at
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m
od

e
of
co
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it
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(R
ig
ht
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m
is
ph
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e)
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o
n
-b
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re
a
u

cr
a
ti
c
in
te
rp
re
ta
ti
o
n
M
ai
n
co
nc
er
n
P
ri
nc
ip
al

co
nc
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n
is
ut
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it
y
P
ri
or
it
iz
es
w
ha
t
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tu
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is
an

d
w
ha
t
co
nc
er
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us
Te
ch
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ic
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a
li
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p
er

so
n
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el
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b
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to
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es
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x
T
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a
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a
p
ts
it
se
lf
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ex
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ly
a
n
d
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fe
ct
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y
to
th
e
cu
rr
en
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tu
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T

he
w
or
ld
as
a
re
so
ur
ce
T
h
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ie
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ld
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p
p

o
rt
(“
fe
ed
”’
)
a
n
d
co
m
p
ly
w
it
h
th
e
bu
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a
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cr

a
cy
ir
re
sp
ec
ti
ve
it
s
fu
n
ct
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n
in
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S
co
pe
L
oc
al

sh
or
t-
te
rm
vi
ew
B
ig
ge
r
pi
ct
ur
e
(b
ro
ad
er
,l
on
g-
te
rm

vi
ew
).
D
ra
w
s
at
te
nt
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n
fr
om
th
e
ed
ge
s
of
aw
ar
en
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s
F
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cu
s
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a
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th
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a
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iz
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la
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p
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D
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a
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e
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ex
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tt
it

ud
e
to
w
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ds
w
or
ld
R
ep
re
se
nt
in
g
th
e
w
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ld
:
th
e

w
or
ld
as
a
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py
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at
ex
is
ts
in
a
co
nc
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tu
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fo
rm
,s
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ta

bl
e
fo
r
m
an
ip
ul
at
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n
E
xp
er
ie
nc
in
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th
e
w
or
ld
:

th
e
w
or
ld
as
it
is
,o
pe
n
fo
r
no
ve
lt
y
an
d
w
ha
te
ve
r

ex
is
ts
ap
ar
t
fr
om
ou
rs
el
ve
s,
w
it
ho
ut
pr
ec
on
ce
pt
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ns

an
d
no
t
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cu
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w
ha
t
it
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re
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s
S
tr
o
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g
fo
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s
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le
s
a
n
d
p
ro
ce
d
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re
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co
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a

ti
o
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w
it
h
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co
rd
ke
ep
in
g
a
n
d
w
ri
tt
en
(e
xp
li
ci

t)
co
m
m
u
n
ic
a
ti
o
n
.
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n
ly
w
h
a
t
ca
n
b
e
re

p
re
se
n
te
d
w
it
h
in
th
e
bu
re
a
u
cr
a
cy
ex
is
ts
a
n

d
is
su
b
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ct
to
m
a
n
ip
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la
ti
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T
h
e
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rg
a
n
iz

a
ti
o
n
in
a
ll
it
s
fu
n
ct
io
n
s
ca
n
a
d
a
p
t
to
w

h
a
t
“t
h
e
w
o
rl
d
b
ri
n
g
s”
[email protected]
T
ab
le
11

.3
(c
on
ti
nu
ed
)
T
op
ic
C
op
in
g
m
od
e
of
co
gn
it
io
n
(L

ef
t
he
m
is
ph
er
e)
B
u
re
a
u
cr
a
ti
c
in
te
rp
re
ta
ti
o

n
P
er
va
si
ve
op
ti
m
iz
at
io
n
m
od
e
of
co
gn
it
io
n
(R
ig

ht
he
m
is
ph
er
e)
N
o
n
-b
u
re
a
u
cr
a
ti
c
in
te
rp
re
ta

ti
o
n
In
te
re
st
s
In
te
re
st
ed
in
th
e
fa
m
il
ia
r
an
d
th

e
kn
ow
n,
di
ffi
cu
lt
y
w
it
h
di
se
ng
ag
in
g
fr
om
th
e
fa
m

il
ia
r
In
te
re
st
ed
in
th
e
no
ve
l
F
o
rm
s
a
n
d
p
ro
ce

d
u
re
s
fo
rm
th
e
o
n
ly
o
b
je
ct
o
f
in
te
re
st
A
lw
a

ys
in
te
re
st
ed
in
w
a
ys
to
a
d
a
p
t
th
e
o
rg
a
n
iz
a

ti
o
n
to
a
ch
a
n
g
in
g
a
n
d
d
ev
el
o
p
in
g
w
o
rl

d
C
on
ce
rn
ed
w
it
h
w
ha
t
it
kn
ow
s
C
on
ce
rn
ed
w
it

h
w
ha
t
it
ex
pe
ri
en
ce
s
W
h
a
t
ca
n
n
o
t
b
e
d
ea

lt
w
it
h
in
th
e
bu
re
a
u
cr
a
cy
d
o
es
n
o
t
ex
is
t
N

ew
in
fo
rm
at
io
n,
ne
w
sk
il
ls
,e
m
ot
io
na
l
en
ga
ge
m
en
t

C
on
ce
rn
ed
w
it
h
m
an
-m
ad
e
ob
je
ct
s
C
o
m
p
et
en
ce

d
ev
el
o
p
m
en
t
o
f
w
o
rk
er
s
is
n
o
t
sc
ri
p
te
d

bu
t
B
ec
a
u
se
th
es
e
a
re
ty
p
ic
a
ll
y
st
a
ti
c
a
n

d
fo
r
a
p
a
rt
ic
u
la
r
u
se
d
ev
el
o
p
s
o
n
th
e
jo

b
th
ro
u
g
h
in
d
iv
id
u
a
l
ex
p
er
ie
n
ce
a
n
d
d
ev

el
o
p
m
en
t.
W
o
rk
s
sh
o
u
ld
b
e
in
h
er
en
tl
y
re
w

a
rd
in
g
N
on
li
vi
ng
ob
je
ct
s
sp
ec
ia
li
st
.L
iv
in
g
en
ti

ti
es
as
to
ol
s
or
in
st
ru
m
en
ts
M
or
e
co
nc
er
ne
d
w
it
h

li
vi
ng
in
di
vi
du
al
s.
L
iv
in
g
in
di
vi
du
al
s
as
ot
he
r
in

di
vi
du
al
s
P
eo
p
le
a
n
d
a
n
im
a
ls
re
d
u
ce
d
to

n
u
m
b
er
s
th
a
t
ca
n
m
a
n
ip
u
la
te
d
in
a
si
m
il

a
r
w
a
y
a
s
o
th
er
re
so
u
rc
es
E
a
ch
in
d
iv
id
u
a

l
cu
st
o
m
er
h
a
s
to
b
e
tr
ea
te
d
in
th
e
w
a
y
m
o

st
su
it
a
b
le
fo
r
th
e
in
d
iv
id
u
a
l
[email protected]
236 T. C. Andringa
T
ab
le

11
.3
(c
on
ti
nu
ed
)
T
op
ic
C
op
in
g
m
od
e
of
co
gn
it
io
n

(L
ef
t
he
m
is
ph
er
e)
B
u
re
a
u
cr
a
ti
c
in
te
rp
re
ta
ti

o
n
P
er
va
si
ve
op
ti
m
iz
at
io
n
m
od
e
of
co
gn
it
io
n
(R

ig
ht
he
m
is
ph
er
e)
N
o
n
-b
u
re
a
u
cr
a
ti
c
in
te
rp
re

ta
ti
o
n
S
tr
en
gt
hs
T
ho
ro
ug
hl
y
kn
ow
n
an
d
fa
m
il
ia

r
G
at
he
ri
ng
ne
w
in
fo
rm
at
io
n
S
ta
n
d
a
rd
iz
ed
ta
sk

ex
ec
u
ti
o
n
b
y
sp
ec
ia
li
st
s
Im
p
ro
ve
u
n
d
er
st
a

n
d
in
g
o
f
a
ll
re
le
va
n
t
p
ro
ce
ss
es
a
n
d
a
sp
ec

ts
o
f
th
e
jo
b
E
ffi
ci
en
t
in
ro
ut
in
e
si
tu
at
io
ns
an

d
fa
m
il
ia
r
sk
il
ls
G
oo
d
w
he
n
pr
ed
ic
ti
on
is
di
ffi
cu

lt
T
ra
in
in
g
to
re
d
u
ce
er
ro
r
fr
eq
u
en
cy
F
le
xi
b
le

ta
sk
ex
ec
u
ti
o
n
b
y
ge
n
er
a
li
st
s
P
ri
or
it
iz
es
th

e
ex
pe
ct
ed
an
d
ge
ne
ra
te
s
ex
pe
ct
at
io
ns
A
no
m
al
y
(i

nd
iv
id
ua
li
ty
)
de
te
ct
or
:
in
di
vi
du
al
s
H
el
p
st
a
n

d
a
rd
cu
st
o
m
er
s
fi
rs
t,
ir
re
sp
ec
ti
ve
o
f
u
rg
en
cy

A
d
a
p
t
o
rg
a
n
iz
a
ti
o
n
to
th
e
si
tu
a
ti
o
n

T
hi
ng
s
m
ad
e
fi
xe
d
an
d
eq
ui
va
le
nt
:
ty
pe
s.
A
ll
th

at
is
re
-p
re
se
nt
ed
as
ov
er
-f
am
il
ia
r,
in
au
th
en
ti
c,
li
fe

le
ss
ca
te
go
ri
es
M
or
e
ef
fi
ci
en
tl
y
w
he
n
in
it
ia
l
as

su
m
pt
io
ns
ne
ed
to
be
re
vi
se
d
or
w
he
n
ol
d
in
fo
rm
at
io

n
ne
ed
s
to
be
di
st
in
gu
is
he
d
fr
om
ne
w
in
fo
rm
at
io
n.
A

ll
th
at
is
“p
re
se
nt
”
as
ne
w
,a
ut
he
nt
ic
,a
nd
in
di
vi
du
at

ed
E
q
u
a
te
p
eo
p
le
w
it
h
(c
a
se
)
n
u
m
b
er
s.
G

u
a
ra
n
te
ed
eq
u
a
li
ty
in
tr
ea
tm
en
t
o
f
a
ll
ca
se
s

T
h
e
a
b
il
it
y
to
g
u
a
ra
n
te
e
th
a
t
th
e
so
ci
et

a
l
g
o
a
l
is
co
n
tr
ib
u
te
d
to
,
ir
re
sp
ec
ti
ve
th
e

si
tu
a
ti
o
n
o
r
th
e
cu
st
o
m
er
P
re
fe
re
nc
es
P
re
fe

re
nc
es
fo
r
th
in
gs
th
at
ar
e
re
pr
es
en
te
d
as
re
la
ti
ve
ly

in
va
ri
an
t
ac
ro
ss
sp
ec
ifi
c
in
st
an
ce
s,
al
lo
w
in
g
fo
r

ab
st
ra
ct
ed
ty
pe
s
or
cl
as
se
s
of
th
in
gs
P
re
fe
re
nc
e
fo

r
th
in
gs
th
at
ex
is
t
in
th
e
w
or
ld
.S
en
si
ti
ve
to
w
ha
t

di
st
in
gu
is
he
s
di
ff
er
en
t
in
st
an
ce
s
of
si
m
il
ar
ty
pe

fr
om
ea
ch
ot
he
r.
P
eo
p
le
(i
n
cl
u
d
in
g
bu
re
a
u
cr
a

ts
)
sh
o
u
ld
a
d
a
p
t
to
th
e
bu
re
a
u
cr
a
cy
.
N
o

t
vi
ce
ve
rs
a
T
h
e
o
rg
a
n
iz
a
ti
o
n
a
d
a
p
ts
it

se
lf
fl
ex
ib
ly
to
th
e
si
tu
a
ti
o
n
a
n
d
/o
r
ch
a
n
ge

s
in
th
e
si
tu
a
ti
o
n
A
tt
en
ti
on
ty
pe
L
oc
al
na
rr
ow
ly

se
le
ct
iv
e
(h
ig
hl
y)
fo
cu
se
d
at
te
nt
io
n
B
ro
ad
,g
lo
ba

l
an
d
fl
ex
ib
le
at
te
nt
io
n
O
n
ly
sp
en
d
ti
m
e
o
n
fo

rm
a
l
ro
le
s
a
n
d
fo
rm
a
l
p
ro
ce
d
u
re
s.
U
n
a
b

le
(a
n
d
u
n
in
te
re
st
ed
)
to
fo
re
se
e
co
n
se
q
u
en
ce

s
S
p
en
d
ti
m
e
o
n
th
e
ro
le
a
n
d
im
p
a
ct
o
f
th

e
o
rg
a
n
iz
a
ti
o
n
in
th
e
co
n
te
xt
o
f
th
e
la
rg
er

so
ci
et
y
[email protected]
T
ab
le
11
.3
(c
on
ti
nu
ed
)
T
op
ic
C
op

in
g
m
od
e
of
co
gn
it
io
n
(L
ef
t
he
m
is
ph
er
e)
B
u
re
a

u
cr
a
ti
c
in
te
rp
re
ta
ti
o
n
P
er
va
si
ve
op
ti
m
iz
at
io

n
m
od
e
of
co
gn
it
io
n
(R
ig
ht
he
m
is
ph
er
e)
N
o
n
-b
u

re
a
u
cr
a
ti
c
in
te
rp
re
ta
ti
o
n
C
on
st
ru
ct
io
n
of
w

or
ld
S
ta
rt
w
it
h
pi
ec
es
an
d
pu
t
th
es
e
to
ge
th
er
.B
ot

to
m
-u
p
S
ta
rt
fr
om
th
e
w
ho
le
an
d
go
,i
f
re
qu
ir
ed
,i

nt
o
de
ta
il
.T
op
-d
ow
n
O
rg
a
n
iz
e
a
lo
n
g
fo
rm
a
l

ro
le
s
a
n
d
w
o
rk
-b
re
a
kd
o
w
n
st
ru
ct
u
re
S
ta
rt

w
it
h
so
ci
et
a
l
ro
le
o
f
th
e
o
rg
a
n
iz
a
ti
o
n
R

ep
re
se
nt
at
io
n
of
ob
je
ct
s
P
re
fe
re
nc
e
to
re
-p
re
se
nt

ca
te
go
ri
es
of
th
in
gs
,a
nd
ge
ne
ri
c,
no
ns
pe
ci
fi
c
ob
je
ct

s
In
di
vi
du
al
un
iq
ue
in
st
an
ce
s
of
th
in
gs
an
d
in
di
vi
du

al
ge
ne
ri
c
ob
je
ct
s:
in
di
vi
du
al
s
ar
e
G
es
ta
lt
w
ho
le

s
P
eo
p
le
a
n
d
ta
sk
s
a
s
n
u
m
b
er
s
o
r
ca
se
s.

V
a
ri
a
ti
o
n
s
b
et
w
ee
n
ca
se
s
su
p
p
re
ss
ed
U
n

iq
u
en
es
s
o
f
p
eo
p
le
a
n
d
ta
sk
s
d
efi
n
es
th
e
a

p
p
ro
a
ch
.V
a
ri
a
ti
o
n
b
et
w
ee
n
ca
se
s
a
s
g
u

id
el
in
e
S
ol
ut
io
n
li
m
it
at
io
ns
P
ro
bl
em
so
lv
in
g:
si

ng
le
so
lu
ti
on
an
d
la
tc
h
on
to
th
at
A
rr
ay
of
po
ss
ib
le
so

lu
ti
on
s,
w
hi
ch
re
m
ai
n
li
fe
w
he
n
al
te
rn
at
iv
es
ar
e

ex
pl
or
ed
A
ll
a
ct
iv
it
ie
s
fr
a
m
ed
a
s
p
ro
b
le
m
-s

o
lv
in
g
w
it
h
a
si
n
g
le
o
p
ti
m
a
l
so
lu
ti
o
n
A

ct
iv
it
ie
s
a
re
fr
a
m
ed
a
s
a
co
n
ti
n
u
a
l
o
p
ti

m
iz
a
ti
o
n
p
ro
ce
ss
o
f
th
e
w
h
o
le
o
rg
a
n
iz
a

ti
o
n
g
iv
en
it
s
so
ci
et
a
l
ro
le
D
en
y
in
co
ns
is
te

nc
ie
s.
S
up
pr
es
si
ng
no
t
cu
rr
en
tl
y
re
le
va
nt
re
la
ti
on

s
A
ct
iv
el
y
w
at
ch
in
g
fo
r
di
sc
re
pa
nc
ie
s
M
is
m
a

tc
h
es
b
et
w
ee
n
bu
re
a
u
cr
a
ti
c
re
a
li
ty
a
n
d
a

ct
u
a
l
re
a
li
ty
a
re
se
tt
le
d
in
fa
vo
r
o
f
th
e
bu
re

a
u
cr
a
ti
c
re
a
li
ty
D
is
cr
ep
a
n
ci
es
b
et
w
ee
n
re

a
li
ty
a
n
d
ex
p
ec
ta
ti
o
n
s
se
en
a
s
a
le
a
rn
in
g

o
p
p
o
rt
u
n
it
y
P
re
fe
rr
ed
kn
ow
le
dg
e
ty
pe
A
ffi
ni

ty
w
it
h
pu
bl
ic
kn
ow
le
dg
e
P
er
so
na
l
kn
ow
le
dg
e
D
ec

is
io
n
s
b
a
se
d
o
n
w
ri
tt
en
re
co
rd
D
ec
is
io
n
s
b

a
se
d
o
n
in
d
iv
id
u
a
l
ex
p
er
ie
n
ce
a
n
d
u
n
d

er
st
a
n
d
in
g
M
ai
n
em
ot
io
ns
E
m
ot
io
ns
as
so
ci
at

ed
w
it
h
co
m
pe
ti
ti
on
,r
iv
al
ry
,
in
di
vi
du
al
-s
el
f-
be

li
ev
e
(p
os
it
iv
e
an
d
ne
ga
ti
ve
)
A
ll
em
ot
io
ns
.E
m
ot

io
ns
re
la
te
d
to
bo
nd
in
g
an
d
em
pa
th
y
E
m
o
ti
o
n
s

a
ss
o
ci
a
te
d
w
it
h
bu
re
a
u
cr
a
ti
c
in
fi
g
h
ti
n

g,
p
re
se
rv
in
g
o
n
e’
s
p
u
b
li
c
fa
ce
,
co
m
p
et
it

io
n
w
it
h
in
a
n
d
b
et
w
ee
n
d
ep
a
rt
m
en
t
E
m
o

ti
o
n
s
a
ss
o
ci
a
te
d
w
it
h
th
e
fu
n
ct
io
n
in
g
o

f
th
e
w
h
o
le
o
rg
a
n
iz
a
ti
o
n
a
n
d
it
s
cu
st
o

m
er
s
E
m
pa
th
y
U
nc
on
ce
rn
ed
w
it
h
ot
he
rs
an
d
th
ei

r
fe
el
in
gs
E
m
pa
th
ic
id
en
ti
fi
ca
ti
on
.S
el
f-
aw
ar
en
es

s,
em
pa
th
y,
id
en
ti
fi
ca
ti
on
w
it
h
ot
he
rs
R
a
ti
o
n
a

li
ty
h
ig
h
es
t
p
er
so
n
a
l
vi
rt
u
e.
C
a
re
er
a
d
va

n
ce
m
en
t
o
n
th
e
b
a
si
s
o
f
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238 T. C. Andringa
Table 11.4 Child rearing qualities used to determine
authoritarianism
Authoritarians
Children should:
Libertarians
Children should:
Should obey parents Be responsible for their actions

Have good manners Have good sense and sound judgment
Be neat and clean Be interested in how and why things happen
Have respect for elders Think for themselves
Follow the rules Follow their own conscience
Stenner (2005) used the 5 two-option questions about child
rearing values to
determine the degree of authoritarianism that are depicted in
Table 11.4.
The difference between the answers that authoritarians and
libertarians choose is
qualitative: authoritarians teach children to behave in certain
proscribed ways and to
obey external authorities (elders, parents, norms), libertarians
teach children how to
understand the world and how to act responsibly and
autonomously. The difference
between authoritarians and libertarians is, therefore, neither
ideological nor political:
it depends on a combination of two aspects (1) internal or
external authority, and
(2) the depth and pervasiveness of understanding of the current
living environment.
Authoritarianism is, therefore, both, a personality trait and a
state-of-being that is
manifested in some situations, but not in others: the more
individuals are brought into
situations they do not (have learned to) understand and the more
they are pressured to
act, the more they will exhibit authoritarian behavior (See
subsection Authoritarian
Dynamic).

The child rearing qualities reflect the conditions that were
identified for the left
hemispheric coping mode and the right hemispheric pervasive
optimization mode.
As such it makes sense to interpret authoritarian behavior as
behavior guided by
the logic of the coping mode and libertarian behavior as
behavior guided by the
pervasive optimization mode. It also follows that bureaucracy is
a manifestation of
authoritarianism. Which also explains the reason why even
strong bureaucrats are
never bureaucratic among friends: here they are responsible for
their own actions,
expected to have a good sense and sound judgment, to be
interested in others, to
think and decide for themselves, and to follow their conscience.
It is just that their
working environment forces them out of this mode and into the
coping mode.
11.3.4 Two Attitudes Toward a Complex World
According to Stenner (2009) authoritarians are not endeavoring
to avoid complex
thinking so much as a complex world. Authoritarians are just as
intelligent as
[email protected]
libertarians3, but they understand the world more shallowly and

less pervasively.
Consequently, two individuals can experience and interpret a
shared world quite
differently. If it is experienced as too complex to comfortably
deal with, one is in
a coping or authoritarian mode of being. Consequently one’s
highest priority is to
eliminate all sources of diversity to bring complexity down to
manageable levels.
And this can explain why people in the authoritarian mode take
control over de-
cision processes and become subtly or overtly intolerant to
uncontrolled diversity
through, for example, coercive formalization. It is not because
they think they can
do it better—although they might be convinced of that—but
because of a strong
unconscious urge to establish a larger measure of control over
the situation with the
aim to simplify it.
In the libertarian or pervasive optimization mode the
complexity of the world is
well below daily coping capacity and where authoritarians see
problems they see
opportunities. This can actually be problematic because
realizing these opportuni-
ties is bound to lead to further social or organizational
complexification that might
aggravate authoritarians even further. Libertarians are therefore,
quite unwittingly,
major sources of feelings of inadequacy in authoritarians.
And this leads to a one-sided resentment—a shared and
therefore unifying
emotion—toward anything beyond coping capacity among

authoritarians of which
libertarians are typically completely unaware. In fact
encroaching bureaucracy can
be interpreted as a (low-intensity) war between two ways of
facing reality. While
libertarians are unaware of any war being fought (because they
fail to see any need
for it), they can be blamed for co-creating a complex world
surpassing authoritarian
coping capabilities. And authoritarians, with their limited
understanding, share a
deep anxiety and are highly motivated to do something about it
collectively.
This subconscious anxiety motivates to oppose all sources of
complexity, unpre-
dictability, novelty, and growth that complexify, confuse, and
destabilize an ordered
and predictable state of affairs. In fact people in an
authoritarian mode want to
distance themselves from all of these things and the people
(e.g., immigrants, homo-
sexuals, libertarians) that embody or promote them. One driving
emotion is disgust
(Frijda 1986; Inbaret al. 2009): the urge to distance oneself
from an unhealthy or
otherwise harmful object, activity, person, or influence.
Authoritarians in this state
speak quite frankly and clearly about the moral decline that they
see all around them
and that disgusts them (and often enough explicitly worded).
And they are quite mo-
tivated to do something about it. Vocal moral outrage about the
organization losing
its values and morals (typically in response to some gentle
questions about the state

of the organization) is an indication of an organization ready to
become dominated
by an authoritarian mindset and the associated urge to bring the
complexity of the
world/organization back to within coping capacity.
3 Authoritarians might value intelligence more than libertarians.
For example more than half of the
21 Nazi Nuremburg defendants had a superior intelligence
(belonging to the most intelligent 3 to
0.2 %) and only one had average intelligence (Zillmer et al.
2013). This suggests that authoritarians
select on intelligence.
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240 T. C. Andringa
In fact this same process seems to occur on a societal scale
during revolu-
tions. In his seminal book on revolutions in the nineteenth and
twentieth century,
Billington (1980) explicitly mentions the revolutionary’s strong
motivation to reduce
complexity when he concludes:
The fascinating fact is that most revolutionaries sought the
simple, almost banal aims of
modern secular men generally. What was unique was their
intensity and commitment to
realizing them.
Billington concludes that popular revolutions invariably aim to
bring society back to

a simpler state of affairs. Those revolutions, equally invariably,
seem to coincide with
periods of increased intolerance (against moral violators,
freethinkers, or libertarians)
and the rise of bureaucracy will not be surprising. It is all part
of the same dynamic.
11.3.5 The Authoritarian Dynamic
This complexity reducing dynamic has a name: it is called the
Authoritarian Dynamic
(conform the name of Stenner’s 2005 book). In its original form
it was formulated
for the domain of Political Psychology as the correlation:
Intolerance = Authoritarianism × Threat
In this “formula” “Intolerance” refers to intolerance to diversity
and in particular
intolerance to (perceived) violations of norms or the normative
order. “Authoritarian-
ism” initially (Stenner 2005) referred to how often one chooses
the left-side answers
of Table 11.4, which in turn is a (crude) measure of the
shallowness of understand-
ing of the world and the need for external (central or group)
authority to create or
maintain a world in which one feels adequate. “Threat” refers to
the perceived threat
and/or abundance of indicators of moral decline. The
multiplication symbol “ × ”
refers to the “and”-condition entailing that for “intolerance to
diversity” to become
prominent both authoritarian disposition and perceived threat
are required to build
up the motivation to restore order through intolerance (or
coercive formalization).

Note that this combination of (1) a low level of understanding
of the world—
ignorance— and (2) the threat-induced significance of acting
appropriately leads to
deep feelings of personal inadequacy. This entails that the
fundamental driver of the
authoritarian dynamic can be reformulated as the “Ignorance
Dynamic.”
Motivation to restore personal adequacy = Ignorance × Cost of
failure to act appropriately
The deep feelings of personal inadequacy can—from the
perspective of the
Authoritarian—only be improved through the realization of a
more tightly controlled
and less diverse world. Interestingly, violence researcher
Gilligan (1997) argues that
shame, due to the public display of personal failure to act
appropriately, is the
root cause of all violence. This is yet another perspective on the
coercive nature of
intolerance.
There is a perfectly viable alternative approach to improve
one’s deep feelings
of personal inadequacy, but, unfortunately, authoritarians
generally do not come up
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with this among themselves. This alternative is to educate

oneself out of feelings of
personal inadequacy through acquiring a deeper and more
pervasive understanding
as a basis for more advanced strategies. Shallow understanding
in combination with
normal or good intelligence prevents this. The strength of the
coping mode’s “in-
telligent” ways of treating problems as self-contained (such as
the problems in an
IQ-test) leads authoritarians to redefine or ignore reality until it
fits with their current
solution repertoire.
This is another way to understand authoritarian intolerance. It is
intolerance
against anything opposing successful coping with an existing
solution repertoire.
It is therefore also intolerance against advanced strategies—
based on a deeper and
more pervasive understanding—that are not (yet) fully
understood. Only when the
threat level and the “cost of failure to act appropriately”
diminish, these coping
strategies can be replaced by pervasive optimization strategies.
This entails that
whoever controls the threat-level, controls the level of
intolerance to diversity and
growth, the moment intolerance becomes dominant, and the
number of people in an
authoritarian mode.
The Authoritarian Dynamic can be defined on the level of the
individual as well
as on a group or even societal level. A single authoritarian in an
organization will
defer its own authority to the more skilled and knowledgeable

around. But the same
authoritarian in a context with more authoritarians will be
highly motivated to collec-
tively adopt and enforce measures, i.e., introduce coercive
formalization, expected to
reduce situational complexity and personal inadequacy.
Actually a small, but highly
motivated, fraction of a society might start a revolution to
(re)turn to a simpler, more
controlled, and better understood world according to
Billington’s (1980) conclusions.
For example one of the slogans of the French revolution
Liberté, égalité, fraternité
(freedom, equality, brotherhood), which became the French
national motto a century
later, is appealing to the libertarian values of diversity and
individual authority. Yet
it is also consistent with an urge to a simpler and better
understood state of affairs,
where people are more equal (similar), more brotherly
responsible for each other
(more able to keep each other to the norm), and free to define
new (narrower) social
norms. In this light it is not at all surprising that the French
Revolution included a
period called “the Reign of Terror” in which all perceived
opposition to the revolution
was punished at the guillotine. It was the period of about a year
in a highly chaotic
revolutionary decade in which intolerance peaked.
Yet the intolerance to diversity of anxious authoritarians is a
normal coping re-
sponse to a situation of which the complexity has developed out
of coping capacity

of some fraction (per definition the authoritarian fraction) of
the population. It is
their good and democratic right to do something about a
situation that they perceive
as highly troublesome. The problem is that their understanding
of society, compared
to the libertarian fraction, is lower and this may easily lead to
the adoption of sub-
optimal or counterproductive strategies. Yet, the feelings of
inadequacy and anxiety
that authoritarians share and that unite them are genuine and
these deserve to be
taken very seriously. Ideally they should not be ignored or
derided by libertarians,
although they neither share nor understand their outlook on
reality.
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242 T. C. Andringa
The more pervasive and deeper understanding of libertarians
should allow them to
understand authoritarians much better than vice versa. This
entails that the libertarian
fraction of society, at least in principle, holds the keys to the
way the authoritarian
dynamic will play out. Libertarians can influence the perceived
complexity of so-
ciety through coaching, education, and media and they can in
some cases respond
adequately to the threats perceived and moral decline
experienced by authoritarians.
Simply taking these seriously and addressing the root causes

may result in a society
in which considerably less people are in the authoritarian mode.
In such a society
many more people feel adequate because they are adequate
social actors. The ensu-
ing equality in personal adequacy ensures that most are in the
mode. This equality enhances overall wellbeing (Wilkinson
2006; Wilkinson and
Pickett 2009) and it minimizes the probability of a concerted
action by authoritar-
ians to overthrow the (morally depraved) status quo in favor of
a simpler, but also
more regimented and less free society.
11.3.6 The Bureaucratic Dynamic
I will now come to the core and title of this chapter. How to
formulate the psycho-
logical enablers of bureaucracy most succinctly? If bureaucracy
is a manifestation
of authoritarianism, i.e., the prevalence of the coping mode of
thought, within
professional organizations, something similar to the
Authoritarian Dynamic or the
“Ignorance Dynamic” should hold. Of course it must be adapted
to the particular
context of professional organizations.
My proposal, as variant of the Ignorance and Authoritarian
Dynamic, for a
“Bureaucratic Dynamic” is as follows:
Incentive for coercive formalization
= Bureaucracy incentive = Institutional ignorance × Worker cost
of failure

In this “formula” the role of “intolerance” and “motivation to
restore personal
adequacy” is played by either the “Incentive for coercive
formalization” or the “Bu-
reaucracy incentive” as described by Adler and Borys (1996)
and summarized in the
left column of Table 11.2. The role of “Authoritarianism” and
“Ignorance” is played
by “Institutional ignorance.” This is a measure of how well
workers understand the
consequences of their own actions, both within the organization
and on the wider
society. Directly associated is their need (often a demand) for
guidance in every non-
standard activity. The role of “Threat” and “Cost of failure to
act appropriately” is
played by “Worker cost of failure.” In the case of bureaucracy,
the threat is not moral
decline, but failing at the job and publicly being revealed as
professionally inade-
quate. This threat pertains as much to the worker making the
mistake, as it does to the
superior who will be shamed because (s)he did not have the
department under control.
Here, again, we have a combination of two factors: (1)
institutional ignorance
leads to an abundance of opportunities to fail and (2) (high)
cost of failure. The
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prevalence and seriousness of failure now becomes the measure
of personal inade-
quacy. As in the “Ignorance Dynamic” the right side of the
Bureaucratic Dynamic
corresponds to deep feelings of personal inadequacy that “can
only be improved
through the realization of a more manageable world,” which,
according to the logic
of the coping mode, is through coercive formalization.
The Bureaucratic Dynamic, formulated like this, explains both
the basic attitude
and all the key properties of coercive formalization so
characteristic of bureaucracy
(see Table 11.2). Workers are seen as sources of problems to be
eliminated, and
opportunism of (“fools” as) workers is to be feared. A formal
system of complex
procedure and guidelines—all strengths of the coping mode of
cognition—replaces
worker’s intelligence, skills, and improvisation ability.
Deviations from protocol
become suspect. To prevent the natural tendency of workers to
use their “good sense
and sound judgment” the whole organization is made
nontransparent and whatever
global transparency exists is made highly asymmetrical so that
superiors at any
moment can, but not necessarily do, monitor workers so that
workers self-impose
limits on their behavior.
In this process capable workers loose their intrinsic motivation
(“The job is no
longer fulfilling and it impedes my personal development”) and

identified motivation
(“The job is no longer important and its results not satisfying”).
These motivations
are replaced by introjected motivation (“I’d better do it
otherwise I’ll face unpleasant
consequences”) and external motivation (“I have no choice,”
“The protocol says so,”
“The computer says so,” Befehl ist Befehl). Quickly enough this
state of being be-
comes habitual. The result is an individual that while at work,
has shut down half of its
intellectual potential, is stuck in a situation with minimal
personal growth potential,
and is reduced to an automaton-like shadow of a fully
functioning human being.
11.3.7 The Psychological Effects on the Bureaucrat
While bureaucracy is annoying and frustrating for the client and
costly for society, its
effects might be worse for the bureaucrat. Compared to a
worker in a non-bureaucratic
organization, the bureaucrat misses many opportunities to
engage in inherently fulfill-
ing activities, to enjoy meaningful activities, to help others, to
contribute undeniably
to a better society, and in general to give meaning and
significance to life.
What happens to the bureaucrat if these high-level human needs
cannot be compen-
sated in the rest of life? What level of life quality will result?
Somewhat alarmingly,
the pattern of these effects resembles those of torture. For
example, torture victim
therapist Leanh Nguyen (2007) concludes the following:

The most terrible, and intractable, legacy of torture is the
killing of desire—that is, of
curiosity, of the impulse for connection and meaning making, of
the capacity for mutuality,
of the tolerance for ambiguity and ambivalence.
This description sounds eerily similar to the description of
someone indefinitely
locked in the coping mode of cognition. This quote describes a
complete inability
to experience curiosity, joy, play, and interpersonal contact. An
inability for playful
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244 T. C. Andringa
sensemaking associated with intrinsic motivation. This is
replaced by a constant need
for the certainty and formal clarity. It is as if the pervasive
optimization mode has
become inaccessible. Does not this resemble the automaton like
bureaucrat in the
introductory example?
This comparison between bureaucracy and torture might, at first
glance seem a
bit over the top, but remember that both are about subduing the
individual to external
authority (just as slavery by the way). Individual autonomy may
well be a defining
characteristic for health (see Andringa and Lanser (2013) for
the role of freedom over

mind-states in sound annoyance). From that perspective it
makes sense to consider
bureaucracy as a low, but prolonged, level of psychological
torture that like full-
blown torture, may have a profound and long lasting influence
on bureaucrats and
by extension on society. As far as I know, this topic has not
deserved the attention it
should have.
11.3.8 Summary of the Psychological Roots of Bureaucracy
In Sect. 2, the psychological enablers of bureaucracy, I have
progressively developed
the psychological foundations of bureaucracy by addressing a
number of comple-
mentary perspectives from different psychological specialisms.
I will summarize its
main results here.
Step one involved the notion of habits. During habitual behavior
it is the envi-
ronment that drives behavior. Habits free the higher faculties of
mind during routine
tasks and have as such great benefits. If, however, the use of the
higher faculties
of mind is discouraged at work, the result is something of an
automaton: a half-
empty human shell performing routine tasks, but devoid of
compassion, empathy,
and understanding.
In the section called “Two modes of thought” I showed that
(proto)typical bu-
reaucratic behaviors fit perfectly with the coping mode of
cognition (Table 11.3).

The coping mode is characterized by intelligently solving self-
contained problems,
while the pervasive optimization mode is characterized by ever-
improving one’s un-
derstanding of the diversity of the world. This leads to two
different attitudes toward
“authority.” For the coping mode some (typically external)
authority must limit and
constrain the world so that one’s existing solution repertoire
can be applied. In the
pervasive optimization mode the individual internalizes the role
of authority and be-
comes progressively more self-deciding and autonomous as
understanding becomes
more pervasive and deep.
To study the interplay between authority and understanding, I
discussed the
phenomenon of authoritarianism as defined by Stenner (2005).
This led to the identi-
fication of two attitudes toward the world: the libertarian
attitude in which the world
is full of possibilities and an authoritarian attitude in which a
lack of understanding
of the world leads to anxiety and feelings of personal
inadequacy of which liber-
tarians are generally unaware. These feelings unify and
motivate authoritarians to
[email protected]
oppose all sources of complexity, unpredictability, novelty, and

growth that complex-
ify, confuse, and destabilize a predictable state of affairs. This
drives encroaching
bureaucracy.
The emergence of intolerance to (ill-understood) diversity has
been summarized
in the “Authoritarian Dynamic” in which “intolerance” scales
with the level of ig-
norance (authoritarianism) and “threat-level” as a measure of
the significance of not
understanding one’s world. Together these lead to a sense of
personal inadequacy.
The strength of the coping mode’s “intelligent” ways of treating
all problems as
self-contained (such as the problems in an IQ-test) leads
authoritarians to redefine
or ignore reality until it fits with their current solution
repertoire.
The digression into political psychology allowed the
formulation of a “Bureau-
cratic Dynamic.” The role of intolerance to diversity is apparent
as the prominence
of coercive formalization. Conform the Authoritarian Dynamic,
this scales with the
product of “institutional ignorance” and “worker cost of
failure.” Public shaming in
case of failure is a measure of worker’s inadequacy as a
professional and the man-
ager’s inadequacy both as a leader and as a person. This leads,
again according to the
logic of the coping mode, to the worker accepting (or
demanding) and the manager
instilling more coercive formalization.

However, in this process workers lose their intrinsic motivation
and become grad-
ually more extrinsically motivated and the work becomes more
and more habitual.
The workers have shut down half of their intellectual potential
and are stuck in a
situation with minimal personal growth potential.
This then, finally, leads me to question whether the
psychological effects on
bureaucracy on bureaucrats might be an ignored, yet imminently
important, psy-
chological and societal problem. The third and last section of
this chapter will not
focus on this problem, but on how the societal goals of
organizations can be pro-
tected from bureaucracy. Fortunately this may also protect
workers from the (likely)
adverse effects of bureaucracy.
11.4 Protecting the Societal Goals of an Organization
The subtitle of this chapter is “Protecting the societal goals of
an organization.”
This section addresses this topic for nonprofit organizations
because these have a
social mission. In the introductory example the original societal
role of the lost-and-
found department was replaced by a new goal: procedural
correctness, irrespective
of the state of the world and the implications of following the
procedure. As I have
outlined in the previous section this is the result of the coping
mode running amok in
an organization conform the “Bureaucratic Dynamic.” This
entails that this section

will firstly address a number of management paradigms in
relation to their societal
goals and bureaucracy, secondly it describes core features of
non-bureaucratic or
libertarian organizations, and thirdly it formulates safeguards
against encroaching
bureaucracy. This chapter ends with some reflections and
conclusions.
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246 T. C. Andringa
11.4.1 Management Paradigms for Nonprofits
As summarized at the end of the previous section, the
psychology of not understand-
ing one’s world and in particular ignorance about one’s working
environment and
not overseeing the consequences (both adverse and beneficial)
of one’s activities
leads to encroaching bureaucracy through the generation of
more self-centered goals
of complexity reduction that progressively erode the focus on
the original societal
goals of an organization. For nonprofit organizations, of which
the mission aims at
the achievement of social purposes rather than in generating
revenues, this entails
that they gradually delegitimize themselves through making
their own stability and
survival more important than their original social raison d’être
(Moore 2000). Yet
depending on the management paradigm, nonprofits run this risk

to varying degrees.
Stoker (2006) describes and summarizes three management
paradigms that
neatly fit a progression from organizations based on coping
mode rationality to the
rationality of the pervasive optimization mode. I will describe
all three.
11.4.1.1 Traditional Public Management
Traditional public management follows the typical Weberian
early twentieth-century
template (Weber 1978) in which bureaucracy delivers
organizational effectiveness
through four features that Stoker (2006) summarizes as follows:
The first is the placing of officials in a defined hierarchical
division of labor. The central
feature of bureaucracy is the systematic division of labor
whereby complex administrative
problems are broken down into manageable and repetitive tasks,
each the province of a
particular office. A second core feature is that officials are
employed within a full-time career
structure in which continuity and long-term advancement is
emphasized. Third, the work of
bureaucrats is conducted according to prescribed rules without
arbitrariness or favoritism
and preferably with a written record. Finally, officials are
appointed on merit. Indeed they
become experts by training for their function and in turn control
access, information, and
knowledge in their defined area of responsibility.
The italic emphasis has been added to indicate concepts arising

from the logic of the
coping mode.
11.4.1.2 New Public Management
New public management arose as an alternative to the
observation “that public ser-
vice organizations tend to be neither efficient in terms of saving
public money nor
responsive to consumer needs” (Stoker 2006). As a result it did
not arise from posi-
tive motivations, but as a solution to the problems of
bureaucracy. Stoker describes
this as follows.
The solution is to fragment monopolistic public service
structures and develop incentives
and tools to influence the way that they operate. Key reforms
include the introduction of a
purchaser-provider divide within organizations and the
development of performance targets
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and incentives. The aim is to create an organizational home for
the client or consumer
voice within the system to challenge the power of producers.
Consumers or their surrogate
representatives, commissioners, would have the power to
purchase the services they required
and measure performance. The achievement of better
performance would be aided by arms-

length systems of inspection and regulation to check not only
the spending of public money
but also the delivery of public services against demanding
targets.
New public management then seeks to dismantle the
bureaucratic pillar of the Weberian
model of traditional public administration. Out with the large,
multipurpose hierarchical bu-
reaucracies, new public management proclaims, and in with
lean, flat, autonomous organiza-
tions drawn from the public and private sectors and steered by a
tight central leadership corps
So the key improvement compared to the traditional model is
the explicit role and
importance of the societal function of the organization, but in
this case limited to
specific performance targets to be delivered by lean, flat, and
autonomous organiza-
tions of which the performance indicators are still fully under
control of some sort
of central leadership that is supposed to represent public and
private sector interests.
New public management is clearly aware of important
drawbacks of Weberian
bureaucracy, yet it is still guided by the logic of the coping
mode. However, it has
some indicators of the pervasive optimization mode such as
greater worker autonomy
(within the tight constraints of performance indicators) and
some, albeit indirect,
representation of consumers and other beneficiaries of the
delivered services.

11.4.1.3 Public Value Management
Public value management (Moore 2000) is an emerging new
management paradigm
that is not so much a response to an existing paradigm but a
formulation of the role of
nonprofits in modern society (Stoker 2006). Public value
management is succinctly
formulated as a public value scorecard (Moore 2003) in which
an organization should
balance (1) the public value produced by the organization, (2)
the legitimacy and
support enjoyed by the organization, and (3) the operational
capacity to achieve its
results. In the public value scorecard the performance indicators
are translated as
measures of performance. Moore (2003) describes these as
follows.
Some of the measures are those we associate with the public
value produced by the
organization—the extent to which it achieves its mission, the
benefits it delivers to clients,
and the social outcomes it achieves.
Others are associated with the legitimacy and support enjoyed
by the organization—the extent
to which “authorizers” and “contributors” beyond those who
benefit from the organization
remain willing to license and support the enterprise. These
measures can, to some degree,
be viewed as important because they indicate the capacity of the
organization to stay in
operation over time. But these measures can also be viewed to
some degree as measures of
value creation in themselves. This is particularly true if we

recognize that some part of the
value created by nonprofit organizations lies in the
opportunities it affords to public spirited
individuals to contribute to causes they care about, and another
part lies in the capacity of
the nonprofit organization to link contributing individuals to
one another in a common effort
to realized shared social goals.
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248 T. C. Andringa
Still others are associated with the operational capacity the
nonprofit organization is relying
on to achieve its results. This includes not only measures of
organizational output, but also
of organizational efficiency and fiscal integrity. It also includes
measures of staff morale
and capacity, and the quality of the working relationships with
partner organizations. And,
it includes the capacity of the organization to learn and adapt
and innovate over time.
Where the Weberian bureaucracy follows the logic of the coping
mode, these mea-
sures read as the pervasive optimization mode specified to the
context of nonprofit
organizations.
The public value scorecard was a response to an earlier score
card: Kaplan’s
(Kaplan and Norton 1996) Balanced Scorecard, for the new
public management

paradigm through its focus on financial and efficiency
measures. The Public Value
Scorecard differs in a number of central aspects that are
characteristic of the pervasive
optimization mode. Moore (2000) formulates these differences
as follows.
First, in the public value scorecard, the ultimate value to be
produced by the organization
is measured in non-financial terms. Financial performance is
understood as the means to
an end rather than an end itself. The end in itself is denominated
in non-financial social
terms. It also notes that the value produced by the organization
may not lie simply in the
satisfaction of individual clients. It can lie, instead, in the
achievement of desired aggregate
social outcomes of one kind or another.
Second, the public value scorecard focuses attention not just on
those customers who pay for
the service, or the clients who benefit from the organization’s
operations; it focuses as well
on the third party payers and other authorizers and legitimators
of the nonprofit enterprise.
These people are important because it is they who provide some
of the wherewithal that the
organization needs to achieve its results, and whose satisfaction
lies in the achievement of
aggregate social states as well as in the benefits delivered to
individual clients.
Third, the public value scorecard focuses attention on
productive capabilities for achieving
large social results outside the boundary of the organization
itself. Other organizations ex-

isting in a particular industry are viewed not as competitors for
market share, but instead as
partners and co-producers whose efforts should be combined
with the effort of the nonprofit
enterprise to produce the largest combined effect on the
problem that they are jointly trying
to solve. In short, a nonprofit organization should measure its
performance not only by its
ability to increase its market share, but also by its ability to
strengthen the industry as a whole.
Again I have added emphasis in italic to stress some the core
concepts of this ap-
proach. The reader can combine these with the italic remarks in
the right column
of Table 11.3 that interprets the strong points of the pervasive
optimization mode in
organizational terms. It will be clear that this description
matches the properties of
the pervasive optimization mode.
Stoker (2006) concludes that public value management rests “on
a fuller and
rounder vision of humanity than does either traditional public
administration or new
public management.” He identifies a key difference, namely the
role of motivation,
when he concludes:
Ultimately, the strength of public value management is seen to
rest on its ability to point to
a motivational force that does not solely rely on rules or
incentives to drive public service
practice and reform. People are, it suggests, motivated by their
involvement in networks and
partnerships, that is, their relationships with others formed in

the context of mutual respect
and shared learning. Building successful relationships is the key
to networked governance
and the core objective of the management needed to support it.
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In terms of motivation, the public value management relies on
the power of identified
(“I find it important”) and intrinsic (“I enjoy doing it”)
motivation. And this can be
contrasted to the bureaucratic extreme in which the motivations
are mainly extrin-
sic (“I have no choice”) or introjected (“I’d better do it
otherwise I face negative
consequences”). The positive motivations are associated with
(not only) experiential
learning (Andringa et al. 2013; Vygotskiı̆ 1978) and the growth
of organization un-
derstanding, which is, conform the Bureaucratic Dynamic, the
key protector against
institutional ignorance.
11.4.1.4 Summarizing Key Properties of the Three Management
Paradigms
Table 11.5 provides a summary, adapted from Stoker (2006), to
which I have added
six rows describing properties in terms of the strengths of the
coping and the pervasive
optimization mode.

11.4.2 Libertarian Organizations
Until now I have focused mostly on bureaucracy and the
personal, organizational, and
societal manifestations of the coping mode. But how does the
mode manifest itself in the context of organizations? Stoker
(2006) notes that for
public value management to work the motivation of workers
needs to be “intrinsic”
or “identified,” which complies with the organic organization
type identified by
Adler and Borys (1996). Alternatively one might call
organizations that realize this
“Libertarian organizations” because the members are dominated
by intrinsic and
identified motivation, understand what they are doing, are
autonomous self-deciders,
and, in summary, rely mostly on the pervasive optimization
mode of cognition.
Organizational structures that effectively contribute to an ever-
changing real world
of dangers and opportunities need flexible access to the
available competence and en-
thusiasm. Libertarian organizations must therefore match the
available competences
and institutional understanding to whatever the world demands
of the organization.
Where authoritarian organizations realize (at best) proscribed
results and predictable
mediocrity, libertarian organizations can realize personal
growth, institutional ex-
cellence, and with that effective contributions to the wider
society. They are truly
optimizing pervasively.

In libertarian organizations the formal hierarchy is as important
as in a bureau-
cracy, but its role is quite different: it has to manage autonomy
instead of enforcing
compliance. For superiors who know how to manage
motivations and how to con-
vey the role of the organization in society, this is not at all
demanding because the
very autonomy and commitment of a healthy libertarian
organization ensures that it
can deal with stability (where efficiency and organizational
optimization are priori-
ties) and change (where protection of quality and the realization
of opportunities are
prominent).
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250 T. C. Andringa
Table 11.5 Management paradigms. (Adapted from Stoker
(2006), which is based on Kelly et al.
(2002). The lowest 6 rows have been added as interpretations of
the original table in terms of the
discourse of this chapter.)
Key objectives Traditional public ad-
ministration
New public manage-
ment
Public value manage-

ment
Role of managers Politically provided in-
puts; services moni-
tored through bureau-
cratic oversight
Managing inputs and
outputs in a way that
ensures economy and
responsiveness to con-
sumers
The overarching goal is
achieving public value
that in turn involves
greater effectiveness in
tackling the problems
that the public most
cares about; stretches
from service delivery to
system maintenance
Definition of public
interest
To ensure that rules and
appropriate procedures
are followed
To help define and meet
agreed performance tar-
gets
To play an active role in
steering networks of de-

liberation and delivery
and maintain the overall
capacity of the system
Approach to public
service ethos
By politicians or ex-
perts; little in the way
of public input
Aggregation of individ-
ual preferences, in prac-
tice captured by senior
politicians or managers
supported by evidence
about customer choice
Individual and public
preferences produced
through a complex pro-
cess of interaction that
involves deliberative
reflection over inputs
and opportunity costs
Preferred system for
service delivery
Public sector has
monopoly on service
ethos, and all public
bodies have it
Skeptical of public sec-
tor ethos (leads to in-

efficiency and empire
building); favors cus-
tomer service
No one sector has a
monopoly on public
service ethos; main-
taining relationships
through shared values
is seen as essential
Contribution of the
democratic process
Hierarchical depart-
ment or self-regulating
profession
Private sector or tightly
defined arms-length
public agency
Menu of alternatives
selected pragmatically
and a reflexive ap-
proach to intervention
mechanisms to achieve
outputs
Interpretation in
terms of cognitive
modes
Typical of the coping
mode

Aware of limitations of
the coping mode
Transition to the perva-
sive optimization mode
Role of worker Skilled obedience Responsible for as-
signed tasks and
maintaining skills.
Customer oriented
Co-responsible for
societal role execution
and the adaptation
of the organization’s
changing societal
demands
[email protected]
Key objectives Traditional public ad-
ministration
New public manage-
ment
Public value manage-
ment
Skills of worker Precision in role exe-

cution (aimed at error
prevention)
Deep understanding of
tasks and role skills
Deep understanding of
role skills and broad un-
derstanding of impact
of own activities on
public value
Motivations Extrinsic and
introjected
Extrinsic, introjected,
identified, and intrinsic.
Role of motivation not
central
Identified and intrinsic.
Essential role of moti-
vation
Attitude to work Obedient and
unengaged
Professional
development
Personal development
In healthy libertarian organizations everyone develops in terms
of (institutional)
understanding. This entails that eventually everyone can “play”
a diversity of formal

and functional roles. Basically the only real requirements for a
healthy libertarian
organization is that everyone in the organization has roles that
are often intrinsically
motivating, are generally satisfying, and that do not exceed
understanding capacity.
An organization that satisfies these conditions will remain in a
mode, even in the face of great organizational or societal
challenges.
Table 11.6 provides a selection of properties of libertarian
organizations formu-
lated to promote the pervasive optimization mode in
organizations.
11.4.3 The Dynamics of Encroaching Bureaucracy
We have probably all been members of a team that functioned
amazingly well for
a time, but then started to dysfunction and eventually
disintegrated. This is because
excellence is fragile: it not only delivers pervasive
optimization, but also depends on
it. In his analysis of how twentieth-century (American)
bureaucrats took over educa-
tion from teachers, Labaree (2011) describes how the
“pedagogically progressive”
vision of education—child-centered, inquiry based, and
personally engaging—is a
fragile hot-house flower because it depends on broadly realized
favorable conditions
(i.e., successful pervasive optimization). In contrast, the
“administrative progres-
sive” vision of education is a weed because it grows under
difficult conditions such

as erratic funding, poorly prepared teachers, high turnover,
dated textbooks, etc. It
is robust “because its primary goal is to be useful in the
narrowest sense of the term:
It aims for survival rather than beauty.”
Labaree accounts a “battle” between the philosopher John
Dewey and educational
reformer David Snedden. As proponent of the pedagogically
progressive vision, John
Dewey formulated a complex and nuanced narrative of
education as a means to make
“workers the masters of their own industrial fate.” In contrast,
David Snedden as the
champion of the administrative progressive approach, saw
education as vocational
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252 T. C. Andringa
Table 11.6 Properties of libertarian organizations. Intended to
stimulate the pervasive optimization
mode of cognition
Topic Property
Vision A “lived” vision of the goals and roles of the
organization is widely shared.
It allows everyone in the organization to contribute to its
realization via
well-formulated procedures and competent improvisation alike
Approach the organization holistically: optimize everything in

context of the
whole; prevent at all cost strict compartmentalization of
responsibilities and
information, because specialism and other forms of close-
mindedness are
seeds of stagnation and corruption
Motivation Promote and ensure a predominance of intrinsic and
identified motivations
Allow people to be happy or enthusiastic about what they have
done well
and allow them to repair and learn from mistakes
Competences Focus on pervasive competence development
Promote a deep insight in the societal effects of individual work
and the
organization as a whole
Stimulate overlapping competences to ensure organizational
redundancy,
optimization opportunities, more timely services, and enhanced
work
satisfaction
Distribute responsibilities according to available competences,
interests,
ambitions, and enthusiasm. Ignore hierarchical considerations
Be alert of indications of low competence, stagnated
development, insensi-
tivity to adverse consequences of (in)action, low inherent
motivation, low
commitment to the organization and the services it should
provide (e.g.,

9-to-5 mentality), and indicators of lack of enthusiasm
Autonomy The task of management is to manage worker
autonomy
Competent autonomy of workers is success indicator
Put real responsibility in every job description and allow a
diversification or
responsibilities as competence grows
Stimulate expertise, but prevent specialization
Information Develop an open information infrastructure
Allow for ample opportunities for unstructured information
sharing
The Scottish proverb “When the heart is full the tongue will
speak” will
ensure that really important information will be shared
training in preparation for a life of servitude. As a narrow-
minded authoritarian, he
understood the world in dualisms and countered nuanced
arguments by ignoring
them and by repeating reasonable sounding dogma. Labaree
(2011) concludes:
Therefore, the administrative progressive movement was able to
become firmly established
and positioned for growth because of Snedden’s flame throwing.
Put another way, a useful
idiot, who says things that resonate with the emerging ideas of
his era and helps clear the
ideological way for the rhetorical reframing of a major

institution, can have vastly more
influence than a great thinker, who makes a nuanced and
prescient argument that is out of
tune with his times and too complex to fit on a battle standard.
[email protected]
This is how authoritarians gain control. Not by the quality of
argument, but by fo-
cusing the discussion, by subtly reinterpreting the goals of an
organization in a less
rich manner, by ignoring nuances or replacing them by similar
sounding opposi-
tions, and by gradually marginalizing and deriding opposition.
When authoritarians
have gained control they start simplifying, harmonizing,
focusing, and reorganizing
the organization according to Billington’s (1980) observations
on revolutions. The
rhetoric is a convenient tool. But the real objective, albeit rarely
acknowledged, is
a simpler, more controlled, and better understood world.
Authoritarians bring the
complexity of the world, or in this case national education,
down to their level of
understanding of it.
This process matches the Bureaucratic Dynamic that we have
formulated.
Incentive for coercive formalization
= Bureaucracy incentive = Institutional ignorance × Worker cost

of failure
The true drivers of the bureaucratization process are feelings of
personal inadequacy
among workers. In the case of educators like Snedden, these
feelings arose from being
lost in the complex world of education in which responsibilities
are unclear and the
means to realize them even more. The resulting personal anxiety
motivates workers
to reestablish their sense of adequacy whenever possible: at
work they are now in an
authoritarian mode. Their colleagues who do understand their
responsibilities and
know how to realize them feel no anxiety. They are and remain
in a libertarian mode
and are generally unaware of the severity of the anxiety in their
(now) authoritarian
colleagues.
The authoritarians gravitate toward each other and start to
formulate and promote
a simplified understanding of the roles and aims of the
organization. The libertar-
ian opposition against this simplified understanding is of course
based on a fuller
understanding of the roles and aims of the organization. But
these arguments have
no impact on the authoritarians because, in their eyes, the
arguments are addressing
irrelevancies with no relation of their new, simplified, and more
tangible understand-
ing of the organization’s scope and aims. While the libertarians
waste their time
and energy with progressively more nuanced arguments, the
authoritarians find each

other and may at some point take control over the organization.
When they do, they make their level of “institutional ignorance”
the norm. And
because they are in the coping mode they will realize this norm
according to the logic
of the coping mode (Table 11.3, left side). This will, according
to the Bureaucratic
Dynamic, lead to the introduction of more “coercive
formalization” and a shift from
being as professional as possible to producing tangible
measureable outcomes and
preventing errors in realizing these. Preventing worker failure
and publicly displayed
inadequacy becomes more important than professional success.
At the same time the libertarians in the organization discover
that many of the
things they used to do—and which still make sense given the
logic of the pervasive
optimization mode—are no longer officially endorsed because
they are incompatible
with the new simplified norm. In fact the old way of working
has become a liability
if it hinders the realization of the new, more tangible,
performance measures. What
used to be the highest indicators of professionalism, are now
costly ways to fail
as a worker. The new professionalism is rule compliance and
not organizational
excellence.
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254 T. C. Andringa
Much of what the libertarian worker motivated, is no longer
officially or practically
part of the organization’s core business. The moment the new
management initiates a
reorganization, of course according to the logic of the coping
mode, the libertarians
are faced with a dilemma: leave with professional dignity or
succumb to the new
normal and deskill and comply. Whatever the libertarian
chooses, the result is the
same: increased institutional ignorance.
11.4.4 Preventing Bureaucracy
I had the doubtful honor to witness such a process in my
university. Only two individ-
uals at key positions in the hierarchy drove the process.
Fortunately, it was followed
by repair measures when the whole process overshot and the
organizational costs
became too high. This happened after some highly skilled and
motivated colleagues
had left and others were on sick-leave. At that point workers
simply refused to take
further responsibility and the department almost stopped
functioning. This paper is
informed by witnessing this process. Without understanding
bureaucracy as well as
I do now, the unfolding process was very difficult to counter.
Yet it is possible to
devise effective protective measures. In Table 11.7 I have
formulated a number of
“Red Flags” as indicators of encroaching bureaucracy that may
be helpful to stop a

bureaucratization process before it becomes self-reinforcing.
According to the Bureaucratic Dynamic, the best protection
against bureaucratiza-
tion is preventing worker (including management) ignorance
and promoting worker
professionalism instead of preventing worker error. A truly
healthy and resilient
organization maintains a sufficient level of institutional
understanding and worker
autonomy so that no one feels inadequate and every one
contributes to the realization
of the organizations full societal goals and not only to a single
or a few “key perfor-
mance objectives.” Yet as the analysis of the three management
paradigms shows,
institutional understanding improves over time. For example,
the new public value
management paradigm starts from the logic of the pervasive
optimization mode in-
stead of the logic of the coping mode as would have been the
natural Weberian option
a century ago.
A future informed public might not accept the products of a
bureaucratic organiza-
tion because it demonstrates, for all to witness, that its
management and workers do
not quite understand what they are doing. In addition, if my
expectation is substanti-
ated that bureaucracy leads to high personal and societal costs
for bureaucrats, future
societies might simply not accept bureaucracy because it
signifies a pathological state
of affairs of which the immediate costs are apparent as reduced
quality and efficiency,

while the full personal and societal costs are deferred to future
generations. In fact
sustainability arguments might drive this.
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Table 11.7 Red flags. Early indicators disrupting the pervasive
optimization mode
Red Flags
Mission The absence of a shared, living vision about the
organization’s goals in a larger
societal context
The advance of a simplified and more focused interpretation of
the organizations
mission, typically as a limited number of “key performance
objectives”
Leaders Leaders insensitive to reasoned and nuanced arguments
by competent individuals
at any position in the organization
Leaders only sensitive to arguments related to goal achievement
or procedure.
Realizable goals are preferred over desirable goals
Leaders preferring obedience over autonomy and who curtail
work-floor auton-
omy

Bureaucrats promoted to key positions
Competences Neglect of work-floor competences
Demotivation of highly autonomous, competent and committed
co-workers
Gradual deterioration of quality of the working environment and
worker
motivation
The most competent and committed coworkers leave
Standardization at the cost of curtailing of essential/useful
diversity
Uniformization Strong focus on formalities while neglecting (or
indefinitely) postponing content
Compartmentalization of information and plans
Mediocracy facilitated
11.4.5 Conclusion and Reflection
In some sense this chapter is about the difference between
intelligence and under-
standing as manifestations of, respectively, the coping and the
mode of cognition. Understanding proofs itself as the ability to
set up, in a statistical
sense, the conditions for an unproblematic future and an
interesting and fulfilling
life. Failure to do so leads to problematic situations to be solved
intelligently. While
understanding shines in an open world, intelligence assumes a

closed world of self-
contained problems to be addressed with an existing solution
repertoire. Anything
in the way of the solution will be ignored or coercively made
irrelevant. While un-
derstanding manifests itself through fostering empathic
relations, intelligence, as a
last line of defense, is self-protective, impersonal, and ruthless.
Without understanding the consequences of one’s activities,
work is bureaucratic.
Since no one is bureaucratic while not at work and especially
not among friends, it
is the work environment that activates bureaucratic behavior. In
this chapter, I have
shown that bureaucracy in all it facets can be understood from
basic psychology.
Bureaucratic behavior is habitual or intelligent rule following.
The bureaucrat obe-
diently performs activities that it understands superficially and
values marginally,
but that it does not endorse or feels responsible for. As such the
bureaucrat appears
and acts as a dehumanized automaton. It is a pitiful state of
being.
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256 T. C. Andringa
The psychological enabler of bureaucracy is a sense of personal
inadequacy among
workers resulting from not understanding their work and its
consequences. This ac-

tivates the coping mode of cognition and with that an urge to
bring the complexity
of the working environment down to more manageable levels
through promoting
coercive formalization. This process can be summarized as the
Bureaucratic Dy-
namic, which states that the prevalence of coercive
formalization depends on the
combination of “Institutional Ignorance” and “Worker cost of
failure.”
Fortunately, in the last century society became gradually more
aware of the effects
and dangers of bureaucracy. New public management arose as a
response to curtail
the adverse effects of Weberian bureaucracy as defining aspect
of traditional public
management. Because it is still based on the coping mode of
cognition it will not
become a bureaucracy-free alternative. New value management
however arises from
the logic of the pervasive optimization mode and it has the
potential to achieve
organizational excellence without bureaucracy.
Anti-bureaucratic measures should not only focus on the
reduction of the number
of rules and regulations because this still follows the logic of
the coping mode. It
should instead focus on motivating workers to understand their
professional roles
and to learn to oversee the impact of their activities; not only on
the organization, but
also on the wider society. This understanding will lead to a
reevaluation of the role of
formalization and will erode the need for coercive

formalization. The organization
will no longer focus on preventing errors, but on optimizing the
multifaceted societal
roles of the organization in ways that are experienced as
important, worthwhile, and
intrinsically motivating for its workers. Yet organizations that
function like this are
somewhat fragile and may be eroded from the inside by a
fraction of workers that
still have an impoverished understanding of the organization
and its societal roles. It
will be important to develop safeguards to prevent this.
Current anti-bureaucratic awareness stems from the observation
that bureaucratic
organizations are neither efficient in terms of saving public
money nor responsive
to consumer needs. Future research may however proof
important adverse effects of
bureaucracy on bureaucrats and on society as a whole. This may
expose bureaucracy
for what I think it is: a pathological state of human
organization, with equally serious
adverse consequences for the bureaucrat and society as a whole.
In the course of writing this chapter I was struck by the
consistency and comple-
mentarity of disparate scientific results. Science produces
wonderful observations
and generates deep insights, but it has difficulty in combining
these if they originate
from different domains. The transdisciplinary framework
presented in this chap-
ter allowed far reaching conclusions through the combination of
a number of these
observations and insights.

For example the work of Adler and Borys (1996) and especially
their conceptual-
ization of bureaucracy, in terms of the degree and type of
formalization (enabling or
coercive), gained theoretical support. The stability of
bureaucracies can be explained
through the link between bureaucracy and habitual behavior,
since bureaucrats feel
a self-imposed responsibility to maintain the condition in which
their habitual func-
tioning is guaranteed. Furthermore, McGilchrist’s (2010)
description of the way
the two brain hemispheres understand the world and our
conceptualization of the
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pervasive optimization mode and the coping mode (Andringa et
al. 2013), seems to
predict how non-bureaucratic and bureaucratic organizations
micromanage. This was
a serendipitous finding that I consider highly relevant. In
addition Stenner’s (2005)
conceptualization of authoritarianism—as having a problem
with a complex world
(and not with complex thinking)—helped to understand the
psychological motiva-
tors of bureaucracy in terms of feelings of personal inadequacy.
Finally, Billington’s
(1980) observations about revolutions always aiming for
simplicity, helped to under-

stand why well-functioning non-bureaucratic organizations
might be eroded from
the inside and turn into a bureaucracy.
All in all, it seems to me that bureaucracy is not just a
phenomenon that occurs
in professional organizations. Instead it is just one of many
manifestations of the
interplay between understanding and intelligence that are
important for every aspect
of live.
Appendix
Some core properties of the bureaucratic syndrome
(authoritarian dominated) and
the non-bureaucratic syndrome (libertarian dominated
organizations).
Topic Bureaucratic syndrome Non-bureaucratic syndrome
Key properties
Organizational goals Societal goals of the orga-
nization are only adhered in
name, but neither understood
nor clearly implemented
Development of a broadly shared
vision about the societal reason
d’être of the organization and the
way to realize it
Overall strategy Stimulating sameness and one-
ness through standardization
and obedience

Continual skilled improvisation
on the basis of a shared vision and
well-chosen procedures
Competence Ignoring, discouraging, and de-
moralizing competent “subordi-
nates.” Deskilling
Relying on and fostering all
proven and budding competencies
in the organization
Autonomy Subordinate autonomy is not an
option. Obedience is more im-
portant than competence
Autonomy and competence devel-
opment of subordinates expected
Content Complete disregard of content
while favoring form
Content is leading, form a means
Organizational
development
Structures and procedures adapt
to the lowest competence level
Everyone is expected to learn and
grow towards autonomous roles in
organization
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258 T. C. Andringa
Main conflicts
Stability versus develop-
ment
Stability and other forms of high
predictability leading. This de-
fines the organization
The workers in the organization
are constantly developing their
skills in order to improve all as-
pects of the societal role of the
organization (i.e., quality and ef-
ficiency)
Form versus optimization Obsessed with form and for-
malisms. Centralized optimiza-
tion of standardized and nar-
rowly defined responsibilities
Actively eliciting creative and de-
centralized optimization of or-
ganizational goals. Disregard of
form when counter-productive
Standardization versus di-
versity

Obsession with standardization
and curtailing diversity, at the
cost of quality if quality entails
diversity
Concerned with the overall opti-
mization of all work processes in
context, of which both standard-
ization and increasing diversity
are options
Error versus learning Obsessed with preventing errors
and mistakes. The organization
redefines itself to produce what
it can, not what it should; “race
to the bottom”
Error and correction after error
part of continual creative opti-
mization of work processes
Short versus long term Exclusively short-term (form)
oriented, neither care for nor un-
derstanding of mid of long term
goals. However, what is short-
or mid-terms depends on the role
in the organization
Optimization, by all workers. on
all time-scales and all dimensions
of success
Structural properties
Role of hierarchy Hierarchy formalized and in-
flexible, based on assumed (but

never fully checked) compe-
tence of superiors
Hierarchy task dependent,
and therefore flexible and
competence-based
Perception of authorities Authorities never fundamentally
questioned
Incompetent authorities not ac-
cepted, but coached or dismissed
Locus of control Formation of stable authoritar-
ian cliques, who take control
over the institutional change
processes to prevent further
complexity
Loosely and varyingly linked lib-
ertarians at control positions.
Measures of success Performance measures rede-
fined to what is delivered
Performance measure based on
what should be delivered (given
reason d’être)
Accountability Suppression of all forms of ac-
countability at the higher levels
and prevention of errors and ret-
ribution in case of error at the
lower levels
Accountability part of normal in-

stitutional learning and compe-
tence building
[email protected]
Emotions
Overall role Rationality and “objectivity”
leading. Emotions treated as ir-
relevant source of variation, to
be suppressed
Central role of positive emotions
(compassion, enthusiasm, inter-
est) as key motivators; prominent
negative emotions indicative of
organizational failure
Emotion of workers Motivating emotion negative:
activities guided by the fear of
losing control or being shamed
publically
Motivating emotion positive: ac-
tivities aimed at realizing shared
benefits including personal devel-
opment
Emotions of co-workers Utter disregard of the feel-
ings and emotional wellbeing of

coworkers
Strong focus on the creation of op-
timal working condition in which
coworkers feel optimally moti-
vated to give their best
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[email protected]
Chapter 12
Active and Passive Crowdsourcing in
Government
Euripidis Loukis and Yannis Charalabidis
Abstract Crowdsourcing ideas have been developed and initially
applied in the
private sector, first in the creative and design industries, and
subsequently in many
other industries, aiming to exploit the ‘collective wisdom’ in
order to perform difficult
problem solving and design activities. It was much later that

government agencies
started experimenting with crowdsourcing, aiming to collect
from citizens infor-
mation, knowledge, opinions and ideas concerning difficult
social problems, and
important public policies they were designing for addressing
them. Therefore, it is
necessary to develop approaches, and knowledge in general
concerning the efficient
and effective application of crowdsourcing ideas in government,
taking into account
its special needs and specificities. This chapter contributes to
filling this research gap,
by presenting two novel approaches in this direction, which
have been developed
through extensive previous relevant research of the authors: a
first one for ‘active
crowdsourcing’, and a second one for ‘passive crowdsourcing’
by government agen-
cies. Both of them are based on innovative ways of using the
recently emerged and
highly popular Web 2.0 social media in a highly automated
manner through their
application programming interfaces (API). For each of these
approaches, the basic
idea is initially described, followed by the architecture of the
required information
and communications technology (ICT) infrastructure, and
finally a process model
for its practical application.
12.1 Introduction
The capability of a large network of people, termed as ‘crowd’,
networked through
web technologies, to perform difficult problem solving and

design activities, which
were previously performed exclusively by professionals, has
been initially recog-
nized by private sector management researchers and
practitioners, leading to the
development of crowdsourcing (Brabham 2008; Howe 2008).
Crowdsourcing ideas
E. Loukis (�) · Y. Charalabidis
University of the Aegean, Samos, Greece
Y. Charalabidis
10.1007/978-3-319-12784-2_12
[email protected]
262 E. Loukis and Y. Charalabidis
have been initially applied in the private sector, first in the
creative and design in-
dustries, and subsequently in many other industries, aiming to
exploit the ‘collective
wisdom’ (Surowiecki 2004) in order to perform difficult
problem solving and design
activities. This has resulted in the development of a
considerable body of knowledge
on how crowdsourcing can be efficiently and effectively
performed in the private
sector (comprehensive reviews are provided by Rouse 2010;
Hetmank 2013; Ped-

ersen et al. 2013; Tarrell et al. 2013). It was much later that
government agencies
started experimenting with crowdsourcing, aiming to collect
from citizens informa-
tion, knowledge, opinions and ideas concerning difficult
problems they were facing,
and important public policies they were designing, through
some first ‘citizensourc-
ing’ initiatives (Hilgers and Ihl 2010; Nam 2012). So there is
still limited knowledge
on how crowdsourcing can be efficiently and effectively
performed in the special
context of the public sector, much less than in the private
sector. Therefore, exten-
sive research is required for the development of approaches and
methodologies for
the efficient and effective application of crowdsourcing ideas in
government for sup-
porting problem solving and policy making, taking into account
its special needs and
specificities. This is quite important, taking into account that
social problems have
become highly complex and ‘wicked’, with multiple and
heterogeneous stakehold-
ers having different problem views, values and objectives
(Rittel and Weber 1973;
Kunzand Rittel 1979); previous research has concluded that
information and com-
munications technology (ICT) can be very useful for gaining a
better understanding
of the main elements of such problems (e.g. issues, alternatives,
advantages and dis-
advantages perceived by various stakeholder groups; Conklin
and Begeman 1989;
Conklin 2003; Loukis and Wimmer 2012).

This chapter contributes to filling this research gap, by
presenting two approaches
in this direction, which have been developed through extensive
previous relevant
research of the authors: a first approach for ‘active
crowdsourcing’ (in which govern-
ment has an active role, posing a particular social problem or
public policy direction,
and soliciting relevant information, knowledge, opinions and
ideas from citizens),
and a second one for ‘passive crowdsourcing’ (in which
government has a more pas-
sive role, collecting and analyzing content on a specific topic or
public policy that
has been freely generated by citizens in various sources, which
is then subjected to
sophisticated processing). Both of them are based on innovative
ways of using the
recently emerged and highly popular Web 2.0 social media in a
highly automated
manner through their application programming interfaces (API)
(which are libraries
provided by all social media, including specifications for
routines, data structures,
object classes, and variables, in order to access parts of their
functionalities and
incorporate them in other applications).
In particular, the first of them is based on a central ICT
platform, which can pub-
lish various types of discussion stimulating content concerning
a social problem or
a public policy under formulation to multiple social media
simultaneously, and also
collect from them data on citizens’ interactions with this
content (e.g. views, ratings,

votes, comments, etc.), both using the API of the utilized social
media. Finally, these
interaction data undergo various types of advanced processing
(e.g. calculation of
analytics, opinion mining, and simulation modelling) in this
central system, in order
to exploit them to support drawing conclusions from them. This
approach has been
[email protected]
12 Active and Passive Crowdsourcing in Government 263
developed mainly as part of the research project PADGETS
(‘Policy Gadgets Mash-
ing Underlying Group Knowledge in Web 2.0 Media’—
www.padgets.eu), which has
been partially funded by the European Commission.
The second passive crowdsourcing approach is based on a
different type of cen-
tral ICT platform, which can automatically search in numerous
predefined Web 2.0
sources (e.g. blogs and microblogs, news sharing sites, online
forums, etc.), using
their API, for content on a domain of government activity or a
public policy under
formulation, which has been created by citizens freely, without
any initiation, stim-
ulation or moderation through government postings. Through
advanced processing
and analysis of this content in the above platform (using
opinion and argument ex-
traction, sentiment analysis and argument summarization

techniques), conclusions
can be drawn concerning the needs, issues, opinions, proposals
and arguments of cit-
izens on this domain of government activity or public policy
under formulation. This
approach is developed as part of the research project NOMAD
(‘Policy Formulation
and Validation through Non-moderated Crowdsourcing’—
www.nomad-project.eu/),
which is partially funded by the European Commission.
The two approaches presented in this chapter combine elements
from management
sciences (concerning crowdsourcing approaches), political
sciences (concerning
wicked social problems) and technological sciences (concerning
social media ca-
pabilities and API), in order to support problem solving and
policy-making activities
of government agencies. We expect that the findings of this
research will be in-
teresting and useful to both researchers and practitioners of
these three disciplines
who are dealing with the public sector. It should be noted that
governments have
been traditionally collecting content created by various social
actors about domains
of government activity, social problems or public policies under
formulation using
various traditional (offline) practices (e.g. collecting relevant
extracts from newspa-
pers); furthermore, they actively solicited relevant opinions and
ideas from citizens
(through various offline and online citizens’ consultation
channels). However, the
proposed approaches allow government agencies to perform

such activities more
extensively and intensively at a lower cost, reaching easily
wider and more diverse
and geographically dispersed groups of citizens’ (e.g. collecting
relevant content
not only from a small number of top newspapers but also from
numerous bigger or
smaller newspapers, blogs, Facebook accounts, etc.; also,
interacting actively with
many more citizens than the few ones participating in
government consultations),so
that they can gradually achieve mature levels of crowdsourcing.
Furthermore, the
proposed approaches allow overcoming the usual ‘information
overload’ problems
of the traditional practices, as they include sophisticated
processing of the collected
content that extracts the main points of it.
This chapter is organized in seven sections. In ‘Background’
our background is
presented, and then in ‘Research Method’ the research
methodology is outlined.
Next, the two proposed approaches for passive and active
crowdsourcing by govern-
ment agencies are described in ‘An Active Crowdsourcing
Approach’ and ‘A Passive
Crowdsourcing Approach’, respectively. A comparison of them,
also with the ‘clas-
sical’ is presented in ‘Comparisons’, while in the final
‘Conclusion’ our conclusions
are summarized.
[email protected]

12.2 Background
12.2.1 Crowdsourcing
The great potential of the ‘collective intelligence’, defined as a
‘form of universally
distributed intelligence, constantly enhanced, coordinated in
real time, and resulting
in the effective mobilization of skills’, (Levy 1997), to
contribute to difficult prob-
lem solving and design activities has lead to the emergence of
crowdsourcing and
its adoption, initially in the private sector, and subsequently
(still experimentally)
in the public sector as well. Crowdsourcing is defined as ‘the
act of a company or
institution taking a function once performed by employees and
outsourcing it to an
undefined (and generally large) network of people in the form
of an open call’ (Howe
2006), or as ‘a new web-based business model that harnesses
the creative solutions
of a distributed network of individuals’, in order to exploit
‘collective wisdom’ and
mine fresh ideas from large numbers of individuals (Brabham
2008). While the use
of the collective intelligence of a large group of people as a
help for solving difficult
problems is an approach that has been used for long time
(Surowiecki 2004; Howe
2008), it is only recently that crowdsourcing started being
widely adopted as a means
of obtaining external expertise, accessing the collective wisdom

and creativities res-
ident in the virtual crowd. The capabilities provided by the
development and wide
dissemination of ICT seem to have played an important role for
this, as they allow
the efficient participation and interaction of numerous and
geographically dispersed
individuals, and also the analysis of their contributions (Geiger
2012; Zhao and Zhu
2012; Majchrzak and Malhotra 2013). Brabham (2008), based
on the analysis of sev-
eral cases of crowd wisdom at work, which resulted in
successful solutions emerging
from a large body of solvers, concludes that ‘under the right
circumstances, groups
are remarkably intelligent, and are often smarter than the
smartest people in them’,
due to the diversity of opinion, independence, decentralization
and aggregation that
characterize such a crowd.
Crowdsourcing started being applied initially in the creative and
design industries,
and then it expanded into other private sector industries, for
solving both mundane
and highly complex tasks. It gradually becomes a useful method
for attracting an
interested and motivated group of individuals, which can
provide solutions superior in
quality and quantity to those produced by highly knowledgeable
professionals. Such
a crowd can solve scientific problems that big corporate R&D
groups cannot solve,
outperform in-house experienced geophysicists of mining
companies, design original
t-shirts resulting in very high sales, and produce highly

successful commercials
and fresh stock photography against a strong competition from
professional firms
(Surowiecki 2004; Howe 2006, 2008; Brabham 2008, 2012).
This can result in a
paradigm shift and new design and problem solving practices in
many industries.
For these reasons there has been significant research interest on
crowdsourcing,
which has resulted in a considerable body of knowledge on how
crowdsourcing can
be efficiently and effectively performed in the private sector;
reviews of this literature
are provided by Rouse (2010), Hetmank (2013), Pedersen et al.
(2013) and Tarrell
et al. (2013). Initially this research focused on analyzing
successful cases, while later
[email protected]
it started generalizing, based on the experience of multiple
cases, in order to identify
patterns and trends in this area and also to develop effective
crowdsourcing practices.
A typical example in this direction is the study by Brabham
(2012), which, based
on the analysis of several case studies, identifies four dominant
crowdsourcing ap-
proaches: (i) the knowledge discovery and management
approach (= an organization
tasks crowd with finding and reporting information and

knowledge on a particular
topic), (ii) the broadcast search approach (= an organization
tries to find somebody
who has experience with solving a rather narrow and rare
empirical problem), (iii) the
peer-vetted creative production approach (= an organization
tasks crowd with creat-
ing and selecting creative ideas), and (iv) the distributed human
intelligence tasking
(= an organization tasks crowd with analyzing large amounts of
information). Het-
mank (2013), based on a review of crowdsourcing literature,
identifies a basic process
model of it, which consists of ten activities: define task, set
time period, state reward,
recruit participants, assign tasks, accept crowd contributions,
combine submissions,
select solution, evaluate submissions and finally grant rewards.
Also, he identifies
a basic pattern with respect to the structure of crowdsourcing
Information System
(IS), which includes four main components that perform user
management (pro-
viding capabilities for user registration, user evaluation, user
group formation and
coordination), task management (providing capabilities for task
design and assign-
ment), contribution management (providing capabilities for
contributions evaluation
and selection) and workflow management (providing
capabilities for defining and
managing workflows), respectively. Furthermore, there are
some studies that attempt
to generalize the experience gained from successful applications
of crowdsourcing
ideas in order to develop effective practices for motivating

individuals to participate
(Brabham 2009; Stewart et al. 2009).
Rouse (2010), based on a review of relevant literature,
distinguishes between two
types of crowdsourcing with respect to participants’ motivation:
(i) individualistic
(aiming to provide benefits to specific persons and firms), (ii)
community oriented
(aiming to benefit a community of some kind, through ideas and
proposals), and
(iii) mixed (combinations of the above). Furthermore, she
proceeds with identifying
seven more detailed types of participant motivations: learning,
direct compensation,
self-marketing, social status, instrumental motivation (=
motivation to solve a per-
sonal or firm problem, or to address a personal/firm need),
altruism (= motivation to
help the community without personal benefit) and token
compensation (= earning a
small monetary prize or gift). Also, the same publication
concludes that many of the
benefits of crowdsourcing described in the literature are similar
to those of the ‘main-
stream’ outsourcing: cost savings, contracts and payments that
are outcome based
(rather than paid ‘per hour’), and access to capabilities not held
in-house; an addi-
tional benefit of crowdsourcing, which is not provided by
outsourcing, is the capacity
to exploit knowledge and skills of volunteers who might not,
otherwise, contribute.
However, at the same time it is emphasized that—as with all
outsourcing—the de-
cision to crowdsource should only be made after considering all

the production,
coordination and transaction costs, and the potential risks.
Many of the highly publi-
cized crowdsourcing successes have been achieved by
organizations with substantial
project management and new product/services development
systems and capabilities,
which lead to low levels of crowdsourcing coordination and
transaction costs.
[email protected]
12.2.2 Public Sector Application
Crowdsourcing ideas, as mentioned above, have been initially
developed and applied
in the private sector, however later some government agencies
started experimenting
with them. Highly influential for this have been central top-
down initiatives in several
countries, such as the ‘Open Government Directive’ in the USA
(Executive Office
of the President 2009). It defines transparency, participation
and collaboration as the
main pillars of open government:
a. Transparency promotes accountability by providing the public
with information
about what the government is doing.
b. Participation allows members of the public to contribute
ideas and expertise so

that their government can benefit from information and
knowledge that is widely
dispersed in society, in order to design better policies.
c. Collaboration improves the effectiveness of government by
encouraging partner-
ships and cooperation within the federal government, across
levels of government,
and between the government and private institutions.
Crowdsourcing can be quite valuable for promoting and
developing two of these
three main pillars of open government: participation and
collaboration. This has
lead government organizations, initially, in the USA and later in
other countries as
well, to proceed to some first crowdsourcing initiatives, having
various forms of
‘citizensourcing’ for collecting information on citizens’ needs
and for the solution of
difficult problems. These initiatives motivated some first
research in this area, which
aims to analyze these initiatives in order to learn from them,
and to identify common
patterns and trends (Lukensmeyer and Torres 2008; Hilgers and
Ihl 2010; Nam 2012).
Lukensmeyer and Torres (2008) conclude that citizen sourcing
may become a new
source of policy advice, enabling policy makers to bring
together divergent ideas
that would not come from traditional sources of policy advice;
furthermore, it may
change the government’s perspective on the public from an
understanding of citizens
as ‘users and choosers’ of government programs and services to
‘makers and shapers’

of policies and decisions. Hilgers and Ihl (2010) developed a
high-level framework
for the application of citizen sourcing by government agencies,
which consists of
three tiers:
1. Citizen ideation and innovation: this first tier focuses on the
exploitation of the
general potential of knowledge and creativity within the
citizenry to enhance the
quality of government decisions and policies, through various
methods, such as
consultations and idea and innovation contests.
2. Collaborative administration: the second tier explicitly
addresses the integration
of citizens for enhancing existing public administrative
processes.
3. Collaborative democracy: this tier includes new ways of
collaboration to improve
and expand public participation within the policy process,
including the incorpo-
ration of public values into decisions, improving the quality of
decisions, building
trust in institutions and educating citizens.
[email protected]
Nam (2012), based on the study of citizen-sourcing initiatives
in the USA, developed
a framework for the description and analysis of such initiatives,

which consists of
three dimensions: purpose (it can be for image making,
information creation, service
co-production, problem solving and policy-making advice),
collective intelligence
type (professionals’ knowledge or nonprofessionals’ innovative
ideas), and govern-
ment 2.0 strategy (it can be contest, wiki, social networking, or
social rating and
voting).
However, since public-sector crowdsourcing is still in its
infancy, having much
less maturity than private-sector crowdsourcing, further
research is required in this
area; its main priority should be the development of approaches
and methodologies
for the efficient and effective application of crowdsourcing
ideas in government
for supporting problem solving and policy making, taking into
account its special
needs and specificities. They should focus on addressing the
inherent difficulties of
modern policy making, which are caused by the complex and
‘wicked’ nature of
social problems (Rittel and Weber 1973; Kunz and Rittel 1979),
enabling a better
and deeper understanding of the main elements of them (e.g.
issues, alternatives,
advantages and disadvantages perceived by various stakeholder
groups; Conklin and
Begeman 1989; Conklin 2003; Loukis and Wimmer 2012).
12.3 Research Method
The development of the two proposed approaches for active and

passive crowd-
sourcing, respectively, was performed through close cooperation
with public sector
employees experienced in public policy making, using both
qualitative and quanti-
tative techniques: semi-structured focus group discussions,
scenarios development
and questionnaire surveys.
12.3.1 Active Crowdsourcing
The development of our active crowdsourcing approach
(described in ‘An Active
Crowdsourcing Approach’) included the following six phases
(for more details on
them see DeliverableD2.1 ‘Padget Design and Decision Model
for Policy Making’
of the PADGETS project accessible in its website
www.padgets.eu):
a. Initially three semi-structured focus group discussions were
conducted in the
three government agencies participating in the PADGETS
project (mentioned in
the introductory section) as user partners (Center for
eGovernance Development
(Slovenia), ICT Observatory (Greece), Piedmont Regional
Government (Italy)),
which aimed at obtaining an understanding of their policy-
making processes, the
degree and form of public participation in them, and also their
needs for and
interest in ICT support.
[email protected]

b. The main themes of the above semi-structured focus group
discussions were used
for the design of a questionnaire, which was filled in and
returned to us through
e-mail by another four government agencies (City of
Regensburg (Germany),
World Heritage Coordination (Germany), North Lincolnshire
Council (UK), IT
Inkubator Ostbayern GmbH (Germany)), which have some form
of close coop-
eration with the above three user partners of PADGETS project.
This allowed us
to obtain the above information from a wider group of
government agencies, and
cover a variety of government levels (national, regional and
local).
c. Based on the information collected in the above first two
phases the main idea
of the active crowdsourcing approach was formulated: combined
use of multiple
social media for consultation with citizens on a social problem
or public policy of
interest, and sophisticated processing of relevant content
generated by citizens.
d. Three application scenarios were developed in cooperation
with the above three
user partners of PADGETS project concerning the application of
the above main
idea for a specific problem/policy of high interest. Each of these
scenarios de-

scribed which social media should be used and how, what
content should be
posted to them, and also how various types of citizens’
interactions with it (e.g.
views, likes, comments, retweets, etc.) should be monitored and
exploited, and
what analytics would be useful to be computed from them.
e. Finally, a survey was conducted, using a shorter online
questionnaire, concerning
the required functionality from an ICT tool supporting the use
of social media
for such multiple social media consultation. It was distributed
by personnel of
the three user partners involved in the PADGETS project to
colleagues from
the same or other government agencies, who have working
experience in public
policy making, and finally was filled in by 60 persons.
f. Based on the outcomes of the above phases C, D and E, we
designed this govern-
ment active crowdsourcing approach in more detail, and then
the required ICT
infrastructure and its application process model (described in
‘Description’, ‘ICT
Infrastructure’ and ‘Application Process Model’, respectively).
12.3.2 Passive Crowdsourcing
The development of our passive crowdsourcing approach
(described in ‘A Passive
Crowdsourcing Approach’) included the following seven phases
(for more details on
them see Deliverable D2.1 ‘Padget Report on User
Requirements’ of the NOMAD

project in its website www.nomad-project.eu/):
1. Initially the main idea was developed, in cooperation with the
user partners of the
NOMAD project (Greek Parliament, Austrian Parliament,
European Academy
of Allergy and Clinical Immunology), based on the digital
reputation and brand
management ideas from the private sector (e.g. see Ziegler and
Skubacz 2006):
passive retrieval of content that has been generated by citizens
freely (without
any initiation, stimulation or moderation through government
postings) in nu-
merous Web 2.0 sources (e.g. blogs and microblogs, news
sharing sites, online
[email protected]
forums, etc.) on a specific topic, problem or public policy, and
then sophisticated
processing of this content using opinion mining techniques.
2. Four application scenarios of this idea were developed by the
above user partners
of the NOMAD project. Each application scenario constitutes a
detailed realistic
example of how this passive croudsourcingidea could be applied
for supporting
the formulation of a particular public policy, and describes how
various types of
users involved in this might use an ICT platform that

implements this idea.
3. A questionnaire was distributed electronically to a sample
population of potential
users, which included questions concerning: (a) respondent’s
personal informa-
tion, (b) general citizens’ participation information (in his/her
organization), (c)
current use of social media in policy-making processes, (d)
general assessment
of this ideaand and (e) specific relevant requirements.
4. Organization of focus groups and workshops with the
participation of potential
users. This allowed in-depth discussion among people
experienced in the design
of public policies, with different backgrounds and mentalities,
about this new
idea, and also ways and processes of its practical application,
required relevant
ICT functionalities and at the same time possible problems and
barriers.
5. Organization of in-depth interviews based of a series of fixed
questions concerning
attitudes towards this new idea, its usefulness and applicability.
6. A review of systems that offer at least a part of the above
ICT functionalities (e.g.
for content retrieval, opinion mining, etc.).
7. Based on the outcomes of the above phases we designed this
government passive
crowdsourcing approach in more detail, then its application
process model and
finally the required ICT infrastructure (as described in

‘Description’, ‘Application
Process Model’ and ‘ICT Infrastructure’, respectively).
12.4 An Active Crowdsourcing Approach
12.4.1 Description
The proposed active crowdsourcing approach is based on the
centralized automated
publishing of multimedia content (e.g. a short text, a longer
description, images,
videos, etc.) concerning a social problem of interest or a public
policy under formu-
lation to the accounts of a government agency in multiple social
media (e.g. Facebook,
Twitter, YouTube, Picasa and Blogger), in order to actively
stimulate discussions on
it. As mentioned in ‘Introduction’ and ‘Background’ social
problems have become
highly complex and ‘wicked’, with multiple and heterogeneous
stakeholders having
different problem views, values and objectives (Rittel and
Weber 1973; Kunz and
Rittel 1979; Conklin 2003), so in order to address this inherent
difficulty our method-
ology uses multiple social media, with each of them attracting
different groups of
citizens. Throughout these social media consultations we
continuously retrieve and
monitor various types of citizens’ interactions with the content
we have posted (e.g.
views, likes, ratings, comments and retweets), and finally we
process these interac-
tions in order to support drawing conclusions from them. Both
content posting and

[email protected]
interactions’ continuous retrieval are performed in a highly
automated manner using
the API of these social media from a central ICT platform, in
which also processing
and results presentation takes place.
In particular, a government agency policy maker, through a
web-based dashboard
or a mobile phone application, initiates a campaign concerning
a specific topic,
problem or policy in multiple social media. For this purpose,
he/she creates relevant
multimedia content (e.g. short and longer topic description,
images, videos, etc.),
which are then automatically published in the corresponding
social media (e.g. in
the Twitter the short-topic description, in Blogger the longer
one, in YouTube the
video, in Picasa the images, etc.) by a central platform. The
citizens will view this
content, and interact with it (in all the ways that each social
media platform allows),
either through these social media, or through a mobile phone
application. Then,
these interactions will be automatically retrieved and shown
continuously to the
policy maker, through the above web-based dashboard or mobile
phone application,
so that appropriate interventions can be made (i.e. new content
can be published)

if necessary. Finally, after the end of the campaign,
sophisticated processing of all
citizens’ interactions with the above content will be performed
in this central ICT
platform, using a variety of techniques (e.g. calculation of web
analytics and opinion
mining), in order to provide useful analytics that support
government decision and
policy making. In Fig. 12.1, this active crowdsourcing approach
is illustrated.
The practical application of the above approach will lead to a
collection of large
amounts of content generated by citizens in various Web 2.0
social media concerning
the particular topic, problem or policy we have defined through
our initial postings.
So it will be of critical importance to use highly sophisticated
methods of automated
processing this content, in order to offer substantial support to
government agencies
policy makers in drawing conclusions from it . Part of this
citizens-generated content
is numeric (e.g. numbers of views, likes, retweets, comments,
ratings, etc.), so it can
be used for the calculation of various analytics. However, a
large part of this content
is in textual form, so opinion mining, defined as the advanced
processing of text in
order to extract sentiments, feelings, opinions and emotions (for
a review of them
see Maragoudakis et al. 2011), will be a critical technology for
processing it and
maximizing knowledge extraction from it. The development and
use of opinion
mining first started in the private sector, as firms wanted to

analyze comments and
reviews about their products, which had been entered by their
customers in various
websites, in order to draw conclusions as to whether customers
like the specific
products or not (through sentiment analysis techniques), the
particular features of
the products that have been commented (through issues
extraction techniques) and
the orientations (positive, negative or neutral) of these
comments (through sentiment
analysis techniques). These ideas can be applied in the public
sector as well, since
citizens’ comments are a valuable source of information that
can be quite useful
for government decision and policy making: it is important to
identify the main
issues posed by citizens (through issues extraction) on a
particular topic, problem
or policy making we are interested in, and also the
corresponding sentiments or
feelings (positive, neutral or negative—through sentiment
analysis). More details
about this active crowdsourcing approach are provided by
Charalabidis and Loukis
(2012), Ferro et al. (2013) and Charalabidis et al. (2014a).
[email protected]
Fig. 12.1 An approach for
active crowdsourcing in
government

12.4.2 ICT Infrastructure
An ICT platform has been developed for the practical
application of the above ap-
proach, which provides all required functionalities to two main
types of users of
it: government agencies’ policy makers and citizens. In
particular, a ‘policy makers
dashboard’ (accessible through a web-based or a mobile
interface (Android mobile
application)) enables government agencies’ policy makers:
1. To create a multiple social media campaign, by defining its
topic, the starting and
ending date/time, the social media accounts to be used, and the
relevant messages
and multimedia content to be posted to them
2. To monitor continuously citizens’ comments on the
messages; in Fig. 12.2, we
can see this part of the web-based policy-makers’ interface,
which is structured
[email protected]
Fig. 12.2 Policy-makers’ interface for viewing active
campaigns, messages and citizens’ feedback
in three columns: in the first column, the active campaigns are
shown, while by
selecting one of them in the second column are shown the

corresponding messages
posted by the policy maker (the initial, and the subsequent
ones), and finally by
selecting one of these messages in the third column are shown
citizens’ comments
on it (textual feedback stream)
3. And after the end of the campaign to view (as graphics and
visualizations) a
set of analytics and opinion mining results, which are produced
by the decision
support component of the platform (described later in this
section) for the whole
campaign.
The citizens can see the initial content of each campaign, and
also other citizens’
interactions with it (e.g. textual comments), either through the
interfaces of the cor-
responding social media, or through a mobile interface (Android
mobile application)
or a widget, which enables citizens to view active campaigns,
and by selecting one of
them to view all policy maker and citizens’ comments on it, or
add a new comment.
The technological architecture of this ICT platform is shown in
Fig. 12.3. We can
see that it consists of two main areas:
1. The front-end area, which provides the abovementioned web
interface to the
policy makers, and also the mobile application and widget
interfaces to both
policy makers and citizens.

2. The back-end area, which includes three components: the
first of them perfoms
publishing of various content types in multiple social media
through the second
component, which consists of connectors with the utilized social
media, while
the third component performs aggregation/analysis of citizens
interactions with
[email protected]
Fig. 12.3 Active crowdsourcing ICT platform technological
architecture
the above-published content in these social media, retrieved
through the second
component; it consists of one subcomponent that allows
continuous monitoring
of these citizens interactions, and several subcomponents that
provide analytics
for government policy-makers’ decision support.
One of these subcomponents collects and processes the ‘raw
analytics’ provided by
the analytics engines of the utilized social media. Another
subcomponent provides
more advanced analytics, which concern citizens’ textual inputs
(e.g. blog post-
ings, comments, opinions, etc.), processing them using opinion
mining techniques
(Maragoudakis et al. 2011). In particular, it performs the
following three types of

tasks:
[email protected]
• Classification of an opinionated text (e.g. a blog post) as
expressing a posi-
tive, negative or neutral opinion (this is referred to as
document-level sentiment
analysis).
• Classification of each sentence in a such a text, first as
subjective or objective (i.e.
determination of whether it expresses an opinion or not), and
for each subjective
sentence (i.e. expressing an opinion) classification as positive,
negative or neutral
(this is known as sentence-level sentiment analysis).
• Extraction of specific issues commented by the author of a
text, and for each issue
to identify its orientation as positive, negative or neutral (this is
referred to as
feature-level sentiment analysis).
Another subcomponent performs simulation modelling
(Charalabidis et al. 2011),
having mainly two objectives: estimation of the outcomes of
various citizens’ propos-
als on the public policies under discussion, and also forecasting
the future levels of
citizens’interest in and awareness of these policies. The
simulation modelling takes as

input various indicators produced by the other two
aforementioned subcomponents.
12.4.3 Application Process Model
Furthermore, an application process model for this active
crowdsourcing approach
has been developed. It provides a model of the process to be
followed by government
agencies for the practical application of it, which includes a
sequence of specific
activities to be executed:
1. The policy maker initially setsup a policy campaign, using
the capabilities of the
central ICT platform described above, through a graphical user
interface
2. Then he/she creates textual content for this campaign (both
short and longer policy
statements), and also can add various types of multimedia
content to it (e.g. policy
images, video, etc.)
3. And finally defines the multiple social media accounts to be
used in this campaign
4. And views a preview of the campaign in each of them
5. The campaign is launched by publishing the above content (in
each of these
multiple social media will be automatically published the
appropriate part of the
above content, e.g. in the Twitter will be published the short
policy statement, in
Blogger the longer one, in YouTube the video, in Picasa the
images, etc.).

6. Citizens interact with the published content in various ways
in these social media
(in the particular ways each of them allows): they access and
see this content, rate
it and make some comments on it, retransmit it in their
networks, etc
7. The above citizens’ interactions are automatically retrieved
continuously from
all the used social media in the central ICT platform, and after
the end of the
campaign are processed there using various advanced
techniques (as described
above), in order to calculate useful analytics that provide
assistance and support
to the policy maker.
8. The results are sent immediately to the policy maker, by e-
mail or SMS message.
[email protected]
Fig. 12.4 A typical application scenario of the active
crowdsourcing ICT approach
In Fig. 12.4, we can see a typical application scenario of this
approach.
12.5 A Passive Crowdsourcing Approach

12.5.1 Description
The proposed passive crowdsourcing approach is based on the
exploitation of the
extensive political content created in multiple Web 2.0 sources
(e.g. blogs and mi-
croblogs, news sharing sites, and online forums) by citizens
freely (= without active
stimulation through some government posting) concerning
various domains of gov-
ernment activity and public policies. An ICT platform
automatically retrieves this
content from these Web 2.0 sources using their API, and then
processes it using so-
phisticated linguistic processing techniques in order to extract
from it relevant issues,
proposals and arguments. So in this approach government is not
active in conduct-
ing crowdsourcing (as it is in the active crowdsourcing
approach presented in the
previous section, by posing to citizens particular discussion
topics, problems or poli-
cies), but it remains passive (just ‘listening’ to what citizens
discuss, and analyzing
the content they freely produce in order to extract knowledge
from it). Taking into
account the highly complex and ‘wicked’ nature of modern
social problems, which
usually have multiple and heterogeneous stakeholders with
different problem views,
values and objectives (Rittel and Weber 1973; Kunz and Rittel
1979; Conklin 2003),
our passive crowdsourcing approach uses multiple Web 2.0
content sources, with
diverse political perspectives and orientations.

[email protected]
In particular, this passive crowdsourcing approach includes
three stages, which
are illustrated in Fig. 12.5. The first stage, called ‘Listen’,
includes listening and mon-
itoring what citizens say concerning a domain of government
activity (e.g. higher
education) or a public policy under formulation (e.g. a new
policy on higher educa-
tion) in a large set of Web 2.0 sources S1, S2,. . . , SN defined
by the policy maker.
For this purpose a ‘focused crawler’ is used, which is a program
that browses the
above sources in an automated and organized manner, and
retrieves solely content
that is relevant to the specific topic of interest.
The second stage, called ‘Analyse’, includes advanced
processing and analysis
of the retrieved content, from which are identified relevant
issues, proposals and
arguments expressed by citizens. As the majority of this content
is in textual form,
this stage makes use of advanced linguistic processing
techniques (for a review of
them, see Maragoudakis et al. (2011)). In particular, each
content unit retrieved by
the crawler will go through a series of automated processing
steps:
• Language detection, which will recognize the language used in

it.
• Opinion and argument extraction, using appropriate semantic
similarity measures
and inference mechanisms that allow the identification of
elements of the analyzed
content which are pertinent to the particular domain or policy.
• Sentiment analysis, using smart sentiment classifiers that
recognize the polarity
(positive, neutral, and negative) of the elements identified
above.
• Argument summarization, using appropriate algorithms for
generating qualitative
information about opposing arguments, in the form of
anonymity-preserving and
automatically generated summaries.
The third stage, called ‘Receive’, aims to present to the end-
user (policy maker)
the knowledge acquired from the previous stages in a complete,
coherent and us-
able manner. The platform will provide an aggregated view of
the results of the
above processing, their polarity, their association with various
policy concepts and
statements, and also statistical indications of their significance
and impact. For this
purpose visual analytics (Wong and Thomas 2004; Thomas and
Cook 2005; Keim
et al. 2010) will be used, so that policy makers can view
visualizations of the results
of previous stages, and easily understand them with minimal
cognitive effort (e.g. in
a familiar word cloud form), which is quite important due to the

high information
overload the policy makers usually experience.
The knowledge gained through this passive crowdsourcing (e.g.
issues, propos-
als and arguments concerning a domain of government activity
or a policy under
formulation) can be used in order to formulate more specific
questions, positions or
proposals about the particular policy and then solicit citizens’
feedback and contribu-
tions on them through more ‘active’ forms of communication.
This can be achieved
through ‘active crowdsourcing’, i.e. by making relevant
stimulating postings (based
on the findings from passive crowdsourcing) to various social
media (e.g. blogs,
Twitter, Facebook, YouTube, etc.), and also to official
government e-participation
websites, in order to collect citizens’ interactions with this
content (e.g. ratings,
votes, comments, etc.). Therefore, the proposed ‘passive
crowdsourcing’ approach
[email protected]
S1 S2 .... SN
Listen Analyse Receive
Fig. 12.5 The three stages of the government passive

can be combined with the ‘active crowdsourcing’ approach
described in the previ-
ous section, in order to increase its effectiveness. More details
about this passive
crowdsourcing approach are provided by Charalabidis et al.
(2014b).
12.5.2 Application Process Model
Extensive effort was required in order to design how the above
passive crowdsourcing
concept can be practically applied by government agencies and
work efficiently, and
formulate an apropriate process model for its application. So we
will describe first
this aspect of it, and then the required ICT infrastructure in the
following ‘ICT
Infrastructure’ (since the latter has been to a large extent based
on the former). There
was wide agreement that since the domains of government
activity and the public
policies for them are quite complex and multidimensional
entities, it is not possible
to search for content on them in the predefined Web 2.0 sources
using just a small
number of keyworks. So it was concluded that the best solution
for addressing this
complexity is to develop a model of the specific domain, for
which a policy is
intended, which will consist of the main terms of it and the
relations among them (a
kind of ‘structured thesaurus’ of this domain). An example of
such a domain model
for the energy domain, which has been developed based on the
documents of the

‘Greek Strategy for Energy Planning’, is shown below in Fig.
12.6.
Based on such a domain model we can then build a policy
model, by adding to
the nodes of the former: (a) the ‘policy statements’ (= the
specific policy objectives
and actions/interventions that a policy includes) and also (b)
positive and negative
arguments in favour or against them, respectively. An example
of such a policy model
for the energy domain is shown in Fig. 12.7 (including three
policy objectives, one
concerning the whole national energy planning, and two
concerning the renewable
energy sources, six positive arguments and nine negative ones).
These two models (domain and policy ones) can be used for
searching for and
retrieving relevant content concerning the main terms of a
domain, or the policy
statements and the arguments of a policy. This search has to be
performed at regular
time intervals in order to keep the retrieved content updated,
and the results should
be stored in a database, and then undergo the advanced
processing mentioned in the
previous section (in the ‘Analyse’ stage), the results of which
will be also stored in
the same database. The authorized policy makers will have the
capability at any time
to explore the results of this advanced processing stored in the
above database, and
[email protected]

Fig. 12.6 Energy domain model
view various visualization of them, e.g. the most frequently
mentioned terms-topics
with respect to a particular domain or policy model (e.g. in a
tag cloud form).
Also, most of the potential users we interviewed mentioned that
it is important
to view citizens’ sentiment with respect to these frequently
mentioned terms-topics
(i.e. whether citizens regard each of them as positive, negative
or neutral), or even
with respect to the individual policy statements and arguments
of a policy model.
Furthermore, our interviewees noted that all the above (i.e.
frequently mentioned
terms-topics and sentiments) may differ significantly between
different citizens
groups (e.g. between age, gender, education and region groups),
so policy mak-
ers should have the capability to view them for particular
citizens’ groups, or to view
comparisons between different citizens’ groups. Furthermore,
since public stance
changes rapidly, it was mentioned that policy makers should
have the capability to
view all the above information for particular user-defined time
periods, or to compare
between different time periods, while future forecasts of them
would be quite useful.

Based on the above, a model of the process to be followed by
government agencies
for the practical application of this passive crowdsourcing
approach was developed.
It includes the following nine activities:
1. Development of a domain model
2. Development of a policy model
3. Definition of Web 2.0 content sources
4. Search of these content sources at regular time intervals
5. Process retrieved content and store results in a database
6. Policy maker views polarized tag glouds with the most
frequently mentioned
terms-topics with respect to a particular domain or policy model
and the
corresponding sentiments for a predefined time period.
7. Policy maker views the sentiments with respect to the
individual policy statements
and arguments of a policy model.
8. Policy maker views the above for particular citizens’ groups,
and then makes
comparisons between different citizens’ groups, or with other
time periods.
9. Policy maker views short-term future forecasts of the above.
[email protected]
Fig. 12.7 Energy policy model based on the above energy

domain model (including policy
statements and arguments)
Finally, we identified four roles which are required for the
practical application of
this process model:
• Domain models author: this role will create domain models
and also modify
existing ones.
• Policy models author: this role will create policy models based
on existing domain
models (= add to their nodes policy statements and
argumentations) and also
modify existing ones.
• End user/policy maker: this role will view the results of
processing the content
retrieved from the Web 2.0 sources in all the abovementioned
forms.
• Platform administrator: this role will have full access to all
platform functionali-
ties, monitor platform operation, manage the set of users
accessing the platform
and their access rights to the offered services and
functionalities.
[email protected]
12.5.3 ICT Infrastructure

Based on the above application process model, we proceeded to
the design of the
functional architecture of the required ICT platform. In
particular, we defined in
more detail the functionality to be provided to each of the above
four roles:
1. Domain models author
– Creation of new domain models (= definition of main terms of
the domain and
the relations among them).
– Modification of existing domain models.
– Import of external domain models (e.g. having the form of
ontology files in
OWL).
– Export of domain models (e.g. in the form of ontology files in
OWL).
2. Policy models author
– Access to domain models.
– Creation of new policy models (using existing domain models,
by adding
policy statements and arguments to their nodes).
– Modification of existing policy models.
– Import of external policy models (e.g. having the form of
ontology files in
OWL).
– Export of policy models (e.g. in the form of ontology files in
OWL).
3. End user/policy maker

– View the most frequently mentioned terms-topics with respect
to a particular
domain or policy model for a predefined time period, citizens’
group and
sources subset (see Fig. 12.8 for a first design of the
corresponding screen).
– View sentiment for these terms-topics.
– View sentiment for each policy statement and argument of a
particular model.
– View differentiations of the above over time.
– View differentiations of the above across citizens’ groups.
– View differentiations of the above across sources subsets.
– View short-term future projections of the above.
4. Platform administrator
– Users and roles management.
– Domain and policy roles management.
– Monitoring and administration of all platform services.
Based on the above functional architecture of the platform, its
technological archi-
tecture was designed. The objective of this design was to
provide this functionality
with an acceptable response time. Since this could not be
achieved through online
retrieval of content from a large number of sources (e.g.
numerous blogs, news web-
sites, Facebook, Youtube and Twitter accounts) and processing
of it at the time a user
initiates a search, the only solution was to perform a scan of the
predefined sources
at some regular time intervals (e.g. every 6 h) in order to
retrieve new content, store it
in a database and then process it and store the results in the

same database. Whenever
the user performs a search, the results will be produced in a
very short time, using this
database. This separation between sources scanning and content
processing on one
[email protected]
Fig. 12.8 View of the most frequently mentioned terms-topics
with respect to a particular domain
or policy model for a predefined time period, citizens’ group
and sources subset
hand, and users’ searches processing on the other, allows a low
response time and at
the same time sufficiently ‘fresh’ content for policy makers (i.e.
allows addressing
these two conflicting requirements).
The above design leads to a three layers’ technological
architecture of the platform,
which consists of a storage layer, a processing layer and a
presentation layer, and
is shown in Fig. 12.9. Each of them includes a number of
components, performing
different tasks, which act as services coordinated by an
orchestration component.
In particular, the data storage layer includes the repositories
where the raw and
processed content is stored:

• The content repository: it stores the raw content retrieved
from the Web 2.0
sources, the cleaned content derived from the raw data, the
content uploaded by
users and the results of the linguistic analysis associated with
each content unit.
• The model repository: it stores in a structured form the
domain and policy models
entered by users with domain expert and policy advisor roles.
• The metadata repository: it stores the metadata retrieved or
calculated for our
sources.
• The thematic catalogues: it stores a representation of the
thematic categories used
by the platform in order to characterize each content unit.
• The users repository: it contains information about the roles
and the users of the
platform.
[email protected]
Presenta on Layer (User Interfaces)
Model AuthoringSystem Interface
Processing Layer
Data Acquisi on

Data Classifica on & Argument Summariza on
Keyword
Selec on
Rela on
Defini on
Argument
Building
Thema c
Classifier
Thema c
Catalogue
Dynamic Content
Crawlers
Sta c Content
Crawlers
Policy Model
Sharing
Visualisa on &
Analysis
Storage Layer
Thema c
Catalogues
Content

Domain/
Policy
Models
Content Cleaner
Metadata
Opinion Mining & Argument Extrac on
Sen ment
Analyser
Segment
Extractor
Argument
Extractor
Linguis c
Demographic
Extractor
Tag Cloud
Generator
Content Inser on
Argument
Summarizer
Users
Administra ve

Interface
Fig. 12.9 Passive crowdsourcing ICT platform technological
architecture
The processing layer includes all the components that retrieve
and process the content
from the predefined sources, which are organized in three sub-
layers:
• The data acquisition layer, which includes the crawling
components for fetching
content from the sources, using their APIs, as well as the
modules responsible for
cleaning the fetched content and obtaining the actual textual
information from it
(static content crawlers, dynamic content crawlers and content
cleaner).
• The data classification and argument summarization layer,
which includes (a)
the thematic classifier, which processes the available content
and associates it
[email protected]
with one more of the defined thematic categories in the thematic
catalogues, and
(b) the result summarizer, which processes the available results
and provides a
summarization that allows their presentation in a condensed
manner.

• The argument extraction and opinion mining layer, which
includes all the com-
ponents that process the available content and extract segments,
arguments and
sentiments (segment extractor, argument extractor, sentiment
analyzer, linguistic
demographic extractor, tag cloud generator).
The presentation layer includes all the components that either
require input from the
user or present to him/her the results:
• The thematic catalogue interface, for entering or updating the
available thematic
categories and also terms associated with each category.
• The keyword selection interface, which allows entering
keywords/terms for
creating domain models.
• The relation definition interface, which allows the user to
introduce relations
between the above keywords/terms for the definition of domain
models.
• The argument building interface, which allows the user to
insert in natural lan-
guage statements and arguments supporting or objecting to
policy statements of
policy models.
• The policy model sharing interface, which provides a
catalogue of the policy
models created by the user and allows defining them as visible
to others.

• The admin interface, which provides the means to an
administrator to manage the
configurable aspects of the system.
• The visualisation and analysis module, which utilizes the
results of the processing
layer in order to provide the user with a view of domain and
policy models, and
also various visualizations of the results of users’ searches,
enabling also the
selection of sources, demographic characteristics and time
periods.
The domain and policy modelling components of the
presentation layer (thematic cat-
alogue, keyword selection, relation definition, argument
building and policy model
sharing interfaces) will be based on the ELEON Ontology
Authoring and Enrich-
ment Environment (http://www.iit.demokritos.gr/ eleon),
developed by the National
Center for Scientific Research ‘Demokritos’, which participates
as a partner in the
NOMAD project. It supports editing ontologies and relating
such ontologies with lin-
guistic resources that can be used to extract structured
ontological information from
text, and also supports the author with a number of innovative
methods for ontology
checking (Bilidas et al. 2007) and autocompletion
(Konstantopoulos et al. 2011).
The sentiment analyser will be based on existing tools
developed by ‘Demokritos’
as well (Rentoumi et al. 2009; Rentoumi et al. 2010), which are
based on algorithms

that take into account various intricacies of the language forms
commonly used in the
context of user-generated web content, such as metaphors,
nuances, irony, etc. For
the summarization task the ‘n-gram graph framework’
(Giannakopoulos et al. 2008;
Giannakopoulos and Karkaletsis 2009) will be used, which is a
statistical, domain
agnostic and language-independent framework that allows the
analysis of texts as
character n-gram graphs.
[email protected]
12.6 Comparisons
In this section, we make a comparison between the two
proposed crowdsourcing ap-
proaches, and also with the private and public sector
crowdsourcing patterns reported
in the literature (outlined in ‘Background’), identifying
similarities and differences.
Both approaches adopt two of the four crowdsourcing
approaches identified by
Brabham (2012): mainly ‘knowledge discovery’ and secondarily
‘creative produc-
tion’. From the four public sector specific crowdsourcing
purposes identified by
Nam (2012) they focus mainly on ‘information creation’, and
secondarily on ‘prob-
lem solving’ and ‘policy making advice’; also from the two

types of collective
intelligence mentioned in the same study both approaches aim at
‘nonprofession-
als innovative ideas’ and much less at ‘professionals
knowledge’. With respect to
participants’ motivation, from the two main motivation types
identified by Rouse
(2010) both approaches are based mainly on citizens’
‘community oriented’ motiva-
tions and much less on ‘individualistic’ ones (since none of the
two approaches is
based on the monetary or other types of rewards used in private
sector crowdsourc-
ing); also, from the seven more detailed participants’
motivations identified in the
same study the ‘altruism’, ‘instrumental motivation’ and ‘social
status’ seem to be
ones our approaches mainly rely on. Finally from the four
organizer benefits identi-
fied in the same study, both methodologies aim to provide to
adopting government
agencies ‘access to capabilities not held in-house’ and ‘capacity
to exploit knowledge
and skills of volunteers who might not otherwise contribute’,
but not ‘cost savings’
or ‘contracts and payments that are outcome based’.
With respect to the required ICT infrastructures it should be
noted that the one of
our active crowdsourcing approach—described in ‘ICT
Infrastructure’—has some
similarities with the typical crowdsourcing IS (which according
to Hetmank (2013))
includes user, task, contribution and workflow management
components), but also
important differences as well. In particular, this active

crowdsourcing ICT platform
includes ‘task management’ components (that enable setting-up
a campaign and cre-
ating/adding multimedia content to it) and ‘contribution
management’ components
(processing citizens’ interactions with the above content in the
utilized social media).
However, it does not include ‘user management’ components (as
the management
of the citizens participating in our campaigns is conducted
through our social me-
dia accounts) and ‘workflow management’ ones. Also the
process model we have
developed for the application of this active crowdsourcing
approach—described in
‘Application Process Model’—has some similarities with the
typical crowdsourcing
process model (according to Hetmank (2013)), but also
important differences as well.
In particular, this application process model includes four out of
the ten activities of
this typical crowdsourcing process model (define task, set time
period, accept crowd
contributions, and combine submissions), however most of them
in a quite different
form. However, the former does not include the remaining six
activities of the latter
(state reward, recruit participants, assign tasks, select solution,
evaluate submissions
and finally grant rewards), due to inherent differences of our
approach from the mainstream crowdsourcing (e.g. lack of
reward and specific task
[email protected]

assignments, participants management through our accounts in
the utilized social
media, lack of individual submissions evaluation, etc.).
On the contrary, both the application process model of our
passive crowdsourcing
approach and also the structure and components of the required
ICT platform are
quite different from the one of the typical crowdsourcing
approaches, which has been
identified by Hetmank (2013). In particular, our passive
does not include any of the main tasks of the mainstream
crowdsourcing (problem
definition, open call for contributions, search for and
motivation of contributors,
evaluation of contributions, and finally reward of the most
successful of them), but
has a quite different task structure (including domain and policy
modelling, definition
of the Web 2.0 sources to be used, automated content retrieval
and sophisticated
processing of the retrieved content, which do not exist in
mainstream crowdsourcing).
For this reason, its application process model—described in
5.2—is quite different
from the one of the typical crowdsourcing. Also, the passive
crowdsourcing ICT
platform we have designed—described in 5.3—includes
‘contribution management’
components (allowing advanced linguistic processing of the
textual content retrieved

from multiple Web 2.0 sources), but not ‘task management’,
‘user management’ and
‘workflow management’ ones. This new passive crowdsourcing
approach requires
more extensive and complex ICT infrastructures than the
existing crowdsourcing
approaches, which are based on the use of API of numerous
Web 2.0 sources, in
combination with advanced linguistic processing techniques.
12.7 Conclusions
Crowdsourcing has been initially developed and applied in the
private sector, and later
introduced in the public sector (still in experimental mode).
Therefore, there is limited
knowledge concerning the efficient and effective application of
crowdsourcing ideas
in government, taking into account its special needs and
specificities, much less
than in the private sector. This chapter contributes to filling this
gap, presenting two
approaches for this purpose: a first one for ‘active
crowdsourcing’, and a second one
for ‘passive crowdsourcing’ by government agencies. The
foundations of both come
from management sciences (crowdsourcing research), political
sciences (wicked
social problems research) and technological sciences (social
media capabilities and
API). For each of these approaches has been presented the basic
idea, the architecture
of the required ICT infrastructure, and its application process
model.
A common characteristic of the two proposed government

crowdsourcing ap-
proaches is that they do not include competitive contest among
the participants and
monetary or other types of rewards, as in private sector
crowdsourcing, but mainly
collaboration among citizens for knowledge and innovative
ideas creation. Also they
both rely mainly on community-oriented motivations of the
participants and not
on individualistic ones. They aim to provide to adopting
government agencies not
benefits associated with ‘cost savings’ or ‘contracts and
payments that are outcome
based’ (as in the mainstream private sector crowdsourcing), but
benefits concerning
[email protected]
Table 12.1 Similarities and differences between the proposed
active and passive crowdsourcing
approaches
Similarities
Both approaches exploit multiple Web 2.0 social media
simultaneously
In a centrally managed manner based on a central platform
Fully automatically using their API
And then both make sophisticated processing of the collected

content, in order to extract the main
points from it, in order to reduce the ‘information overload’ of
government decision makers
They both aim to provide to government agencies access to
resources (e.g. information, knowledge,
ideas, and skills) not available in-house
But without competitive contests and monetary rewards (which
are quite usual in private sector
crowdsourcing)
Relying both on community oriented motivations of the
participants and not on individualistic
ones
Differences
The active crowdsourcing approach uses the accounts of the
particular government agency in
several social, while the passive crowdsourcing approach goes
beyond them, using other accounts,
blogs, websites, etc., not belonging to government agencies
Also the former actively stimulates discussions and content
generation by citizens on specific topics
(through government postings and content), while the latter
does not: it passively collects content
created by citizens freely, without any initiation, stimulation or
moderation through government
postings
The initial preparation—content generation requirements for the
application of the passive crowd-
sourcing approach (= creation of domain and policy models) are
much higher than the ones of

The processing of the collected content has to undergo much
more sophisticated processing in the
case of the passive crowdsourcing approach than in the active
crowdsourcing one
And also the required ICT infrastructure for the active
crowdsourcing approach, and its application
model are more similar to the ones of the mainstream private
sector crowdsourcing than the passive
‘access to capabilities not held in-house’ and ‘capacity to
exploit knowledge and
skills of volunteers who might not otherwise contribute’.
However, while for our
active crowdsourcing approach the required ICT infrastructure
and its application
process model have some similarities with the ones of the
mainstream private sector
crowdsourcing (also important differences as well), our passive
crowdsourcing ap-
proach requires quite different forms of ICT infrastructure and
application process
model from the ones of the mainstream crowdsourcing. The
similarities and differ-
ences between the two proposed approaches are summarized
below in Table 12.1.
However, it should be noted that these two approaches are not
mutually exclusive,
but can be combined: the results of passive crowdsourcing can
be used for guiding
active crowdsourcing on the most important of the identified
issues and problems,
or even for organizing relevant discussions in government e-

consultation spaces.
[email protected]
From a first evaluation we have conducted for the active
based on pilot applications (see Ferro et al. 2013; Charalabidis
et al. 2014a), it has
been concluded that it constitutes a time and cost efficient
mechanism of reaching
wide and diverse audiences, and stimulating and motivating
them to think about
social problems and public policies under formulation, and
provide relevant infor-
mation, knowledge, ideas and opinions. Furthermore, it enables
identifying the main
issues perceived by citizens with respect to a particular social
problem or domain
of government activity, and collecting from them interesting
ideas on possible so-
lutions and directions of government activity. However, our
pilot applications have
shown that the above information generated from such multiple
social media crowd-
sourcing might be not be at the level of depth and detail
required by government
agencies. So in order to achieve a higher level of detail, and
more discussion depth
in general, a series of such multiple social media consultations
might be required,
each of them focused on particular subtopics and/or
participants. Another risk of this

active crowdsourcing approach is that it can lead to
unproductive discussions among
like-minded individuals belonging to the network of the
government policy maker
who initiated the consultation; such discussions are
characterized by low diversity of
opinions and perspectives, low productivity of knowledge and
ideas, and in general
limited creativity. Therefore, for the effective application of
this crowdsourcing ap-
proach it is of critical importance to build large and diverse
networks for these social
media consultations; for his purpose, we can combine networks
of several govern-
ment agencies, and also politicians, preferably from different
political parties and
orientations, and also invite additional interested and
knowledgeable individuals and
civil society organizations. Our passive crowdsourcing approach
is currently under
evaluation based on pilot applications.
The research presented in this chapter has interesting
implications for research and
practice. It opens up new directions of multidisciplinary
research concerning the ap-
plication of crowdsourcing ideas in government, taking into
account its special needs
and specificities, and also for the development of advanced ICT
infrastructures for
this purpose, and appropriate application process models. With
respect to govern-
ment practice, it provides to government agencies advanced,
efficient and effective
methods and ICT tools, in order to conduct ‘citizen sourcing’,
and collect useful

information, knowledge, ideas and opinions from citizen, and
the society in general,
so that it can finally design better, more socially rooted,
balanced and realistic public
policies for addressing the growing problems of modern
societies. Such tools can be
for government policy makers valuable ‘sensors’, allowing the
early identification
of new problems, needs, ideas and trends in the society, so that
appropriate policy
responses can be developed. It is important that such
approaches are gradually intro-
duced and integrated in the policy formulation processes and
practices, which can
lead to a significant ‘renewal’ of them.
Further research is required concerning the multidimensional
evaluation of the
two proposed government crowdsourcing methodologies,
through various ‘real-life’
applications (aiming at conducting crowdsourcing for various
types of problems and
public policies), and using various theoretical foundations and
lenses from multiple
[email protected]
disciplines. Also, it would be interesting to conduct research
towards the develop-
ment of contest oriented government crowdsourcing
methodologies, which include
definition of a more specific task to be performed, competition

among participants
and monetary or other types of rewards.
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[email protected]

Chapter 13
Management of Complex Systems: Toward
Agent-Based Gaming for Policy
Wander Jager and Gerben van der Vegt
Abstract In this chapter, we discuss the implications of
complexities in societal sys-
tems for management. After discussing some essential features
of complex systems,
we discuss the current focus of managers and management
theory on prediction and
the problems arising from this perspective. A short overview is
given of the leadership
and management literature, identifying what information is
lacking concerning the
management of complex systems. Next agent-based gaming,
which allows for model-
ing a virtual and autonomous population in a computer-game
setting, is introduced as
a tool to explore the possibilities to manage complex systems.
The chapter concludes
with a research agenda for management and leadership in
complex systems.
13.1 Introduction
The Dexia bank run, which started with a tweet, and Project X
Haren, that started
with an open invitation on Facebook, demonstrate that social
interactions may give
rise to developments that spin out of control. In many different
areas, managers in
both the public and private sectors have to deal with the
management of such com-
plex behaving systems, e.g., the transition in the energy system,

the development
toward sustainability of our society, the developments in our
health care system and
the robustness of our financial–economic system, to name a
few. Complexity the-
ory applied to social systems contributes to our understanding
of the mechanisms
driving the sometimes turbulent developments in such social
systems. It explains
how the interactions between many individual agents may result
in sometimes sur-
prising processes of self-organization. In Chap. 4, Jager and
Edmonds explained
the principles of social complexity in more detail. A relevant
contribution of the so-
cial complexity perspective is that it explains under what
conditions a social system
is rather predictable, and under what conditions it may start
behaving turbulently,
W. Jager (�)
Groningen Center of Social Complexity Studies, University of
Groningen, Groningen,
The Netherlands
G. van der Vegt
Faculty of Economics and Business, University of Groningen,
Groningen, The Netherlands
10.1007/978-3-319-12784-2_13
[email protected]

292 W. Jager and G. van der Vegt
making prediction in a classical sense impossible. For example,
the car market has
been a relatively predictable market for many years. For many
brands and models,
estimations of sales were being made that often lived up to their
expectations. How-
ever, the introduction of hybrid and electric cars in the existing
market resulted in
turbulences. One example would be that in 2013 virtually all
produced Mitsubishi
Outlander PHEV (plug-in hybrid electric vehicle) models were
shipped to the Nether-
lands due to a beneficial financial regime in that country. In
2014, the sales of this
model dropped significantly1. It can be imagined that with the
introduction of newer,
more radical designs, such as the Google driverless car and new
systems of car-
sharing that utilize web based sharing tools new uncertainties
are introduced in the
car-market that may give rise to large turbulences and
unpredictability in the market.
In a way this reflects the uncertainties of a century ago when
steam and gasoline
were two viable and competing sources of propulsion.
Besides technological aspects, social aspects are also critical in
the success or
failure of introducing a new product or technology. Uncertainty,
social norms, the
spreading of rumors through social networks, and the behavior
and opinions of role

models all have significant effects on the success or failure and
contribute to the
turbulence during the introduction of new technology. Although
social mechanisms
underlying social complex phenomena have been identified in
many social–scientific
studies, the management of developments in turbulent systems
remains problematic.
At the same time, the more social interaction takes place in a
system, which is usually
the case with the introduction of radical new technology, the
more turbulently it can
behave, and the more important effective management of the
system becomes. Yet
little is known about the effective management of complex
systems’ behavior. It is
precisely in such turbulent situations where good management
can result in favorable
outcomes. However, bad management may result in disasters
hitting the news, such
as failed evacuation plans, civil war, or power shortages. Hence,
the question—if we
can develop a tool to identify managerial leadership styles that
help better manage
complex social systems—seems a highly relevant one.
13.2 Simulating Social Complex Phenomena
In improving our understanding of how social complex systems
can be managed, the
first step is getting a better understanding of how interactions
between people may
give rise to social complex phenomena. Due to the large scale
of many social systems
and the often unique events that happen, experimentation with
real populations is not

possible. However, it is possible to experiment with computer
simulated populations
of artificial people, through so-called agent-based modeling
(ABM, see e.g., Chap. 4).
This methodology has proven to be a suitable approach in
exploring the dynamics of
social complex systems and is gaining momentum in many
disciplines (e.g., Gilbert
1 In December 2013 4988 Mitsubishi Outlander PHEV were
registered, against 83 in January 2014
(Kane 2014).
[email protected]
Gaming for Policy 293
and Troitzsch 2005). In ABMs, agents are connected in a
network and follow simple
rules that are programmed at the individual level. In a model to
investigate a particular
social system, these rules can be derived from a more general
behavioral theory as
well as specific data originating from the field.
An example is the model of Van Eck et al. (2011), where the
role of opinion
leaders on the diffusion of a new product was explored. In an
empirical study, they
found that opinion leaders had a more central network position,
possess more ac-
curate knowledge about a product, and tend to be less
susceptible to norms and

more innovative. Implementing this in an agent-based model
opened the possibility
of introducing new products in a simulated market and
comparing the effects of the
presence versus absence of such opinion leaders in the system.
The simulation experi-
ments demonstrated that opinion leaders increase the speed of
the information stream
and the adoption process itself. Furthermore, they increase the
maximum adoption
percentage. The simulation model thus suggests that targeting
these opinion leaders
might be a viable marketing strategy.
ABM makes it possible to conduct many experiments and
explore the conditions
under which social systems start behaving turbulent, which
implies that the social
system gets into a state where fast and unforeseen developments
take place, such
as in fashion dynamics or social conflicts. ABM also allows for
exploring how
individuals change their behavior over time due to social
interactions and allows
for the identification of the key individuals in a social network.
Interestingly, it
opens the possibility to explore how certain management
strategies would perform
in different conditions of turbulence.
13.3 Managing Social Complex Phenomena
From a social scientific experimental perspective, one would
suggest running exper-
imental designs as to identify the effects of different strategies.
Comparing different

interventions in a simulation model would yield information on
what interventions
are most effective. However, whereas empirically validated
ABMs clearly provide a
relevant perspective on identifying the social complexities in
many social systems,
their application in experimentally testing the effects of
operational management
strategies remains problematic in turbulent conditions. Two key
reasons cause that
experimentation with predefined leadership interventions are
problematic.
First, in a turbulent system the effects of a specific management
strategy may
vary considerably. This is because in one simulation run such a
strategy may be
on spot with the developments that take place in the simulation,
whereas in another
simulation run the same timing of that specific strategy may be
very inconvenient. As
a result, specific management strategies may have different
consequences, making
it difficult to draw conclusions about their effectiveness.
Relaxing the experimental rigor of adopting certain management
strategies at an
identical moment in the simulation would allow for tracking the
developments in the
simulation and adopting the strategy at a moment that seems
most effective. This
[email protected]

implies that the experimentation bears an adaptive character,
responding with the
manipulation on developments in the simulation run as they
evolve. However, here we
run into the problem that in understanding effective
management of a system it is not
sufficient to study specific leadership behaviors in isolation.
Rather, the management
of social complex phenomena often implies that a sequence of
behaviors takes place,
and that (unforeseen) responses of other stakeholders have to be
addressed as well.
In regular experimental designs as used in the social sciences,
this would result in an
exponential growth of possible strategies to be tested. For
example, given a simulated
simple market with ten competing products, a manager trying to
stimulate the sales
of an “innovative green” product may decide on pricing, quality
of the product, and
type of marketing. Each of these elements already implies a
choice from a wide
array of possibilities. In selecting one possibility, the other
product managers have
an equal number of possibilities that even interact with each
other. Many decision-
making contexts are much more complex than this example, as
they involve many
stakeholders with different and sometimes conflicting goals,
different valuations, and
perspectives on outcomes, and different responsibilities and
influencing power. And
realizing that many complex social processes may span longer
periods of time (e.g.,

years), it is clear that testing the effect of particular strategies
in managing turbulent
behaving social systems is not feasible in ABMs.
However, on a more aggregate level we may identify consistent
patterns in man-
agerial behavior, such as being adaptive to change, collecting
information, and having
a long-term perspective. These can be understood as a
management or leadership
style, and we hypothesize that these styles are far less divergent
than operational man-
agement strategies, so that their effectiveness may be observed
from the interaction
between managers and a complex social system.
13.4 Leadership and Management in Complex Systems
The dominant paradigm in leadership research has been to
examine the relation-
ships between leadership styles, such as task- and relationship-
oriented behavior
(Bass 1990), and the outcomes of these behaviors, including
follower attitudes (sat-
isfaction, commitment, trust), behaviors (extra effort,
cooperation, organizational
citizenship behavior), and performance or unit level outcomes,
like group cohesion,
collective efficacy, and unit performance. Within this paradigm,
the vast majority of
studies has examined such relationships at a single period in
time and has ignored the
dynamic character of most of these relationships. In the field of
leadership studies,
this state of affairs is unfortunate because: (1) the effectiveness
of specific leader-

ship behaviors may depend on their timing, and (2) because
leadership essentially
represents a dynamic influence process between leader and
followers that unfolds
over time (Uhl-Bien and Marion 2009). Scholars have,
therefore, recently started to
develop theoretical frameworks that address these shortcomings,
and now focused
on team leadership as a dynamic process necessitating adaptive
changes in leader
behavior.
[email protected]
Kozlowski et al. (2008) proposed that the effects of certain
leadership styles and
actions may depend on their timing. They developed a team
leadership framework
that portrays team development as a cyclical and dynamic
process, which requires
leaders to adapt their leadership style to the various phases of
team development
and to the different adaptation needs of the team at each phase.
This means that
certain leadership actions and interventions may lead to
desirable outcomes at a
certain phase of the relationship but not in another phase. The
theoretical framework
proposes that for leaders to be adaptive, they must be aware of
the key contingencies
that necessitate shifts in leadership behavior, and they must

possess the underlying
skills needed to help the team resolve challenges. In these
models, the leader has two
major responsibilities or functions.
One leadership function is instructional and regulatory in
nature. By responding to
variations in team tasks by goal setting, performance
monitoring, diagnosis, and feed-
back, the leader may help or stimulate team members to develop
the knowledge and
skills that contribute to team effectiveness. Leadership
behaviors associated with this
leadership function are transactional, structure-initiating,
monitoring, authoritative,
and directive leadership.
The second leadership function is developmental. As teams
acquire the necessary
knowledge and skills, the leader role shifts to help the team
develop progressively
more complex skills and capabilities (Kozlowski et al. 2008).
Leadership behav-
iors associated with this leadership function are
transformational, consideration,
coaching, empowerment, facilitative, and participative
leadership. Over time, this
dual-pronged leadership process is hypothesized to yield team-
level regulation and
adaptive teams.
Other scholars have developed theoretical frameworks that
explicitly conceptual-
ize leadership as a dynamic influence process between leaders
and followers. They
suggest that leadership has to be understood as a reciprocal

interaction process be-
tween leaders and followers, taking into consideration the
characteristics, actions,
and reactions of both sides (Collinson 2005). Uhl-Bien et al.
(2007), for example,
have proposed complexity leadership theory (CLT) as a model
of team leadership
consistent with this line of thinking. According to CLT, the
question is how leaders
might enable and coordinate the dynamic interactions between
interdependent in-
dividuals without suppressing their adaptive and creative
capacity. It distinguishes
between three leadership functions that are important in this
regard: adaptive, ad-
ministrative, and enabling leadership. Adaptive leadership
refers to the creative and
learning actions that emerge from the interactions between team
members; it is an
informal, emergent dynamic that occurs among interacting
individuals. Adminis-
trative leadership refers to the actions of persons in formal
managerial roles who
plan and coordinate activities to accomplish organizationally
prescribed outcomes
in an efficient and effective manner; it is about structuring
tasks, planning, building
a vision, allocating resources, and managing crises. Enabling
leadership is behav-
ior aimed at creating appropriate conditions to foster effective
adaptive leadership
in places where innovation and adaptability are needed, and
facilitating the flow
of knowledge and creativity from adaptive structures into
administrative structures.

[email protected]
Enabling leadership tailors the behaviors of administrative and
adaptive leadership
so that they can effectively function in tandem with one
another.
Although leadership scholars have acknowledged the dynamic
nature of leader-
ship and have started to develop models and frameworks to
organize and describe
the factors that are important, empirical research testing these
models is scarce. The
reason for the lack of research on dynamic leadership processes
is that it requires
a different approach than the traditional research methods
employed in leadership
research. Even multiwave studies are at best a series of time-
spaced sequential snap-
shots and do not capture the dynamic relationships between
inputs and outcomes over
time. Serious gaming may provide a suitable tool to enable real-
time observation of
leadership processes: longitudinal methods for the real-time
tracking of leadership
relationships and consequences as they evolve over time.
13.5 Serious Gaming
Serious gaming is well equipped as a tool to experiment with
and train people for
situations that are less easy to practice in reality (e.g., Lisk et

al. 2012). The flight
simulator is the most renowned serious game in this context,
offering the possibility
to develop and maintain the skills to deal with all kinds of
flying events, starting
with routine flights and ending with co-occurrences of rare
events that in reality
often lead to disaster. One of the main advantages in studying
and training behavior
is that the behavior of a person can be observed and tracked
over time, offering
precise and controlled measurements, and allowing for detailed
evaluations of the
behavior. Currently gaming is widely adopted in military
training, and increasingly
being used in testing, for example, the organization of
firefighting departments, new
product introductions (marketing), the optimal crowd streams in
cities and stadiums,
and managing traffic flows. Leaving the implementation of
strategies to real people
interacting with the model thus has the advantage of creating a
plausible managerial
environment, as real people manage the system.
Up till now, serious games that are being used to study and train
leadership and
management were based on deterministic models. The basic
principle here is that
the player is confronted with a choice, and depending on the
selection, a predefined
scenario path is picked. Whereas the multitude of choices
creates a large landscape
of possible routes through the game, the game is deterministic
by nature. This im-
plies that current games are not capable of capturing the social

complex phenomena
mentioned earlier that emerge as the result of many autonomous
interacting agents.
Yet for testing leadership and management in social complex
systems over time, it
is critical to include these social complexities in a gaming
setting. Whereas several
scientists within the ABM community identified gaming as a
tool to explore and fore-
cast developments in complex systems (e.g., Arai et al. 2006;
Guyot and Honiden
2006), it has not been used to study leadership and management
of social complex
systems. Considering that especially turbulent developments in
social systems can
[email protected]
be problematic and require effective leadership, and observing
that management sci-
ence lacks a suitable tool to study this, we propose to use agent-
based gaming as a
new tool to study leadership and management behavior of real
people in a controlled
simulated complex environment.
13.6 Agent-Based Games for Testing Leadership and
Management
To study the management of social complex phenomena we
propose developing

agent-based games that includes an autonomously behaving
artificial population.
Players will perform a management task in this game, allowing
us to experimentally
test under what system characteristics a specific management
strategy or leadership
style performs best. We hypothesize that in systems that are
more turbulent, an adap-
tive and people-oriented style will outperform conservative and
outcome-oriented
styles. More adaptivity prevents unwanted lock-ins to emerge,
and a people-oriented
style will translate in better relations, and thus performance in
the long run. However,
we also hypothesize that a long-term goal orientation is critical
in efficient leader-
ship/management. Just concentrating on adapting to short-term
developments may
cause that the long-term goals are neglected, which may also be
a sign of ineffective
management. Hence, our basic hypothesis is that effective
management of social
complex systems requires a strong adaptive capacity for
responding to short-term
developments combined with a clear long-term perspective of
the goals to reach. For
example, in stimulating the adoption of more sustainable
technologies, one should
be very alert on what is currently happening in the market, and
responding to threats
and identifying opportunities, but at the same time one should
have a clear vision of
how the successful diffusion of sustainable technology creates
future conditions for
further developments.

Currently, several practice-oriented agent-based simulation
models have been de-
veloped that are aimed to support policy making. A recent
inventory initiated by
Gilbert2 demonstrated that dozens of simulation models are
being used in practical
settings. In many instances, the contributions remain at a more
conceptual level,
providing the practitioners with a deeper understanding of the
complexities of the
system they are interacting with, and the implications for policy
making. If prac-
titioners actually interact with a model it usually requires close
guidance by the
modelers because of the expertise required to interact with the
interface.
It is our stand that the use of social simulation models, and their
application
in studies exploring managerial and leadership styles, is
necessary to work toward
interfaces that make it easier and more intuitive to interact with
the simulation tool.
In the gaming industry, many developments took place, and as a
result there is an
2 On 04-12-2013, Gilbert sent out an e-mail on the SIMSOC list
asking for examples of ABMs that
have actually been used to support policy decision making or
for other purposes “in the real world.”
[email protected]

abundance of games that create a very convincing environment
in which it is relatively
easy to operate, to learn what the behavioral options are, and
most importantly, to
create an experience of “flow” in the players that keeps them
motivated to continue
playing the game. Important here is that the behavior of
simulated agents and the
environment one interacts with provide a sufficiently realistic
experience.
Whereas for amusement games convincing graphics are essential
to create a flow,
and we emphasize the importance of developing high-quality
interfaces for agent-
based games, the applicability of appeal of these serious games
resides primarily in
the realism of the behavioral dynamics that are being simulated.
This implies that
the behaviors as displayed by the artificial agents should reflect
the behavior that
can be observed in reality. The consumat approach has
specifically been designed
to capture a number of main processes of human behavior and
has been used in a
variety of applications (see e.g., Jager 2000; Jager et al. 2000).
Figure 13.1 gives an
overview of the components in the consumat framework.
A simulated agent, when satisfied and certain, may continue
repeating itself many
times. Only if the satisfaction drops, and/or the uncertainty
rises, the consumat
may switch to using a different decisional strategy. If this
results in an increase in

satisfaction and the uncertainty is reduced, the agent may return
to a new habitual
behavior.
Agents differ concerning their uncertainty tolerance (when to
engage in social
processing) and aspiration level (when is an agent dissatisfied).
The agents also
differ concerning the relative importance of the needs that
determine their satisfaction
(individual preferences). As a consequence, an action directed
on a specific agent
may result in different responses depending on the type and
state of an agent. Agents
also have different abilities, such as financial means, which
determine if a behavior
is possible to perform.
The consumat approach has been used to guide the modeling of
different agent-
based models, such as fashion dynamics (Janssen and Jager
2001), transitions in
a society (Jager et al. 2000), sustainable life styles (Bravo et al.
2013), farmer
crop choice behavior (Mialhe et al. 2012; Speelman 2014), and
currently projects
are addressing the diffusion of light-emitting diode (LED)
household lighting
(Schoenmacker 2014) and electric cars (Jager et al. 2014).
A key attribute of the artificial population in our proposed
agent-based gaming
approach is that the agents are linked in a social network, where
some agents have
more links and influence than other agents. This implies that
experiences of agents

are being communicated through the social network as
information exchange and
normative influences, and may give rise to the emergence of
precisely those social
complexities and turbulences where we want to study
management and leadership.
Depending on the requirements of the topic to be modeled, one
can implement static
or dynamic networks (see e.g., Squazzoni et al. 2013). For
example, in developing
a game directed at the diffusion of new energy technology one
could use a static
network with a number of actors having many contacts,
representing influential
opinion leaders. However, in developing a model to experiment
with the emergence
of extremism it is essential to include dynamic networks that
allow for clusters of
agents that separate from mainstream society.
[email protected]
optimising
inquiring
repetition
imitation
Fig. 13.1 How different factors influence one another and result

in behavior (opportunity consump-
tion), which aggregates over all simulated consumers and
results in macrolevel outcomes that set the
conditions for a next behavioral cycle. In the consumat
approach, the agents have existence needs
(e.g., food, income), social needs (group belongingness and
status), and identity needs (personal
preferences, taste). To select a behavior an agent can employ
four different types of decisional
strategies, depending on its satisfaction and uncertainty. A
satisfied and certain agent will repeat its
previous demand, which captures habitual behavior/routine
maintenance. A satisfied but uncertain
agent will imitate the demand of a similar other in its network,
which reflects normative compli-
ance (fashion). A dissatisfied and certain agent will evaluate all
possible demands and select the
one providing the best outcomes (optimizing). And finally, a
dissatisfied and uncertain agent will
inquire the demands other agents had and copy this demand if
the outcomes are expected to be
better (social learning).
13.7 Single and Multiplayer Settings
A player confronted with an agent-based game using this
approach will experience
realistic behavioral dynamics. First, because the consequences
and impacts of policy
decisions will be communicated through the network of agents,
which may elicit
far fetching effects, treating one person wrong may elicit a
cascade of negative
[email protected]

information, whereas convincing an opinion leader of the
benefits of, e.g., an energy-
efficient device may stimulate the social diffusion of this
device. Players thus learn
how their policies may spread through the social system and
may develop strategies
that use these social forces. This also implies that a player
becomes aware of the fact
that his/her policy action may spread through society and hence
may have long-term
effects on other agents beyond those directly affected in the
here and now.
A second valuable outcome of using such a model is that the
player not only
observes the impact of policy measures in the diffusion of
products of the changing
of behavior but also sees what is happening with the level of
need satisfaction of
different groups of agents. Hence, a deeper insight is generated
concerning the effects
of policy on well-being and it can be observed if well-being
drops, so measures can
be taken before a decrease in well-being results in negative
behavior.
Up till now, we mentioned the players as an individual
interacting with the sim-
ulation model. And indeed it is very well possible to let an
individual interact with
the simulation tool, both in training, teaching, and
experimentation settings. How-

ever, many social complex systems are being managed by
multiple stakeholders. In
developing agent-based models several researchers already
include different stake-
holders in the model building stages and also in the playing of
the game, which were
often cardboard games (e.g., Barreteau et al. 2001). In the
context of gaming, it
is already a common practice to use multiplayer settings. An
example is World of
Warcraft (WoW), an Internet-based war game that started in
1994 as a simple com-
puter game and transformed into a massively multiplayer online
role-playing game
(MMORPG), where over 7 million people are playing. A very
relevant attribute of
WoW that makes it very appealing to play is the possibility to
join a team of people
in accomplishing a mission. Here similarities in interest and
complementary in skills
may lead to effective teams. These teams are often in
competition with other teams
and often conflicts arise, which are being settled in fights.
In managing social complex systems often the same social
structures can be
observed as in WoW and similar games. Decisions by a firm or
organization are
made by groups of people having different and often
complementary competences,
and they often have other and sometimes conflicting interests
than other companies
and organizations. For agent-based gaming, a same venue can
be created as in such
games. Instead of single players, a team may be composed, and
it can be studied what

team compositions function well in managing turbulent
situations in social complex
systems. It depends on the issue at stake if multiple teams are a
relevant extension
of the agent-based game.
An example of a single group agent-based game would be a
crowd managing game.
Here an artificial crowd can be modeled (see e.g., Wijermans et
al. 2013), which is
gathering at a specific environment, such as a city center or a
stadium. The crowd
can be given a general motivation, e.g., demonstrating,
watching a performance, of
attending a soccer match, and a distribution can be given of the
behavioral tendencies
of the simulated agents, e.g., in terms of aggression motivation.
The players can be
a police task force that is composed of people in the field and a
command level.
At the field level, a number of players will represent law-
enforcement in the field.
These players will move in the environment and have a
perception of the local
[email protected]
environment that is limited. They can interact with the
simulated civilians. Here
the agent-based architecture allows for a realistic response of
the simulated people,

such as obeying orders, discussing and changing of arousal, or
engaging in aggressive
behavior. Essential is that the behavior of an artificial agent
will affect the behavior of
other agents as well, which may lead to cascade effects. For
example, if a policeman
decides to hit an agent, nearby agents will experience a higher
arousal upon observing
a fellow agent being hit, and will probably have a higher
tendency to either flee or
engage in aggressive behavior. In the game policemen can also
communicate with
each other using a radio system (which can be blurred or fail in
game scenarios).
At the command level, the player (which can be a group of
people) has a generic
overview of the festival terrain, but they rely on the reports of
the field-level players
for the interpretation of what is happening. The command level
can give orders to
the field-level concerning their position and actions. Hence,
they may decide to have
law enforcement as couples in the crowd, or cluster them in a
line to block an area.
Also, they can give orders to the field-level players, such as
arresting a civilian. Such
a single team game can be played to study optimal group
compositions, learn about
how to manage turbulent situations in groups and as a practice
tool. Also, such a
game can be played in a specific location on a confined time
slot.
A multiple agent-based group game can target social complex
systems where dif-
ferent groups are interacting with the agent population, but are

having different and
sometimes conflicting interests. An example would be a team
that is trying to stim-
ulate the diffusion of electric cars, which implies experts on
technical possibilities,
economical viability, infrastructural development, and
marketing and communi-
cation. Experts from these different domains have to develop
and manage a joint
proposition toward segments in an artificial consumer base. At
the same time, com-
parable teams can work on fuel-propelled cars, and thus
compete with the electric
team for market share. Using a realistic artificial population
that is based on empirical
data will provide a simulated environment where these different
teams can operate.
Because in these settings is becomes impractical to meet in a
specific location on
a confined time slot, it seems more realistic to implement
multigroup agent-based
games as Internet games where different teams can operate quite
independently, but
are being confronted with the implications of each other’s
actions. In the multigroup
game teams also have to identify the strategy of competitors,
and adapt to that, which
adds to the complexity of the game.
13.8 Experimentation with Management
The proposed game setting allows for measuring different task-
and relationship-
oriented behaviors of the players. Critical factors to track are
adaptive speed,
information collection, orientation on agent behavior,

orientation on competitor be-
havior, and overall performance in terms of reaching predefined
goals and agent
satisfaction. Experimentation will be possible to show the
relation between the man-
agerial/leadership style of the player(s), and its success in
turbulent settings. It will
[email protected]
also be possible as a post measurement to ask the players for
their understanding of
the dynamics of the system using the methodology of Endsley
(1995) and Edwards
et al. (2006), which focuses on measuring situation awareness in
complex situations
and the identification of relations. This will contribute also to a
more qualitative
indication of the extent to which they understand the complex
nature of the game
and the best styles and strategies within social complex
systems. Experiments thus
are capable of showing: (1) what leadership and managerial
styles perform best in
conditions of varying complexity (single vs. multi player) and
the type of popula-
tion (importance of social need), (2) how task- and relationship-
oriented behavior,
adaptive speed, information collection, agent, and competitor
orientation relate to
performance in management under different conditions of
complexity, and (3) what

leadership and managerial styles are more effective in
developing an understanding
of the system.
13.9 Conclusions and Discussion
In this chapter, we discussed how agent-based gaming may
provide a methodology
to study effective management and leadership on social complex
systems. Because
the major challenges in our society often deal with behavior
change of large pop-
ulations and social complex processes, turbulent developments
are common. This
proposed direction of development of methodology may
contribute to a better under-
standing of how our species can manage common behavior, both
among ourselves
and in interaction with our habitat. A critical contribution will
be the formalization
of some main principles of human behavior in an integrated
model, which subse-
quently allows for interaction with an artificial but realistically
behaving population.
This will facilitate the transfer of behavioral insights into the
realm of policy testing
and will contribute to the development of the required
knowledge on how to manage
social complex behaving systems. Currently, the respective
fields of social simula-
tion, management science, and gaming technology seem
developed far enough to
make such an endeavor possible, but, considering agent-based
gaming projects will
require the interaction between many different actors, also, here
turbulences can be

expected.
Acknowledgment This chapter has been written in the context of
the eGovPoliNet project. More
information can be found on http://www.policy-community.eu/
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Chapter 14
The Role of Microsimulation in the Development
of Public Policy
Roy Lay-Yee and Gerry Cotterell
Abstract This chapter seeks to provide a brief introduction to
the method of mi-

crosimulation and its utility for the development of public
policy. Since the inception
of microsimulation in the 1950s, its use for policy purposes has
extended from the
economic to other domains as data availability and
technological advances have
burgeoned. There has also been growing demand in recent times
to address increas-
ingly complex policy issues that require new approaches.
Microsimulation focuses
on modelling individual units and the micro-level processes that
affect their devel-
opment, be they people’s lives or other trajectories. It comes in
various types, for
example along the dimensions of arithmetical or behavioural,
and static or dynamic.
It has its own distinctive model-building process, which relies
on empirical data and
derived parameters with an insertion of chance to simulate
realistic distributions.
The particular utility of microsimulation for policy development
lies in its ability to
combine multiple sources of information in a single
contextualised model to answer
‘what if’ questions on complex social phenomena and issues.
14.1 Introduction
This chapter provides an introduction to the method of
microsimulation and its role
in the development of public policy. The chapter firstly
provides a brief history of
microsimulation, which tracks the development of this method
since its inception
in the 1950s. This is followed by an explanation of
microsimulation itself, the dif-

ferences between its various types, and the model-building
procedure. We end with
assessing the utility of microsimulation for policy development,
and setting out its
strengths and weaknesses as a method for this purpose. We
describe a case study
taken from a dynamic microsimulation model of the early life
course developed in
New Zealand. In the Appendix, we provide selected examples of
a number of other
existing microsimulation models in use from around the world.
R. Lay-Yee (�) · G. Cotterell
Centre of Methods and Policy Application in the Social
Sciences (COMPASS Research Centre),
University of Auckland, Private Bag 92019, 1142 Auckland,
New Zealand
10.1007/978-3-319-12784-2_14
[email protected]
306 R. Lay-Yee and G. Cotterell
14.2 A Brief History
The academic literature generally acknowledges the pioneering
work of Guy Orcutt
in his 1957 paper, ‘A new type of socio-economic system’, as
providing the founda-
tion for the field of microsimulation. In his paper, Orcutt

posited that microsimulation
models consisting of ‘various sorts of interacting units which
receive inputs and gen-
erate outputs’ (Orcutt 1957, p. 117) could be used to investigate
‘what would happen
given specified external conditions and governmental actions’
(Orcutt 1957, p. 122).
Even today, Orcutt’s prescient ideas still provide a relevant
blueprint for modelling.
However, the potential of this new approach was slow to
materialise because of
limitations in computing power and the lack of suitable data.
As these respective limitations were gradually overcome with
the rise of tech-
nology and data collection, the use of microsimulation
increased. In the 1970s,
microsimulation models were being used in the USA to assist
the development of
social policy (Citro and Hanusek 1991), and by 1990
‘microsimulation had be-
come widespread enough in the domain of tax and transfer
analysis’ (Anderson and
Hicks 2011, p. 1). Merz identified six major microsimulation
projects in the USA
and Europe in the 1980s, growing to 18 in the 1970s and then to
33 in the 1980s
(Merz 1991). Further rapid advances in computing power, along
with information
sharing, served to increase the use of microsimulation
modelling, and in 2005, the
International Microsimulation Association was formed
(Anderson and Hicks 2011,
p. 1; http://www.microsimulation.org/). Microsimulation models
have historically
been employed for tax and transfer policy purposes (Harding

1996; Gupta and Kapur
2000; Harding and Gupta 2007), but their use has extended to
other social domains
(Gupta and Harding 2007). In recent times, there has been
growing demand for new
approaches to address increasingly complex policy issues.
However, even today,
despite the advances in technology and data availability,
microsimulation projects
typically require large investments of time and resources.
14.3 What Is Microsimulation?
As a form of social simulation, the microsimulation variant can
be seen to be lo-
cated between and contrasted with system dynamics—with a
macro focus and low
complexity—on one side, and agent-based modelling—with a
behavioural focus of-
ten relying on notional rules—on the other (Gilbert and
Troitzsch 2005). Based on
the use of empirical individual-level data, it can potentially
account for complexity,
heterogeneity, and change (Orcutt 1957; Spielauer 2011).
The core of microsimulation has been defined as ‘a means of
modelling real life
events by simulating the actions of the individual units that
make up the system where
the events occur’ (Brown and Harding 2002), and as ‘computer-
simulation of a soci-
ety in which the population is represented by a large sample of
its individual members
and their behaviours’ (Spielauer 2011). This has been broadened
to encompass its
role in policy so that ‘microsimulation models are computer

programs that simulate
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Policy 307
aggregate and distributional effects of a policy, by
implementing the provisions of
the policy on a representative sample of individuals and
families, and then summing
up the results across individual units using population weights’
(Martini and Triv-
ellato 1997, p. 84). Microsimulation models are based on
individual-level data, or
microdata relating to the characteristics and behaviours of
individuals. The method
endeavours to approximate an experiment whereby the potential
range of outcomes
is ‘simulated or imputed on the basis of a set of assumptions
about the behavioural
reactions of individuals following changes. . . brought about by
the introduction of a
policy’ (Zucchelli et al. 2012, p. 3).
The microsimulation method relies on data from the real world
to create an artifi-
cial one that mimics the original, but upon which virtual
experiments can be carried
out (Gilbert and Troitzsch 2005). Microsimulation operates at
the level of individual
units, for example people, each possessing a set of associated
attributes as a start-
ing point. A set of rules, for example equations derived from

statistical analysis of
(often multiple) survey data sets, is then applied in a stochastic
manner to each and
every individual in the starting sample to simulate changes in
state or behaviour.
Such a model (Rutter et al. 2011; Spielauer 2007) can
essentially generate a set of
diverse synthetic biographies for the base sample of individuals.
Modifications of
influential factors can then be carried out to test hypothetical
‘what if’ scenarios on
a key outcome of policy interest (Davis et al. 2010). Simulation
at the micro level
enables the assessment of the impact of potential policy changes
for subgroups as
well as aggregates of the population. Microsimulation has the
capability to integrate,
and accommodate the manipulation of, the effects of variables
across multiple model
equations (often derived from multiple data sources) in a single
simulation run. Thus,
each otherwise separate equation is given its context and
influence among the other
equations, representing a system of interdependent social
processes.
Last but not least, the human and technology requirements for
implementing
microsimulation are demanding, especially in relation to
forming a team with multi-
disciplinary skills, finding relevant data sources (for inputs),
and deploying computer
hardware and software (for implementation and outputs)
(Cassells et al. 2006).
In summary, the essential characteristics of microsimulation

are: the use of em-
pirical micro-level data both to form a starting sample and from
which to derive
equations to drive the simulation forward; the integration of
data and equations from
disparate sources; the use of a stochastic simulation process that
generates both
aggregate and distributional effects; and the facility to be able
to manipulate the
underpinning model to test counterfactuals or policy scenarios.
14.4 Types of Microsimulation
There exists a range of types of microsimulation for policy
purposes, which can
be categorised on various dimensions. Zucchelli et al. propose a
taxonomy that
comprises ‘arithmetical versus behavioural models and static
versus dynamic models’
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(Zucchelli et al. 2012, p. 6). The primary features of each of
these types are outlined
below:
1. Arithmetical microsimulation models are typically run to
estimate distributional
and budgetary change occurring as a response to changes in
taxes, benefits, and
wages. These models ignore the behavioural responses by
individuals to the pol-

icy change being examined. For example, in the case of a model
simulating tax
and benefit changes, the simulation mimics adjustments to real
disposable income
following a policy change, and assumes that the behaviour of
the individual does
not change (Bourguignon and Spadro 2006). Such models are
useful in highlight-
ing the impact of reforms, and in ascertaining those who benefit
and those who
lose from the modelled reforms, along with their characteristics
(Zucchelli et al.
2012).
2. In contrast, behavioural microsimulation takes into account
changes in the be-
haviour of the individuals in the simulation in response to the
policy parameters
modified. For example, changes to tax-benefit policy introduced
into a model are
considered to impact on the options favouring the individual,
and may lead to a
change in the amount of labour they choose to supply (Zucchelli
et al. 2012, p. 8).
3. Static simulation models restrict their analysis to a single
point in time or a set
of points in time, without modelling the processes that drive the
changes over
time (Spielauer 2011, p. 2). Thus static models evaluate the
putative state of each
individual under a changed set of policy rules (Brown and
Harding 2002). Static
models are typically used to model changes in taxes and social
security benefits.
A data-based static microsimulation model consists of two

parts: (1) a baseline
database—containing information on individual or
family/household units, par-
ticularly sociodemographic characteristics and economic
information that bears a
relationship with a set of policies; and (2) a set of accounting
rules—computer lan-
guage instructions that produce for each unit the results of, for
example, alternative
tax or transfer policies and procedures (Martini and Trivellato
1997).
4. ‘Dynamically ageing microsimulation models, on the other
hand, involve up-
dating each attribute for each micro-unit for each time interval’
(Brown and
Harding 2002, p. 3.). Dynamic microsimulation models are
constructed using
either stochastic or deterministic algorithms. If the former, then
the model is
‘based on conditional probabilities that certain economic or
social conditions or
processes will exist or occur’ (Brown and Harding 2002, p. 3.).
If the latter, it is
rule-based so that a condition will trigger a state or event.
Spielauer argues that
behaviours studied in dynamic microsimulation are of two
types: ‘behaviour that
produces events that take place over time such as demographic
events, for ex-
ample, marriage, divorce, death, and economic events such as
leaving the labour
force’, and ‘behaviour producing feedback reactions of
individuals and/or fami-
lies to changes in external circumstances, notably to changes in
public policies’

(Spielauer 2011, p. 4).
Distinctions can be made among dynamic microsimulation
models themselves
depending on the nature of the starting sample of individuals
and the process by
which it is aged (Spielauer 2011).
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Policy 309
(a) Actual or synthetic starting sample, i.e. the starting sample
comes from an
actual survey, or it is synthesised from various data sources;
(b) Open or closed population, i.e. other individuals can be
introduced to the
starting sample as the simulation progresses, or membership of
the sample
is fixed;
(c) Cohort or population, i.e. a cross-sectional sample that is
aged and even-
tually dies out, or a sample of the population whose
representativeness is
maintained over time;
(d) Discrete time or continuous time, i.e. individuals’
characteristics are sim-
ulated at fixed time intervals, for example yearly, or their
characteristics
are assigned and can change at any time;

(e) Case-based or time-based, i.e. each individual’s life course
is simu-
lated independently from beginning to end, or all individuals
have their
characteristics updated at the same time points.
The taxonomy of Zucchelli et al. (2012) is useful to
policymakers in choosing the
type of microsimulation model to support their decisions. For
example, if they wanted
to take into account the impact of not only a policy change but
also changes in
individual behaviour due to that policy change, then the model
of choice would
be the ‘behavioural’ type rather than the more straightforward
‘arithmetical’ type.
Similarly, if the policy interest was in changes over time, then
the ‘dynamic’ type
would be more appropriate than the ‘static’ type.
14.5 The Process of Microsimulation
The process of building a microsimulation model follows
several linked stages and
components (Caro et al. 2012; Cassells et al. 2006; Zaidi and
Rake 2001). This
process is not strictly sequential—as presented for exposition—
but branched and
iterative where steps may be overlapping or repeated until the
model is functioning
as required. A case study is described in Sect. 14.8.
1. Conceptualisation—considerations
(a) Setting clear objectives delineating the focus and scope of
the model, e.g.

what processes and variables to include;
(b) Deciding on the level of simplicity or complexity of the
model;
(c) Ensuring that the framework accounts for core processes of
the phe-
nomenon being modelled so that the simulation sufficiently
matches
reality;
(d) Recognising that proper configuring of model structure and
pathways is
critical to the success of the model’s application for projection
or scenario
testing;
(e) Balancing the dual goals of explanation and prediction of
the phenomenon
of interest.
[email protected]
2. Computing platform—considerations (Percival 2007; Scott
2003)
(a) Following good IT practice, e.g. best-practice guidelines and
standards,
software selection, testing regime, and computing facilities;
(b) Procuring adequate and sufficient resources to sustain a
project;
(c) Building a developer and user platform that is fit for
purpose, reliable,

and well supported, and that ideally can be maintained and
repurposed for
further studies.
3. Data integration—considerations
(a) Identifying and accessing various data sources relevant to
the phenomenon
of interest, i.e. quantitative, qualitative, findings from other
studies, or
guesstimates;
(b) Generating data to form the starting file (initial conditions)
from which the
simulation proceeds;
(c) Generating parameters (e.g. from data analyses) to inform
the rules that
drive the simulation;
(d) Combining both data and parameters into a cohesive model,
and as inputs
to the simulation engine.
4. Implementation—steps
(a) Initialisation, presimulation—setting up model
specifications, the starting
file, and procedures;
(b) Simulation—invokes a stochastic process—in which a
(transition) proba-
bility is compared to a number from a random draw to assign a
state or
behaviour to a unit or individual;

(c) Validation—comparing simulation results against both
internal and external
benchmarks;
(d) Calibration (before simulation) or alignment (after
simulation) of simula-
tion results to benchmarks.
5. Application
(a) Projection of the current state into the future assuming that
inherent features
of the status quo remain the same;
(b) Simulating the impact of potential policy change or
intervention over time,
i.e. posing and testing scenarios, by altering relevant features of
the model
and observing flow-on effects.
14.6 Is Microsimulation Useful for Policy Development?
Complex policy issues require approaches that enable research
synthesis and use
systems thinking (Grimshaw et al. 2012; Lobb and Colditz
2013; Milne et al. 2014).
Large sums of money are spent on public policy sometimes
without a clear under-
standing of how they will impact on or benefit different groups
in the population.
Anchored in empirical data, microsimulation modelling has the
potential to repre-
sent systems and processes in various social domains and to test
their functioning
for policy purposes (Anderson and Hicks 2011; Zaidi et al.

2009). By attempting to
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Policy 311
mimic an experiment, it is a relatively cost-effective means of
testing policy options
to ensure defective polices are not implemented (Mitton et al.
2000). Microsimula-
tion has been primarily used for investigating the effects of tax
and benefit policies
but its use is spreading rapidly to other domains such as health
policy (Glied and
Tilipman 2010; Ringel et al. 2010; Rutter et al. 2011).
Microsimulation techniques bring a range of benefits to social
policy modelling,
including the ability to change a greater variety of parameters
independently and the
capacity to provide considerably more accurate estimates and
detailed projections of
the distributional effects of changes. Two key advantages of
microsimulation models
are that: (1) they can replicate the complexity of the policy
structures, transfers, and
settings; and (2) they can be used to forecast the outcomes of
policy changes and
‘what if’ scenarios (i.e. the counterfactual where the results
describe what, under
specified conditions, may happen to particular individuals and
groups) (Brown and
Harding 2002).

Microsimulation models are important tools for the development
of policy for
other reasons. Policy evaluation is typically undertaken ‘by ex-
post techniques which
by definition are used to evaluate the impact of interventions
and programmes fol-
lowing their implementation’ (Zucchelli et al. 2012, p. 2). Use
of microsimulation
removes the need to collect information after the
implementation of the policy—
allows the testing of a wide range of scenarios—with the range
only being limited
by the data that are available. Microsimulation also allows ex
ante examination of
the impact of policies before their implementation under
different conditions. In this
role, the models can be used to assist in recognising ineffectual
policies prior to their
implementation (Zucchelli et al. 2012).
A microsimulation model, as a policy decision-making support
tool, is ideally
implemented in a software package that can be interrogated by
policymakers in their
workplace as the need arises (Milne et al. 2014). However, it is
a major undertaking,
adding another layer of complexity, to make such a model
transparent and accessi-
ble to end users. Not only does the package need to
accommodate the simulation
code but it must also contain a user-friendly graphical interface
and adequate user
documentation. Then there are the challenging requirements of
delivering on-site
deployment, and end-user training and support.

Despite resource barriers to implementation, microsimulation
models hold much
promise for policy development. In the Appendix, we provide a
selection of well-
known microsimulation models used for policy purposes from
around the world. In
Sect. 14.8, we also present a case study from New Zealand.
14.7 Strengths and Weaknesses
‘To simulate a society realistically requires detailed data,
complicated models, fast
computers, and extensive testing’ (Spielauer 2011, p. 1).
The primary strength of microsimulation techniques is their use
of actual
individual-level data, which allows them to reproduce social
reality and the intri-
cacy of policy structures. The model can then be used to
estimate the outcomes of
[email protected]
‘what if’ scenarios (Brown and Harding 2002, p. 4). The benefit
of such models is
dependent on the circumstances in which they are used.
Spielauer notes that microsimulation is certainly the preferred
modelling choice
in three situations: (1) if population heterogeneity matters and
if there are too many

possible combinations of considered characteristics to split the
population into a
manageable number of groups; (2) if behaviours are complex at
the macro level but
better understood at the micro level; and (3) if individual
histories matter, that is,
when processes possess memory (Spielauer 2011, pp. 6–8).
1. The strengths of microsimulation for policy purposes can be
summed up as
follows:
(a) Can provide another piece to the jigsaw complementing
other forms of
evidence;
(b) Can tackle problems perhaps intractable by conventional
means of analysis
and interpretation;
(c) Can model processes or pathways that may be identified for
and amenable
to policy intervention;
(d) Can account for social complexity, heterogeneity, and
change in its use of
rich micro data for modelling;
(e) Can combine relevant information from disparate data
sources into a
cohesive whole to form the substantive content of the model;
(f) Can be used to carry out virtual experiments by altering
features of the
model and observing changes to outcomes, i.e. testing policy
scenarios

with various underlying assumptions;
(g) Can produce both aggregate and distributional results—a
property derived
from the use of individual-level data—and allows subgroup
analysis that
can inform the targeting of policy interventions.
2. There are typical weaknesses of the microsimulation method
that are not
necessarily inherent given more sophisticated modelling.
(a) Macro social outcomes tend to be merely a mechanical
aggregation of
micro ones—the macro is not greater than the sum of the micro
parts;
(b) There is no creative emergence of macro social phenomena
from micro
ones;
(c) There is no interaction between individuals or their
trajectories;
(d) There may be no feedback loops or reciprocal effects where
changes in
individual behaviour may modify policy impacts;
(e) Models tend to be predictive rather than explanatory—this
may improve
the accuracy of projections but may hamper substantive scenario
testing;
(f ) Pathways tend to be simplified and may miss important
elements;
(g) True individual behaviour, e.g. motivation or intention,
tends to be

neglected in favour of external manifestations, e.g. working
longer hours;
(h) There tends to be no influence of exogenous macro factors
(e.g. economic
climate) that may provide a broader societal context for the
model;
( i) There is high demand for data and computer technology.
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Policy 313
14.8 A Case Study: Modelling the Early Life Course
We present a case study describing the prototype of a dynamic
microsimulation
model (nicknamed MEL-C) developed by the COMPASS
Research Centre (Cen-
tre of Methods and Policy Application in the Social Science) at
The University of
Auckland, New Zealand (www.compass.auckland.ac.nz). The
full 5-year project
(2008–2013) was funded by the Ministry of Business,
Innovation and Employment
to produce a microsimulation-based policy tool to model aspects
of the early life
course, particularly focusing on family factors that influence
child outcomes (Milne
et al. 2014). End users from government agencies were involved
at an early stage to
ensure policy-relevant issues would be addressed and to

facilitate ultimate usage of
the tool. The prototype model reported here was based on data
from a single longi-
tudinal study of children while the full model incorporates data
and parameters from
the New Zealand 2006 Census and various longitudinal studies.
The emphasis here
will be on the methodology related to dynamic microsimulation
modelling and how
it can be used to test policy-relevant scenarios.
14.8.1 Aim
The overall aim of the project was to construct and apply a
computer-based model in
a New Zealand setting designed to represent social outcomes in
early childhood—
e.g. related to health or education—and enable experimentation
on the impact of
changing family factors—e.g. socioeconomic status (Lay-Yee et
al. In Press). The
construction of the model followed a framework (Fig. 14.1)
where structural factors
related to social advantage or disadvantage fundamentally
influence intermediary
family factors and final outcomes (Solar and Irwin 2010). Any
specific factor may
have a direct or an indirect effect, through a mediating factor,
on the outcome.
14.8.2 Methods
We employed a behavioural dynamic microsimulation model
reflecting a life course
perspective (Fig. 14.2). According to the typology introduced in
Sect. 14.4, this

dynamic model had the following features: it used an ‘actual’
starting sample of
children, a birth ‘cohort’, which was ‘closed’ to new entrants
and where mortality
was considered ignorable; and it was organised as ‘discrete-
time’ with annual time
steps in the simulation process, and ‘time-based’ with the
updating of individual
attributes occurring at the same time for all individuals in the
sample.
In order to build an empirically realistic model, our prototype
used longitudinal
data on a cohort of 1017 children born in Christchurch, New
Zealand in 1977 and
followed to age 13 (Christchurch Health and Development
Study or CHDS) (Fer-
gusson et al. 1989; Gibb et al. 2012). The CHDS data were used
for three purposes:
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Fig. 14.1 Model of structural and intermediary influences on
child outcomes
Starting
sample
Initial conditions at
Year 1 (Y1 )
Statistical

rules
How to transition
Simulation
engine
Y1 Y2 … Yi
Stochastic yearly update of
child characteristics
Scenario
testing
Change key features to
assess impact
Outcome
Family doctor visits
Real data
→ →
Fig. 14.2 The dynamic microsimulation model
(1) to form a starting sample providing initial conditions to the
simulation, (2) to
generate statistically derived simulation rules, and (3) to
provide benchmarks with
which simulated results could be compared to gauge
reproducibility. Data manipu-
lation and statistical analysis were carried out using SAS (SAS
Institute 2013) and R
(R Development Core Team 2013). The simulation was

implemented in JAVA and R
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Policy 315
(Mannion et al. 2012). Underlying tools for the graphical user
interface, ‘Jamsim’,
can be found at http://code.google.com/p/jamsim/ and for the
simulation engine,
‘Simario’, at http://code.google.com/p/simario/.
We applied statistically derived rules to age the cohort from
birth to 13 years and so
create a virtual cohort composed of representative synthetic
histories approximating
the original sample data (McLay et al. unpublished manuscript
under review). The
simulation model required firstly a critical sequencing of steps
within each annual
cycle and secondly dynamic transitions from year to year. Each
step or transition
can be seen to be governed by a submodel with the full model
consisting of an
ordered series of submodels. Each time-variant factor was
predicted and in turn was
a potential predictor to a successive time-variant outcome in the
cycle. Successive
submodels included both time-variant and time-invariant
predictors from preceding
submodels. Dynamic transition was achieved by the use of time-
variant predictors
including, where justified, a lagged dependent variable (state in

current year depends
on state in previous year). Thus, for any given individual, a
persistent link from year
to year enabled the generation of a coherent trajectory through
the life course.
14.8.3 Implementing the Simulation
Our discrete-time model used a starting sample comprising data
on a cohort of chil-
dren at birth. The simulation process for each subsequent year
followed a sequence
of steps from structural through intermediary factors to the final
outcomes. Each
time-variant attribute (in turn) for an individual child was
updated each year using
a statistically derived probability and Monte Carlo simulation.
The simulated esti-
mates were the average results of 100 runs with a different
random seed specified
for each run. We calculated 95 % confidence intervals around
simulated estimates
by taking the 2.5 and 97.5 percentiles on the distribution of the
means from the 100
runs. Validation of the simulated results was carried out by
comparison to the actual
real world CHDS data as borne out over the first 13 years of the
life course with the
test being whether the simulation model was able to reproduce a
similar distribution
of outcomes to the original longitudinal data.
14.8.4 Scenario Testing
Ultimately, the model could be used to produce projections into
the future or be

interrogated to assess the likely effects of changing various
social factors and their
pattern across groups. What if there was a policy intervention
that could improve
family factors and what would be its impact on outcomes for
children? We attempted
to answer this ‘what if’question by testing various scenarios.
We used scenario testing
as a form of counterfactual reasoning where we simulated a
potential outcome by
varying relevant (single or multiple) factors of interest in the
starting sample, e.g.
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aspects of family socioeconomic status, while holding other
initial factors constant,
and then observing change to the outcome, e.g. health service
use, compared to
baseline.
14.9 Conclusion
Microsimulation employs a systems approach, which can take
into account social
complexity, heterogeneity, and change. Microsimulation relies
on the availability
and quality of individual-level data that are representative of
the target population.
These data can come from various sources, which
microsimulation can potentially
combine into a cohesive whole. Microsimulation also relies on

the incorporation of
parameters for the core processes underlying the system of
interest. Thus, a virtual
(and realistic) world can be created, on which experiments can
be undertaken and
scenarios tested. Such a model is a simplification of reality but
is nevertheless a
powerful source of indicative information that can be used
alongside other evidence
for policy.
The future of microsimulation is bound with that of
computational social science in
general (Conte et al. 2012). The exponential growth in digital
data availability, allied
with burgeoning computational capacity to handle these data,
offers unparalleled
opportunities to model social systems and processes. This also
comes with challenges
notably developing frameworks that better address features
which have typically been
weak in the microsimulation approach: the macro–micro link,
the emergence of
social phenomena, social interaction, and reciprocal effects.
Such enhanced models
will be increasingly valuable in supporting policy practice in a
complex and rapidly
changing social world.
Acknowledgements The Modelling the Early Life Course (MEL-
C) project was funded by the
Ministry of Business, Innovation and Employment. Data were
made available by the Christchurch
Health and Development Study, University of Otago.
Appendix

Examples of Usage for Policy This section highlights a number
of existing mi-
crosimulation models used for policy purposes. The list is not
intended to be
exhaustive, merely to provide an indication of the broad sweep
of models in use across
various domains and countries. There are useful reviews and
compendia of existing
microsimulation models that describe their features in more
detail (for example: Li
and O’Donoghue 2012; O’Donoghue 2001).
APPSIM (Australian Population and Policy Simulation Model)
APPSIM, developed
by the National Centre for Social and Economic Modelling
(NATSEM), is a dynamic
model which simulates the life cycle of 200,000 individuals (1
% sample of census)
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Policy 317
from 2001 to 2050. It shows how the Australian population
develops over time under
various scenarios, and allows the social and fiscal impacts of
policy changes over
time to be simulated.
URL: http://www.natsem.canberra.edu.au/models/appsim/
EUROMOD (Europe) EUROMOD, based at the Institute for

Social & Economic
Research, University of Essex, is a static tax-benefit model for
the European Union
(2000s). It enables researchers and policy analysts to calculate,
in a comparable
manner, the effects of taxes and benefits on household incomes
and work incentives
for the population of each country and for the European Union
as a whole.
URL: https://www.iser.essex.ac.uk/euromod/
FEM (Future Elderly Model—USA) FEM, developed by the
RAND Roybal Cen-
ter for Health Policy Simulation, is a demographic and
economic simulation model
designed to predict the future costs and health status of the
elderly and explore
the implications for policy. It uses a representative sample of
100,000 Medicare
beneficiaries aged 65 and over drawn from the Medicare
Current Beneficiary
Surveys. Each beneficiary in the sample is linked to Medicare
claims records to
track actual medical care use and costs over time.
URL:
http://www.rand.org/labor/roybalhp/projects/health_status/fem.h
tml
LIFEPATHS (Canada) LIFEPATHS, developed by Statistics
Canada, is a dynamic
model of individuals and families from an 1872 birth cohort, to
today. It creates
synthetic life histories from birth to death that are
representative of the history of

Canada’s population. It can be used to evaluate government
programmes, or to
analyse societal issues of a longitudinal nature, e.g.
intergenerational equity.
URL:
http://www.statcan.gc.ca/microsimulation/lifepaths/lifepaths-
eng.htm
MIDAS (Microsimulation for the Development of Adequacy and
Sustainability—
Belgium, Germany, Italy) MIDAS is a dynamic population
model. Starting from
a cross-sectional dataset representing a population of all ages at
a certain point in
time (early 2000s), the life spans of individuals are simulated
over time. So new
individuals are born, go through school, marry or cohabit, enter
the labour market,
divorce, retire, and finally, die. During their active years, they
build up pension rights,
which result in a pension benefit when they retire.
URL:
http://www.plan.be/publications/Publication_det.php?lang=en&
TM=30&
KeyPub=781
POHEM (Population Health Model—Canada) POHEM is a
dynamic microsim-
ulation model intended to represent the lifecycle dynamics of
the population. The
simulation creates and ages a large sample until death. The life
trajectory of each
simulated person unfolds by exposure to different health events.
The model com-

bines data from a wide range of sources, including cross-
sectional and longitudinal
surveys, cancer registries, hospitalisation databases, vital
statistics, census data, and
treatment cost data as well as parameters in the published
literature.
[email protected]
TM=30&KeyPub=781
TM=30&KeyPub=781
URL: http://www.statcan.gc.ca/microsimulation/health-
sante/health-sante-eng.
htm#a2
SADNAP (Social Affairs Department of the Netherlands Ageing
and Pensions)
SADNAP is a dynamic microsimulation model for estimating
the financial and eco-
nomic implications of an ageing population and evaluating the
redistributive effects
of policy options. It simulates the life paths of a sample of the
Dutch population
using transition probabilities on demographic events. The model
uses administrative
data sets on pension entitlements and payments.
URL:
http://ima.natsem.canberra.edu.au/IJM/V4_1/Volume%204%20I
ssue%

201/5_IJM2011_van_Sonsbeek_CORRECTED_GD_JMS.pdf
STINMOD (Static Incomes Model—Australia) STINMOD is a
static microsimula-
tion model of Australia’s income tax and transfer system,
developed by NATSEM.
The model is mostly used to analyse the distributional and
individual impacts of
income tax and income support policies and to estimate the
fiscal and distributional
impacts of policy reform.
URL: http://www.natsem.canberra.edu.au/models/stinmod/
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[email protected]

Chapter 15
Visual Decision Support for Policy Making:
Advancing Policy Analysis with Visualization
Tobias Ruppert, Jens Dambruch, Michel Krämer, Tina Balke,
Marco
Gavanelli, Stefano Bragaglia, Federico Chesani, Michela
Milano and Jörn
Kohlhammer
Abstract Today’s politicians are confronted with new
information technologies to
tackle complex decision-making problems. In order to make
sustainable decisions,
a profound analysis of societal problems and possible solutions
(policy options)
needs to be performed. In this policy-analysis process, different
stakeholders are
involved. Besides internal direct advisors of the policy makers
(policy analysts),
external experts from different scientific disciplines can support
evidence-based deci-
sion making. Despite the alleged importance of scientific advice
in the policy-making
process, it is observed that scientific results are often not used.
In this work, a concept
is described that supports the collaboration between scientists
and politicians. We
T. Ruppert (�) · J. Dambruch · M. Krämer
Fraunhofer Institute for Computer Graphics Research,
Darmstadt, Germany

J. Dambruch
M. Krämer
J. Kohlhammer
GRIS, TU Darmstadt & Fraunhofer IGD, Darmstadt, Germany
T. Balke
University of Surrey, Surrey, UK
M. Gavanelli
University of Ferrara, Ferrara, Italy
S. Bragaglia · F. Chesani · M. Milano
University of Bologna, Bologna, Italy
F. Chesani
M. Milano
10.1007/978-3-319-12784-2_15
[email protected]
322 T. Ruppert et al.
propose a science–policy interface that is realized by including
information visualiza-
tion in the policy-analysis process. Therefore, we identify
synergy effects between
both fields and introduce a methodology for addressing the

current challenges of
science–policy interfaces with visualization. Finally, we
describe three exemplary
case studies carried out in European research projects that
instantiate the concept of
this approach.
15.1 Introduction
The increasing complexity of societal and economic problems in
the past decades
has brought new challenges to politicians. In order to make
sustainable decisions,
a profound analysis of the problems and possible solutions has
to be performed.
The process addressing this challenge, often referred to as
policy analysis, is one
of the most critical steps in the policy-making process. The
creation and analysis of
alternative solutions (i.e., policy options) to a public problem
remain a complex and
challenging task with many different stakeholders involved.
From our experience in European research projects in the field
of policy mod-
eling, there mainly exist two methods to make decisions on a
profound knowledge
basis: data-driven and model-driven approaches. Prominent
data-driven methods in
the policy-making domain include social media analysis, text
analysis (like opinion
mining, hot topic sensing, and topic summarization), and
statistical data analyses ap-
proaches. In this chapter, we focus on model-driven approaches
that aim at reflecting
complex real-world dependencies between social,

environmental, and economical
factors that have to be included in the analysis process. The
application of these
complex models in policy analysis helps to improve decision
making by providing
insight into the impacts that new policies may induce. To build
these complex models,
policy analysts often need to collaborate with external experts
consulted as advisors.
Due to different expertises of these stakeholders, the whole
process may suffer from
knowledge gaps. This implies challenges to be addressed.
In this chapter, we introduce a concept for visual decision
support systems to
support the policy analysis stages of the policy-making cycle.
These systems in-
clude information visualization and visual analytics as possible
solutions to bridge
knowledge gaps between stakeholders involved in the policy-
making process. The
methods can help non-IT experts to get access to complex
computational models.
The coupling of visualization techniques and computational
models supports differ-
ent stakeholders in the policy-making process. The standard
policy cycle will build
the foundation of identifying the need for objective analysis in
the entire policy-
making cycle. Therefore, we characterize the main stakeholders
in the process, and
identify knowledge gaps between these roles. We emphasize the
merits of including
visualization techniques into the policy-analysis process, and
describe visualization
as a facet bridging the knowledge gaps in a collaborative

policy-making life cycle.
After describing our concept, best practices and research
approaches will be dis-
cussed that have already implemented aspects of our concepts in
European research
[email protected]
15 Visual Decision Support for Policy Making 323
projects. These approaches are implemented into decision-
support systems that in-
clude visual interfaces to complex models and enable policy
makers, policy analysts,
etc. to participate in the analysis of policy options.
15.2 Background
In the following, we first give an introduction to information
visualization and related
fields. The goal is to provide an overview of the capabilities
that these research fields
offer for policy making. Second, the discipline of policy
analysis is characterized.
With this introduction, we intend to harmonize the terminology,
summarize different
perspectives on the discipline, and identify open problems and
challenges that inhibit
the field.
15.2.1 Information Visualization and Visual Analytics
Information visualization is defined as “the use of computer-
supported interactive,

visual representations of abstract data to amplify cognition”
(Card et al. 1999). In
this definition, several aspects of information visualization are
highlighted. First of
all, as a research discipline from computer science, its solutions
are provided as
software. Second, these software solutions address the visual
representation of data
through visual artifacts or diagrams. The artifacts consist of
basic visual elements
that have been presented in the theory of graphics by Bertin
(1983). In contrast to
the static visual representation, information visualization deals
with the interactive
visual representation of data. Hence, an important aspect lies in
the possibility of
the user to interact with graphics generated by the software.
Zoom and filter opera-
tions on the data are examples for user interaction. As a further
aspect, information
visualization deals with abstract data in contrast to scientific
data. While scientific
data are typically physically based reflecting at least some
geometric information,
abstract data such as economic data or document collections are
not. Finally, the
goal of information visualization is to amplify cognition.
Cognition is defined as
the acquisition of knowledge and insight about the world (Card
et al. 1999). With
information visualization, the user is enabled to gain knowledge
about the internal
structure of the data and causal relationships in it. Thereby,
vision as the human
sense with the highest bandwidth is exploited to support the
comprehension of in-

formation. “Visual representations and interaction techniques
take advantage of the
human eye’s broad bandwidth pathway into the mind to allow
users to see, explore,
and understand large amounts of information at once” (Thomas
and Cook 2005).
Following Stephen Few, the purpose of information
visualization is to support
the exploration, sensemaking, and communication of data (Few
2009). Extracted
from his work, Fig. 15.1 provides an overview of the broader
data visualization field.
Here, the activities addressed by data visualization are
exploration and sensemaking
[email protected]
Fig. 15.1 A characterization of data visualization by Stephen
Few (2009). Distinction between
activities addressed by data visualization. Exploration and
sensemaking as analysis tasks. Commu-
nication as knowledge transfer task. Technologies differ with
respect to presented data (information
visualization for abstract data and scientific visualization for
physically based data) and interaction
capabilities of the technologies, e.g., graphical presentation
does not imply user interaction. Under-
standing of provided information as intermediate goal. Good
decisions based on derived knowledge
as the end goal

as analysis tasks, and communication as knowledge transfer
task. While exploration
and sensemaking have the goal to extract knowledge from data,
the purpose of
communication is the transfer and presentation of these analysis
outcomes. The
applied data visualization technologies in Fig. 15.1 are
information visualization, on
which we focus in this approach: scientific visualization and
graphical presentation.
Information visualization can be used for both analysis and
presentation. However,
the choice of information visualization techniques and their use
will differ with the
task, data, and users involved. As an intermediate goal, data
visualization attempts
to support the understanding of the information hidden in the
massive amounts of
data. The ultimate goal is to support good decision making
based on the knowledge
extracted from the data.
Information visualization emerged from research in human–
computer interac-
tion, computer science, graphics, visual design, psychology, and
business methods
(Shneiderman and Bederson 2003). It allows to intuitively
access results of complex
models, even for nonexperts, while not being limited to intrinsic
application fields.
In fact, information visualization is increasingly considered as
critical component
in scientific research, data mining, digital libraries, financial
data analysis, manu-
facturing production control, market studies, and drug discovery
(Shneiderman and

Bederson 2003).
The growing amount of data collected and produced in modern
society contain
hidden knowledge that needs to be considered in decision
making. Due to the data’s
[email protected]
Fig. 15.2 Visual analytics process adapted from Keim et al.
(2008). Connecting the information
visualization (top) and the data mining (bottom) processes
volume and complexity information, visualization can no longer
be applied alone.
A new research discipline within information visualization was
introduced. Visual
analytics is defined as “the science of analytical reasoning
facilitated by interactive
visual interfaces” (Thomas and Cook 2005). The goal of visual
analytics research is
the creation of tools and techniques to enable the user to (a)
synthesize information
and derive insight from massive, dynamic, ambiguous, and often
conflicting data, (b)
detect the expected and discover the unexpected, (c) provide
timely, defensible, and
understandable assessments, and (d) communicate assessment
effectively for action.
In contrast to pure information visualization, visual analytics
combines interactive
visualization with automated data analysis methods to provide

scalable interactive
decision support.
Figure 15.2 shows an adaptation of Keim’s widely accepted
process model for
visual analytics (Keim et al. 2008). The visual data exploration
process from in-
formation visualization (upper part), and automated data
analysis methods (lower
part) are combined to one visual, and interactive analysis
process model. The user
is directly included in the model by interactive access to the
process steps. This
generic process model makes visual analytics applicable to a
variety of data-oriented
research fields such as engineering, financial analysis, public
safety and security,
environment and climate change, as well as socioeconomic
applications and policy
analysis, respectively. The scope of visual analytics can also be
described in terms of
the incorporated information and communication technologies
(ICT) key technolo-
gies like information visualization, data mining, knowledge
discovery or modeling,
and simulation (Keim et al. 2008). In its framework program
seven, the European
[email protected]
commission (EC) emphasized visualization as a key technology
in the objective for

ICT for governance and policy modeling (European Commission
2010).
Recently, methodologies on how to design and implement
tion and visual analytics solutions for data-driven challenges of
domain specialists
have been presented (Munzner 2009; Sedlmair et al. 2012). Due
to their reflec-
tion upon practical experiences of hundreds of information
visualization and visual
analytics research papers, the value of the introduced
methodologies is widely rec-
ognized. In these methodologies, visualization researchers are
guided in how to
analyze a specific real-world problem faced by domain experts,
how to design visu-
alization systems that support solving this problem, and how to
validate the design.
Considering information visualization validation, we refer to
Lam et al. (2012).
Recent approaches in visual analytics focus on the questions
how to simplify
the access to the analysis functionality of visual analytics
techniques, and on how to
present analysis results. This includes the analysis process with
its intermediate steps,
and the findings derived with the visual analytics techniques
(Kosara and Mackinlay
2013).
Information visualization and visual analytics approaches in the
policy analysis
domain are still surprisingly scarce compared to the number of
approaches presented

in other analysis-driven fields (Kohlhammer et al. 2012). Still,
the policy analysis
domain is an ecosystem with a variety of involved stakeholders
that intend to col-
laborate in the best possible way. This outlines policy analysis
as an interesting
application field for information visualization.
15.2.2 Policy Analysis
Policy analysis as a discipline of the policy sciences was
introduced by Laswell and
Lernen in their work “The Policy Sciences” in 1951 (Schneider
and Janning 2006). It
was interpreted as societal problem-solving discipline with the
higher goal to support
rational decisions in policy making (Blum and Schubert 2009).
The main breach
of this approach erased from the experience that decisions
solely based on rational
perspectives (positivism) are not sufficiently considering
external factors within real-
world scenarios. From this experience, post-positivism
approaches evolved (Fischer
et al. 2007). With this change of perspectives, the profession of
the policy analysts
had to be newly interpreted (Howlett and Wellstead 2009).
The knowledge background of policy analysts has to cover
different facets ranging
from policy science, social science, and economical science, to
computer science
(Göttrik 2009). The main objective of policy analysts is to
provide scientific advice
to policy makers during their political decision-making process.
This process is also

defined as the policy cycle (Blum and Schubert 2009). The
policy cycle introduced
by Jones (1970) and Anderson (1975) is depicted in Fig. 15.3.
It consists of five succeeding stages: problem identification and
agenda setting,
policy formulation, policy adoption, policy implementation, and
policy evaluation.
In the first stage, public problems are identified and the
political agenda is set by
[email protected]
Fig. 15.3 Policy cycle adapted from Jones (1970) and Anderson
(1975). Policy analysis is mainly
conducted in the policy formulation and the policy adoption
stage. While in the policy formulation
stage, alternative solutions to a given problem are defined; in
the policy adoption stage, one of
these solutions (policy options) is selected for implementation.
In a broader sense, policy analysis
can also be conducted in the other stages of the cycle. However,
in this work, we focus on the
pre-decision phase before a policy is implemented
prioritizing societal problems. In the second stage, alternative
solutions to these
problems are explored and evaluated. In the third stage, these
policy options are
compared, and it is decided which option to choose. In the
fourth stage, the selected
policy option is implemented through legal process. In the final

stage of the cycle,
the implemented policy is evaluated with respect to the
objectives defined in the first
stage of the cycle.
Within the policy cycle, especially the policy formulation phase
is supported by
the policy analysts’ expertise. “Policy formulation clearly is a
critical phase of the
policy process. Certainly designing the alternatives that
decision makers will consider
directly influences the ultimate policy choice” (Fischer et al.
2007).
More precisely, policy formulation is about choosing from
different types of
policy options those that can be used to address particular
policy problems. Then,
these choices are analyzed in terms of both their technical and
political feasibility,
with an eye to reducing their number to a small set of
alternative courses of action that
can be laid out for decision makers at the next stage of the
policy process (Howlett
et al. 1995).
Despite the expected importance of considering scientific
knowledge in the policy
formulation phase, a main deficit of policy making is
recognized: the policy paradox
describes the asymmetry between the amount of knowledge
generated by scientific
experts, and the actual amount of knowledge effectively used in
the decision-making
process (Shulock 1999). Among others, this paradox evolved
from the following

obstacles:
[email protected]
Limitations of scientific approaches: The impacts of societal
processes are highly
complex. The scientific models attempting to simulate reality
are seldom precise or
accurate. Furthermore, in most cases this uncertainty is not
communicated to the user.
This fails to raise the awareness of the model’s uncertainty and,
as a consequence,
reduces the credibility of scientific outputs (Hove 2007;
Schneider 2008; Göttrik
2009).
Isolation, complexity, and ephemerality of political processes:
The policy cycles
move faster than the research cycles (Schneider 2008; Howlett
and Wellstead 2009).
Some of the most frequent problems that policy analysts
mention are lack of time,
resources, and the ignoring of information (Howlett and
Wellstead 2009).
Subjectivity of stakeholders: Different facets of subjectivity
influence scientific
experts and advisors. Human subjectivity is induced by
individual, cultural, and
religious values (Hove 2007). Economic subjectivity is induced
since the advisors
and scientific experts may be influenced by their employer

(Dobuzinskis et al. 2005).
Moreover, political subjectivity may be induced due to an
affiliation to political
parties (Greven 2008).
Concepts have been introduced to address these problems,
which evolve from
bringing two contrary systems, politics and science, together.
Most of these con-
cepts can be summarized under the term science–policy
interface. Janse defines the
science–policy interface as “the point at which science and
policy meet and act on
each other” (Janse 2008). The positive aspects out of this are:
(a) rationality and
legitimation through knowledge in politics, (b) exploration of
policy alternatives
with focus on cause and effect, (c) communication between two
fields—e.g., re-
search assignment and scientific advice. In order to realize
these aspects, the concept
of “knowledge brokers” is propagated. Their goal is to mediate
between the two
systems (Howlett and Wellstead 2009). Still, these concepts
contain the risk of sub-
jectivity described above. As a consequence, the propagation of
a mere technocratic
model has to be replaced by a concept with high interaction
possibilities between
knowledge and decision makers as mentioned in Göttrik (2009).
In our approach,
we will lay out how information visualization may support this
concept.
15.3 Approach

In this section, we describe our approach of using visual
decision support systems
as the means to bridge gaps in policy analysis. A first version of
this concept can be
found in our previous work (Ruppert et al. 2013a). In the
following, we characterize
the stakeholder involved in the policy-analysis process. Then
we provide more detail
about the policy-analysis process with its challenges, and
provide a concept how
to address these challenges. As the last part of this approach, we
summarize the
advantages of our concept with regards to enhanced policy
making.
[email protected]
15.3.1 Characterization of Stakeholders
As described in Sect. 15.2, several stakeholders involved in the
policy-making process
can be identified.
Policy makers are the final decision makers in the policy-
analysis process. They
decide which societal problems appear on the political agenda,
and by which policy
option they are finally tackled. In most cases, policy makers do
not have the time and
the technical background to execute the policy analysis by
themselves. For making
profound decisions, they have advisors, namely policy analysts,

who conduct the
analysis, and provide summaries of the analysis in the form of
reports, or presenta-
tions. Still, in the agenda setting and problem definition stage,
policy makers decide
which public problems appear on the political agenda, and how
these problems are
defined. Requirements for the analysis of policy options are
derived from this prob-
lem definition. After the analysis process, policy makers finally
decide which of the
generated policy options will be implemented.
Policy analysts (or policy advisors) are the coordinator of the
policy analysis.
Their goal is to conceptualize the problem based on the
requirements defined by the
policy maker. Then, they have to identify information sources,
and consult external
advisors that assist in analyzing the problem. Finally, the policy
analyst provides
alternative solutions (policy options) to the policy maker via a
report or presentation.
Modeling experts are in most cases external advisors recruited
by the policy ana-
lyst. They have profound knowledge in modeling techniques.
Expertise in the policy
domain is not necessarily required from modeling experts. Still,
the models have to
be adapted to the policy domain, which can be realized by
translating the problem to
the model domain or by defining technical requirements on the
model. The adapted
model supports the policy analysis by producing outcomes—
e.g., impact of possible

actions that are the basis for the generation of policy options.
Domain experts are optional stakeholders in our concept. In
many cases, the
analytical models have to be fed with domain knowledge and
data. If neither the
modeling expert nor the policy analyst can provide this
information, a domain expert
has to be consulted. The domain expert does not necessarily
have expertise in policy
analysis or modeling techniques but rather contributes as an
information provider.
Public stakeholders are not explicitly considered in our concept.
Still, they play
an increasingly important role in the policy-making process. By
realizing an intuitive
visual access to complex models for the analysis of policy
options, even nonexperts
like most citizens may be involved in the policy analysis. This
will increase the
transparency of the whole policy-making process, improve
democracy, and increase
the trust in the policy makers. An example of how public
stakeholders can be in-
cluded in the policy analysis is described in the case study of
the urban agile policy
implementation (urbanAPI) project in Sect. 15.4.3.
A further stakeholder that is not explicitly included in our
concept is the role
of the politician. From our perspective, politicians influence
various of the above
stakeholders and sometimes even play their roles. For example,
the agenda setting
and thereby the work of the policy maker is influenced by

politicians. Moreover,
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they may act as a neutral (or public) stakeholder observing the
potential solutions
generated through science–policy interfaces. Furthermore, the
acquisition of external
advisors, the work of the policy analyst, may be influenced by
politicians. Due to this
heterogeneity, we do not explicitly define the role of the
politician in this approach.
However, we want to point out their influences.
These different stakeholders need to collaborate in the policy-
analysis process to
generate policy options that will later be implemented to tackle
societal problems.
Due to the stakeholders’ different backgrounds and knowledge,
the collaboration is a
challenge to be addressed by science–policy interfaces. In the
next section, we show
how information visualization technology may be applied as
supporting component
of science–policy interfaces.
15.3.2 Bridging Knowledge Gaps with Information
Visualization
We now sketch how the policy-analysis process is usually
conducted. This is based
on the literature review presented in Sect. 15.2 and our

experience with projects in
the field of policy modeling. After characterizing the process,
we introduce a method
to include visualization into policy analysis to bridge the
knowledge gaps between
different stakeholders involved.
At the beginning of each policy-analysis process, a public
problem is identified that
is put on the political agenda. It is mostly described in a more
or less abstract way by
the policy maker. In order to generate policy options that tackle
this problem, policy
analysts (policy advisors) are consulted. These policy analysts
(a) gather information
to provide policy options by themselves, or (b) ask external
experts for help in
analyzing the problem. Often, these external experts have a
scientific background.
They provide models that help in isolating the problem, and
simulating the potential
impact of generated policy options on societal, environmental,
and economic aspects.
The extracted knowledge provided by scientific experts is
summarized by the policy
analyst, most likely as a written report, as described in Weimer
and Vining (2005).
This report is presented to the policy maker textually, or as a
presentation. It contains
policy options to tackle the defined problem as well as an
analysis of the impacts
that each of these options inhibit. Based on this knowledge, the
policy maker has
to decide which option to choose. Alternatively, another
iteration of the process can
be requested by refining some parts of the problem definition.

The upper part of
Fig. 15.4 summarizes this process in a simplified way.
Our approach extends the “classical” policy-analysis process
with information
visualization technology. As described above, the generation of
policy options is
enriched by including scientific knowledge, in terms of
information extracted from
data or scientific models, into the process. This information and
the models can in
most cases be represented by computational models. However,
the complexity of
these models impedes the usage of the underlying knowledge
for policy analysis. In
order to simplify the access to computational models developed
by policy analysts,
[email protected]
Fig. 15.4 Visual support model for policy analysis. The upper
part denotes the policy analysis
process which is conducted in the policy formulation stage of
the policy cycle (cf. Fig. 15.3).
Visualization is introduced into the process in order to support
the analysis of policy options
or in most cases external scientific experts, we propose to
connect information visu-
alization techniques to these models. In this way, the
complexity of the models can
be hidden in the computational back end, while only the

information necessary for
providing user input (e.g., control parameters, etc.) and
analyzing the model output
(e.g., simulation results, statistical measures, etc.) is displayed
on the screen. The
most crucial aspect of this concept is that non-IT expert users
can visually interact
with computational models and execute their own analysis.
Hence, the policy ana-
lyst and the policy maker can define their requirements and
constraints via a visual
interface (see Fig. 15.4, left side). They can “experiment” with
different settings, and
generate alternative model outputs (see Fig. 15.4, right side).
As another aspect, due
to the similar representation of the model, the different
stakeholders can validate the
models’ utility and usability with visualization technology. For
example, the policy
maker can detect aspects not covered yet by the model, that the
modeling expert
might include in an improved model. The communication of
results is facilitated,
since all stakeholders work with the same visual representation.
To achieve a user-centered system that combines computational
models with
information visualization capabilities, several steps have to be
undertaken. In the
following, we propose a possible development cycle based on
the concept of An-
drews (2008). Before the visualization design process, the
policy maker and the
policy analyst have to define the societal problem (agenda
setting) and identify rele-
vant external experts that may support the decision-making

process. We propose to
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include visualization experts in the process from the very
beginning, since they have
to help in conducting the requirement analysis. The steps that
follow are:
Phase before design: In this phase, the requirement analysis has
to be conducted.
That includes the characterization of the (policy) domain, the
needs of the users
(e.g., policy maker, policy analyst, etc.), and an abstraction of
the tasks the users
want to achieve (e.g., generation of policy option, impact
analysis, etc.). Furthermore,
together with the scientific experts, computational models that
may help in solving
the tasks have to be defined.
Phase during design: In this phase, the information gathered in
the previous step
have to be summarized and structured. The data exchange
interfaces to the computa-
tional models have to be specified. Furthermore, an initial
design of the visualization
tool has to be developed. Therefore, the interaction design, and
the visual encodings
of the underlying data has to be created.
Phase during implementation: In this phase, the initial design is

implemented into
software. This software will be implemented in an iterative
process together with the
user. Intermediate versions of the software will be shown to the
users and improved
based on the users’ feedback regarding initial and upcoming
requirements.
Phase after implementation: In the phase after the
implementation, the system
will be deployed, and tested regarding usability and utility. It
will be measured
whether the users can solve their tasks with the system, and how
easy and intuitive
it is adopted by the user.
The method presented here supports the design process whose
main purpose is to
find the right kind of visualization for the given problem. Note
that there is no one-
size-fits-all solution for information visualization. There are
visualizations that work
better as others in certain use cases but fail otherwise. If we
generalize this problem
to notations—i.e., ways to express information, either visually
or textually—we can
refer to Green who states, “a notation is never absolutely good,
therefore, but only in
relation to certain tasks” (Green 1989). Indeed, the question
whether general visuals
are appropriate for policy analysis is difficult since policy
analysis usually involves
many tasks (cf. Whitley 1997). However, visualizations tailored
to a specific problem
can help stakeholders understand its complexity. Gilmore and
Green summarize this

connection between a visual and the ability of stakeholders to
solve a certain problem
based on this visual in their match–mismatch hypothesis
(Gilmore and Green 1984).
They state that every notation (i.e., visualization) highlights
certain aspects of a
problem while it hides others (Whitley 1997). The match–
mismatch problem implies
that for every task different kinds of visualizations have to be
used. That is also the
reason why the case studies presented in Sect. 15.4 differ from
each other in terms of
visualization methods and why they cannot be generalized. For
further readings about
design processes in information visualization and visual
analytics, we recommend,
among others, the approaches by Sedlmair et al. (2012) and
Munzner (2009).
[email protected]
15.3.3 Synergy Effects of Applying Information Visualization
to Policy Analysis
In order to address the challenges imposed on science–policy
interfaces, we pro-
posed the inclusion of information visualization techniques
within policy making
(see Fig. 15.4). Hereby, information visualization serves as an
important component
of the science–policy interface itself. The following aspects can
be resolved with this

integration:
Communication. The communication between science and
policy fields will be
facilitated. Visualization may serve as a mediator of
information between two dis-
tinct environments. Through the similar visual appearances of
the system, different
stakeholders may discuss issues on the same visually presented
information basis.
Thereby, the interaction of scientists and policy analysts with
the policy-making
process will be supported.
Complexity: Through the abstraction of user tasks and
interactions with scientific
models, the complexity of the underlying models may be
reduced. With visualization,
the complexity of scientific models can be executed on the
machine side, while the
degrees of freedom in form of parameters for their execution
can be intuitively
displayed on the screen. Visual interfaces provide the
information on the level of
detail needed by the respective user role.
Subjectivity: The aspect of subjectivity can be reduced since
get access to the same information provided in an “objective”
way via information
visualization techniques. Hence, the provided information can
be discussed among
the stakeholders to balance subjective interpretations of the
findings.
Validation: The outcomes of the policy-analysis process can be

transparently pre-
sented to all involved stakeholders including public
stakeholders. That way, decisions
can be justified since they have been made based on an
objective analysis. This can
improve the trust in scientific results and political decision
making.
Transparency and reproducibility of results: Public stakeholders
(e.g., journal-
ists, interest groups, etc.) can generate analysis results with the
same tool and
therefore better understand the rational background of political
decisions.
15.4 Case Studies
In the following, we present three case studies that have already
implemented aspects
of our concept within European research projects. The target of
each case study is
briefly described. Relevant stakeholders are identified, and their
roles in the process
are characterized. In each approach, scientists have developed a
computational model
to support policy makers and policy analysts in their decision-
making process. To
simplify the access to these models, visual interfaces have been
designed that have
[email protected]

been developed by information visualization experts. The
models and the visual
interfaces to them are described. Finally, for each case study,
findings are presented
that substantiate the benefits of our concept. The three case
studies covered the
following scenarios:
1. Optimization: the optimization of regional energy plans
considering environmen-
tal, economical, and social impacts (cf. Sect. 15.4.1).
2. Social simulation: the simulation of the impact of different
policy instruments on
the adoption of photovoltaic (PV) panels at household level (cf.
Sect. 15.4.2).
3. Urban planning: the integration of heterogeneous data
sources (simulation results,
user input, etc.) in collaborative urban planning scenarios (cf.
Sect. 15.4.3).
15.4.1 Optimization
The first case study presented comes from the ePolicy1 project,
whose main aim is to
support policy makers in taking transparent, informed, and well-
assessed decisions.
The ePolicy goal is to develop a decision support system
assisting the policy maker
in understanding the impact of her decisions on the
environment, the economy, and
the society. The specific case study of ePolicy is the regional
energy plan of the
Emilia-Romagna region in Italy, in its part concerning the
renewable energy share.

The design of a policy for regional planning is followed, as
prescribed by European
regulations, by an environmental assessment of the devised
plan. Traditionally, the
policy maker decides a policy, possibly with allocation of the
available funds to
chapters, and the plan is submitted to an environmental expert
to be assessed. In the
Emilia-Romagna region, the assessment is performed by using
the so-called coaxial
matrices (Cagnoli 2010) that are a development of the Leopold
matrix (Leopold
1971). The information contained in the plan is usually very
high level and misses
many details, so the environmental assessment is usually only
qualitative. One of
the coaxial matrices links the possible actions that are taken in
a plan with their
environmental pressures providing information on how much
each of the activities
impacts on the environment. Current coaxial matrices consider
93 activities that range
from building new constructions (buildings, sewers, factories,
bridges, yards, etc.)
to energy plants (PV, biomasses, coal, etc.) to moving materials
(waste, dangerous
materials, etc.) to installing infrastructures (pylons, wires,
cables, etc.). Activities can
have various impacts (or pressures) on the environment: e.g., a
thermoelectric power
plant emits pollutants or greenhouse gases in the atmosphere,
consumes water, etc.
The environmental pressures, then, modify the environment, and
in particular, some
environmental indicators called receptors: the emission of air

pollutants changes the
receptor quality of the air, while the emission of greenhouse
gases impacts on the
climate change. A second matrix links environmental pressures
with environmental
receptors. Both matrices contain qualitative values: the impacts
can be high, medium,
1 http://www.epolicy-project.eu/.
[email protected]
or low (or null). By considering the plan devised by the policy
maker, and elaborating
the environmental matrices on a large spreadsheet, the
environmental expert is able
to point out critical aspects of the plan.
However, even if there are important critical aspects in the plan,
the effort for
building a new plan, and reassessing it is so high that only
small variations can be
done. Moreover, although European regulations state that two or
more alternative
plans should be compared and environmentally assessed, this is
rarely done in prac-
tice due to the difficulties and costs of designing alternative
plans; in some cases, the
devised plan is compared to the do-nothing case (the absence of
a plan), but devising
actual alternative plans and comparing them is usually out of
reach.

15.4.1.1 Involved Stakeholders
The involved stakeholders in this research are primarily two.
One is a policy analyst,
an expert in the energy field. She is responsible for devising the
Regional Energy
Plan for the Emilia-Romagna region of Italy. Basically, she has
the possibility of
stating the minimum amount of energy to be produced and
constraints limiting the
minimum and maximum amount of energy to be produced by
each renewable energy
source given the regional characteristics and some political
choices. As output, the
policy analyst could obtain a number of alternative scenarios for
the energy plan that
can be easily compared.
The second is the domain expert that in this case is an
environmental expert whose
main task is the configuration of the system. For example, the
coaxial matrices are
inserted into the system by environmental expert who studies
the impact of activities
on the environment.
The system being tailored on a specific policy domain needs a
modeling expert
whose main aim is to (a) define the decisions that should be
taken in the regional
plan, (b) state the constraints tightening possible combinations
of decisions, and (c)
specify objective functions defining the evaluation metrics for
the policy.

15.4.1.2 Underlying Technologies
In order to overcome the difficulties in current practices, and
improve current method-
ologies, we developed a constraint-based application for
performing automatically
the environmental assessment of a plan (Gavanelli et al. 2010).
In this way, the assess-
ment phase could be performed easily and in a very short time,
and the environmental
impact of different, alternative plans could be quickly
compared.
However, the real challenge was the integration of the two
phases: planning and
environmental assessment. A unique constraint model to
perform both the planning
and the environmental assessment was later proposed (Gavanelli
et al. 2013).
The constraint model included not only the coaxial matrices
needed for environ-
mental assessment but also the cost of each activity, so that a
global cost of the plan
could be computed. Some of the activities are of primary
importance for the given
[email protected]
type of plan: For example, power plants are the main activities
considered in an en-
ergy plan. On the other hand, power plants require

infrastructures to be performed;
although these secondary activities are not the main aim of the
given plan, they can
have an impact on the environment, and should be considered in
the assessment. For
example, thermoelectric plants require power lines, pipelines
(for oil, gas, or steam),
cooling systems, roads, etc., while a hydroelectric power plant
may require the con-
struction of a dam or other hydraulic works. The construction of
such facilities has
a significant impact on the wildlife, or on the wellness of
people living nearby.
Other aspects taken into consideration in the constraint model
are the minimum
and maximum amount of each energy source, the required
energy production (in
terms of both electrical energy and thermal energy), and the
amount of energy
produced in a year for a given energy source.
The constraint model can be used to generate a single plan, e.g.,
optimizing an
objective function. Examples of objective functions are the
minimization of the cost,
the maximization of the produced energy, the maximization of
some environmental
receptor (for example, one can compute the plan that improves
most the quality of
the air in the region), or a linear combination of these.
In case the policy maker wants to optimize more than one
objective function,
the constraint model can be used to compute a set of solutions.
If two objectives

are given, one solution can be the optimum for the first
objective, another one for
the second objective. Moreover, a series of plans in between can
be found that are
nondominated, i.e., for which there exists no other plan that
improves both objective
functions. The set of nondominated points is called the Pareto
front. In such a way, it is
easy to generate and compare alternative plans. Moreover, the
considered alternative
plans are not simply the absence of a plan, but they are the
result of an optimization,
and, in particular, represent plans for which it is impossible to
improve one objective
without sacrificing another objective.
15.4.1.3 Visual Design
We embedded the global optimizer component into a web
interface that takes as input
the bounds for each energy source, the objective functions to be
optimized, and the
number of plans that should be compared (see Fig. 15.5).
The global optimization component computes the Pareto front of
the solutions that
optimize the declared objectives. The visualization module
provides the computed
energy plans in several views. An overview shows the Pareto
front through the
different objective functions in a scatter plot view. The plans
are also compared
through bar graphs showing the amount of energy produced by
each source. Each
of the computed plan can be monitored in a single view
providing details about this

plan (see Fig. 15.6).
The environmental expert also suggested to add to the interface
a set of dashboards:
for each plan, they show the three environmental receptors with
the best and worst
values; in this way, one gets immediately the idea of which
environmental aspects are
most critical for this plan, and which are improved. For a more
detailed description
and evaluation of the visual interface, we refer to Ruppert et al.
(2013b).
[email protected]
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15.4.1.4 Findings
The developed system provides a number of features that enable
a better policy-
making process. First of all, it enables the policy analyst to
compare different sce-
narios on demand, evaluating their strengths and weaknesses
from an environmental
and economic perspective.
Second, the domain expert that configures the system can insert
and update the
coaxial matrices and activity costs. As new technologies evolve,
the impact of activ-
ities on the environment might change and coaxial matrices as
well as costs can be
consequently updated.
Finally, the system enables a more transparent decision process
empowering
a participatory and collaborative approach to public policy
making. Citizens and
stakeholders can get an explanation and a reason why some
choices have been done.

15.4.2 Social Simulation
One further area of research, which has become important for
supporting the policy-
making process in recent years, is the area of social simulation
(Brenner and Werker
2009). Social simulation is a research field that applies
computational methods to
study issues in the social sciences. One of its main aims thereby
is to bridge the
gap between the descriptive approach used in the social sciences
and the formal
approach sometimes used in the computer sciences, by moving
the focus on the
processes/mechanisms/behaviors that build the social reality
(Edmonds et al. 2007).
The goal of social simulation is to study this (complex) social
reality in order to
understand it better—and in case of policy making—to be able
to influence and
shape this social reality in a better way.
In the EU-funded ePolicy project, a social simulation was used
to analyze the
impact of different policy instruments on the adoption of PV
panels at a household
level. The social simulation logically is to be used by policy
makers and policy
analysts after the planning step on the regional level (also
referred to as global level in
the project). Once planning goals have been determined,
through optimization (e.g.,
with respect to minimum costs, maximum CO2 reduction,
minimum disruption, etc.)
and decisions on how to allocate the budget to policy

instruments in order to achieve
these goals at the level of the region have been made, it is then
necessary to apply
the policy instruments.
However, what is an optimal policy at the regional level may
not be locally or
individually optimal. Thus, some (incentive) mechanism is
needed to enforce the
policy—one cannot assume that individual agents will adopt the
desired behaviors
of their own accord. These potential policy instruments could
reach from fiscal
incentives, via tax incentives and different tariffs to legislation
for example. All
options have disadvantages, and which is best is not clear and
will vary from case
to case. In order to be able to assess effects of the different
policy instruments on
individual households better, a multilevel agent-based model
has been developed
[email protected]
that allows policy makers to explore the consequences of
different types of policy
instrument and thus enable them to make better choices.
In the social simulation (software), agents represent the main
actors, the indi-
vidual households. They are given behavioral rules modeling
their likely individual

responses to policy instruments (including the effect of
influences from other actors,
e.g., as a result of collective actions, imitation etc.). The overall
response to the
simulated policy instruments will be measured to inform policy
makers.
15.4.2.1 Stakeholders
In the policy-making process, typically a large number of
different stakeholders can
be involved with a social simulation, including the mentioned
policy makers, policy
analysts, domain and modeling experts as well as public
stakeholders.
In the social simulator of the ePolicy project in particular, four
stakeholder groups
thereby have been focused on:
• Policy analysts and policy makers that use the simulation to
derive new informa-
tion and knowledge about the modeled system (possibly in order
to make policy
decisions).
• Domain and modeling experts who are required in order to
build the model both in
terms of the technical implementation as well as the
contribution of domain knowl-
edge required for conceptualizing the simulation model. The
domain experts
thereby were asked to provide domain knowledge about the
behavior of house-
holds with respect to PV as well as demographic information of
the households

to be modeled. The modeling expert’s task was to translate this
domain knowl-
edge into agent rules and to incorporate the provided
demographic household
information (mainly data) into the simulation setup.
In this case study, we are mainly looking into the first group,
i.e., policy analysts
and policy makers and will consider the second group (i.e.,
domain and modeling
experts) as suppliers of tools for them.
15.4.2.2 Underlying Technologies
The term social simulation can have several types of simulation
and modeling of
which agent-based modeling (ABM) is the most popular one.
An ABM “is a computational method that enables a researcher
to create, analyze,
and experiment with models composed of agents that interact
within an environment
(Abdou et al. 2012).”
There are several important elements in this description. First,
the model is com-
posed of autonomous and heterogeneous agents. That is, there
are many simulated
individuals with different properties and decision-making rules.
In ePolicy for exam-
ple, properties include geographic location, PV, and policy
instrument knowledge as
well as housing and financial situation, and rules include PV
prevalence at which the
[email protected]

individual will consider gathering information about PV
because they might install
panels.
Second, these agents interact within an environment. That is,
the individuals are
able to perceive the situation in which they find themselves take
that situation into
account in their decisions and take actions that affect the
environment. Continuing
the example, the individuals are able to perceive the PV
prevalence in their loca-
tion, which allows them to check their perception of PV and the
respective policy
instruments.
Finally, ABM is a computational method that simulates
interactions over time.
Simulations allow “what if” questions to be tested quickly,
cheaply, and without
the ethical problems of setting up experiments. Provided the key
interactions are
properly represented in the model, the simulation can explore
the consequences of
different actions. In the policy context, for example, different
policy situations can
be explored and better understood. Modeling a system thereby is
often understood
as a first step to understanding the system to be modeled better.
It is important to recognize, however, that the results of a

simulation run will
not be suitable for forecasting (Antunes et al. 2008). The model
is a simplified
representation of the key relationships that exist in the real
world. That simplification
is what makes the model useful—knowledge about the real
world can be captured
and its consequences can be understood—but the model will not
be detailed enough
to support specific claims. In the terminology of Heath et al.
(2009), the model is
a mediator “used primarily to establish the capability of the
conceptual model to
represent the system and to then gain some insight into the
system’s characteristics
and behaviors” so as to understand potential implications of
different scenarios.
As a result, one of the main problems with respect to the
in a social simulation is to help them to understand the
advantages and limits of a
social simulation and its results in a better way.
That is why, in addition to an easy user interaction with the
social simulation in
general, it is important to help—by means of visual
representation—the developers
(domain and modeling experts) to communicate the limitations
and assumptions of
the simulation (and their impact on the simulation results) to the
policy analysts and
decision makers.

For better understanding what is considered “useful and easy to
use” for the poten-
tial users of the social simulator, we designed a questionnaire
which was given to
representatives of potential user groups (including the regional
policy makers, as
well as PV companies interested in the reaction of households
to different incentives
fostering the uptake of PV panels).
Besides the obvious requirement that the social simulator must
allow to analyze
the update of PV by individual households based on different
policy instruments,
the main result of the questionnaire was that the users want to
be able to look at
individual subregions (e.g., their electoral region) of the
Emilia-Romagna region
[email protected]
Fig. 15.7 Social simulation interface: (a) input parameters, (b)
policy instruments to be chosen,
(c) geographic representation of photovoltaic adoption, (d)
output of simulation: costs per policy
instrument (left), energy produced by photovoltaic panels
(middle), number of panels installed
(right)
and want to be able to look at both regional as well as national
policy instruments.
For these policy instruments, they want to be able to specify

funding levels. With
respect to the output, in particular adoption rates, the resulting
energy production
from PV and the costs associated with the adoption (per policy
instrument) were of
interest to the users. Furthermore, the speed of the simulation
was mentioned as one
attribute that can influence the utilization of the simulator.
Based on the above described user requirements, the social
simulator shown in
Fig. 15.7 was developed for the ePolicy project.
The social simulator interface consists of four areas, which will
now be explained
in more detail. Starting at the top left, as shown in Fig. 15.7a,
the first area is composed
of two buttons labeled setup simulation and run simulation as
well as several sliders
and a drop-down menu for specifying parameters of the
simulation.
As indicated by their labels, the first of the purple buttons sets
up the simulation
with all specified parameters and the different decision entities,
whereas the second
one can be used to run it. Running the simulation without
setting it up beforehand
is not possible, thus it is strictly necessary to press the setup
button before running
the simulation. The parameters that can be specified by the user
for the setup include
the region to be simulated (via the drop-down menu; as this is a
proof-of-concept
[email protected]

prototype currently available only for the Emilia-Romagna and
the Bologna region),
the initial percentage of people having PV (initial-percentage-
of-PV-users slider),
the average interest rate for credits in percent (credit-interest-
rate slider) as well as
the target percentage of people who should have PV (target-PV-
percentage slider).
In addition to these parameters, the users can specify which
policy instruments
should be available to the households being simulated. We
thereby distinguish two
regional and two national policy instruments all shown in Fig.
15.7b:
1. investment grants
2. contributions to interest rates for loans individuals have to
take in order to finance
a PV
3. feed-in tariffs, and
4. tax deductions on the PV investment.
For each of these instruments, using the on–off switches, the
user can specify whether
the instrument shall be considered (on) or not (off position of
the switch). Further-
more, for each instrument the user can specify the size of the
support by the policy
instrument.

On the top right-hand side of the simulation interface, a third
area can be seen,
which shows the geographic representation (in form of a map)
of the PV adoption.
Before setup, this map is black. However, once the region the
user is interested in
has been defined in the setup process (and this setup process has
been completed),
it changes to a map showing the selected region. In the map, for
easier orientation,
different red shades are used to reflect population densities
(with darker red shades
representing higher population densities and lighter shades
lower densities). The ex-
ample map shown in Fig. 15.7c displays the map of the Bologna
region. Furthermore,
in each area where the previously specified target PV threshold
is met, a green dot
is shown to indicate this success.
The social simulation can be run by clicking on run simulation
after the setup
of the simulation is completed. A message announcing this
setup completion is
displayed at the end of the setup process. When the simulation
is running, the area
at the bottom of the simulator interface shows the results of the
simulation in form
of three plots displaying outputs of the simulation run over
time: costs per policy
instrument (left), energy produced by PV panels (middle),
number of panels installed
(right; cf. Fig. 15.7d). These plots were indicated as desired
plots by the policy makers
questioned about the interface. The screens show the liabilities

the policy instruments
have generated in a particular (per instrument)2, the total
number of PV installations
over time, and the energy produced by these PV panels in terms
of kilowatt hour.
2 “Liability generated” that in a given year, also generated
future liabilities will be shown. In terms
of feed-in tariffs this means that all costs/liabilities resulting
from the long-term (e.g., 30 years)
feed-in-tariff contracts are completely in the year they were
generated in.
[email protected]
15.4.2.4 Findings
During the requirement analysis with the users of the simulation
system it became
clear that an intuitive visual interface to the simulation model is
of high relevance.
The users want to “play” with parameters of the computational
model to simulate the
PV adoption depending on the policy instruments chosen. That
way, the simulation
becomes more accessible to them compared to a written report
that only statically
describes the outcomes of the simulation for a number of given
parameterizations.
15.4.3 Urban Planning

The EC-funded project urbanAPI3 is seeking for ICT-enabled
tools that support the
policy-making process in modern urban planning—hence the
project’s full name
“Interactive Analysis, Simulation and Visualization Tools for
Urban Agile Policy
Implementation.” UrbanAPI supports both sides in the urban
policy and governance
system: the policy making and practitioner side as well as the
stakeholders and the
public. UrbanAPI provides a tool set that enables the city
planning authorities to
effectively use interactive simulation and visualization
instruments, and additionally
facilitates direct participation of stakeholders and citizens.
The term agile is used to express the interaction with the
different stakeholders,
in particular the public. Krämer et al. amend the policy cycle
from Fig. 15.3 and
replace policy adoption with a more general stakeholder
engagement phase (Krämer
et al. 2013). They propose that all stakeholders—urban
planners, decision makers as
well as citizens—participate in discussing policies in order to
find alternatives and
finally achieve consent.
Policy making in the area of urban planning works quite
similarly to the general
process described in Sect. 15.2.2. The policy cycle describes the
process of formu-
lating, implementing, and evaluating policies as it is done
nowadays. In the area of
urban planning, however, this process has recently started to
change. The top-down

driven policy-making process is gradually transforming to one
that is working bot-
tom up. In Europe, for example, citizens and other stakeholders
request to participate
in political decisions more and more often. Experience from the
past has shown that
modern urban planning cannot be done at the municipal
administration level alone
anymore without risking public discontent. Additionally,
stakeholders often have a
different view on certain issues. Incorporating their ideas and
proposals can improve
the policy-making process and finally lead to policies
experiencing higher acceptance
within the public.
Obviously, involving a large number of stakeholders in the
discussion requires
provisioning of participation tools that are widely accessible.
ICT tools developed
in the urbanAPI project are web based and run in a typical
Internet browser.
3 http://www.urbanapi.eu/.
[email protected]
In the following, we describe the different types of stakeholders
in the area of
urban planning and their respective needs and requirements. We
then present the
ICT-enabled tools developed in urbanAPI and their underlying

techniques and visual
design.
15.4.3.1 Involved Stakeholders
In the context of the urbanAPI project, a thorough process of
requirements gathering
for ICT-enabled tools as mentioned above was carried out
(Kahn and Ludlow 2013),
which contributed a lot of information to application
development (Dambruch et al.
2013) in the project. The stakeholder’s requirements were
analyzed and also which
types of stakeholders are typically involved or are to be
additionally addressed. The
term stakeholder refers in this context to individuals or an
organization that has a
vested interest in the results of urban planning and also the
process of policy modeling
itself.
In addition to the stakeholders from Chap. 3, several other
stakeholder groups
were identified. The main target audience as already mentioned
are:
• Policy analysts, the typical end users, who will actually
operate the software
applications to provide information, reports, etc.
• Domain experts such as architects, environmentalists, traffic
planners, and urban
planners
• Policy makers, the functional or political beneficiaries of the
information

generated by the applications for strategic planning
• Citizens as there is a growing demand for public participation
The interests of the users are manifold and distinct in several
ways. This is countered
by a requirements engineering approach which lead to an
exhaustive amount of
use cases elaborated together with the four case study cities
Bologna (Italy), Ruse
(Bulgaria), Vienna (Austria), and Vitoria-Gasteiz (Spain). Three
levels of stakeholder
involvement have been identified, each is targeted with a
specific application.
On the city quarter or neighborhood level, a 3D virtual reality
application can
be used to visualize alternative planning scenarios and support
evaluations by using
Internet technology to target to a broader audience, e.g., general
public or citizens.
By using interactive web technology, it is also possible to
gather direct feedback on
several topics without additional efforts.
On the citywide level, the public motion explorer provides
additional information
about the real movement of citizens over the daytime and
therefore contributes to
the analysis phase, and finally, can also be used in the
evaluation phase to asses the
impact of measures taken on the behavior.
Finally, on a region-wide level, the urban growth simulation
combines several
layers of socioeconomic and spatial information to create

simulations of the possi-
bilities of future developments which can be used to define
indicators and possible
hazardous conditions for the evaluation phase.
[email protected]
15.4.3.2 Underlying Techniques
In Gebetsroither (2009) and Krämer and Kehlenbach (2013), the
authors show how
a state-of-the-art agent-based simulation model can be applied
to urban change sim-
ulation and contribute to urban policy analysis. The simulation
includes geo-spatial
data as well as socioeconomic data to model the phenomena of
urbanization together
with some rules to describe a likely human behavior.
The public motion explorer application, described in Loibl and
Peters-Anders
(2012), uses data logged by mobile devices while connected to a
radio tower. Basi-
cally, modern smart phones have the capability to provide
location information based
on global positioning system (GPS), but often this functionality
is disabled or the
information is not logged due to regulations and privacy
protection issues. On the
other hand, the location and movement information can be
derived, if the location
of the radio tower to which a device is connected is known, but

with a much coarser
resolution. There are also some other side effects connected to
this approach which
have to be identified and corrected by preprocessing and data
cleansing to eliminate
potential hazards.
With the processed device data the application can provide
answers to questions
such as “Where are the people from district x at noon?” or
“Where do the people in
district x at noon come from?”
In the context of the urbanAPI project 3D visualization plays an
important role in
several aspects. First, a 3D city model is a natural way to
visualize changes and
alternative designs for a visual impact assessment. The transfer
to a virtual digital
domain is straightforward, but depending on the data available.
For example, if a
sophisticated 3D city model is available, an architect can
provide a virtual 3D model
of a new building project. This building model can then be
integrated and a visual
impact assessment, such as the analysis how the new building
would cast his shadow
on other existing buildings, can be done.
Second, enabling the users to leave some feedback directly in
the 3D visualization,
or at least on the same website, opens up new possibilities in
interaction. For example,
the users can change a 3D scenario by putting in some custom

objects which seem
to be appropriate for them. Other options can be the inclusion of
textual feedback or
filling out some questionnaires about the scenarios.
Finally, the 3D scenario can provide an integrated view of
several crosscutting
concerns of all applications. For example, the simulation results
from the urban
growth simulation can be related to the public motion analysis
tracks in the 3D city
model.
The benefits of such an integrated view is that data can be
interpreted in a context
that is a more concrete and visually compelling, thus easier to
understand by people
with non-IT background. It can also be used to amend city
planning scenarios and
impact assessment for planned actions.
[email protected]
To achieve the aspects above the software design decision was
taken to build a
web-based portal application (Dambruch et al. 2013) which is
based on a concept
that uses reusable and customizable components. The
components we created are
designed to fulfill the requirements and use cases identified in
(Kahn and Ludlow
2013), but they are generalized and easily customizable in such

a way, that also other
use cases can be fulfilled. All components can be arranged
visually on the web pages
that are created for the evaluation projects. Especially, the
adoption of a role-based
security concept for the users gives a lot of flexibility to
address different target
audiences. The policy analysis can therefore tailor the
visualization to the level of
expertise and, for example, can include additional information.
On the other hand,
confidentiality of information can be assured. So, it is easy to
map the stakeholders
into several user groups to which different access rights may be
granted. For example,
citizens may not be granted to change things, but only to
annotate designs for a certain
project. Moreover, the portal software provides many
components off the shelf such
as content management systems or blogs which can be used
together with the 3D
components to improve the user experience further.
Two of the prominent use cases are an architectural competition
scenario and a
comparison of design alternatives. In both cases, the
stakeholders should have the
possibility to place some annotation on an arbitrary point in the
3D scene. Also, the
position of the viewer should cover several perspectives, such
as viewing the scene
as a pedestrian or getting an overview from a helicopter
perspective. However, the
key advantage of the applications is that the viewer can move
interactively by just
clicking and moving the mouse or a similar gesture on a touch

device to move around
in the 3D scene freely.
The next level of interaction is to leave some feedback by
directly putting some
annotations in the 3D scene. Whenever a user places an
annotation, the exact position
of the viewer along with the point the user was looking at when
placing the annotation
is recorded, so that the planner can easily take the point of view
of the users. The
user has the option to enter simple text in a pop-up window that
is put directly into
the 3D display, therefore not requiring distractive mouse
movements to enter the
text. Other components can access the annotations made and
present it in a different
format, which may be more appropriate, for example, a list
view.
The third level is then to modify the 3D scene itself by placing
additional or
removing existing objects.
In Fig. 15.8, an example for the design comparison use case is
given. The page
displayed is made up of four components: The two 3D
components are prominently
placed side by side, each using the same basic 3D city model
but with alternative
buildings proposed. On the lower left side, a navigation bar
with interesting per-
spectives is given which navigates both 3D views in sync. On
the lower right side,
a simple vote component is placed, so that stakeholders can
select which alternative

they like best.
[email protected]
Fig. 15.8 A portal page displaying an A/B comparison of design
alternatives
15.4.3.4 Findings
The traditional policy-making process in the area of urban
planning is transforming
from a top-down approach to one that works bottom-up. The
public’s demands have
changed and people want to take part in political discourse more
and more often.
However, at the moment policies are only discussed on a
political level and citizens
are rather informed than involved. At the same time, there is a
gap in the availability
of urban planning to anybody outside the city administration.
ICT tools such as the web-based solution of urbanAPI help
mitigate this prob-
lem. Results of complex calculations such as the public motion
analysis and the
agent-based urban change simulation are visualized in a way
that is understand-
able by non-IT personnel. The 3D visualization adds
attractiveness that increases
acceptance of the solution among the citizens. At the same time,
the solution allows
stakeholders to participate in the discussion by contributing

feedback or new ideas.
This happens in a controlled environment as the web-based
portal application is
highly configurable and individual modules can be customized,
enabled, or disabled
by the urban planners according to the scenario they wish to
present and according
to the degree of participation they are aiming for.
[email protected]
Within the time frame of the urbanAPI project, the developed
tools are regularly
evaluated in depth by users from the partner cities of Bologna
(Italy), Ruse (Bulgaria),
Vienna (Austria), and Vitoria-Gasteiz (Spain). We were able to
derive the following
results from these evaluations which we consider key for the
success of ICT-enabled
tools in participatory urban planning.
• The data quality has to be reasonably high. The 3D
visualization has to be ap-
pealing in order to improve acceptance by citizens. This is only
possible if it is
based on high-quality geo-spatial data like textured 3D building
models or high-
resolution digital terrain models and aerial images. While many
European cities
already maintain a 3D city model, at the moment they often
miss textures or fine
geometrical details which would make the visualization more

realistic.
• Usability plays an important role for ICT tools that are made
available to a large
audience. The tools can only gain high acceptance if they can be
used easily
and without barriers. The user interface has to be clear and
understandable. The
software should allow stakeholders to participate and contribute
without too much
effort. Otherwise, the software will not be used and the
advantages of participatory
urban planning are lost.
• In addition to that, the ICT tools have to be portable in order
to run on a wide
range of systems from desktop PCs to tablets and mobile
devices. This improves
the acceptance and lowers the barriers, which stakeholders have
to take before
they can participate in urban planning.
15.4.4 Summary of Case Studies
Figure 15.9 summarizes the presented case studies with a short
task description, the
applied modeling techniques, the relevant data types, the
implemented visualization
techniques, and the involved stakeholder. The table shows that
the selected case
study differ in nearly all of these characteristics. From this, we
conclude that for
policy analysis a broad range of scenarios exist that need to be
tackled with different
strategies. We already stated that a one-fits-all-solution from
the field of information

visualization does not exist. For each problem addressed in a
case study, a specific
solution needs to be designed in order to support the users in
the best possible way. The
heterogeneity of case studies in the field of policy analysis even
amplifies this fact.
Therefore, we strongly recommend to conduct a precise problem
characterization
and analysis of tasks to be solved with the technologies prior to
their implementation.
For this, all relevant stakeholders need to be involved. Design
study methodologies
in the field of information visualization and visual analytics
already address this
challenge. However, from our point of view these
methodologies need to be adapted
to the specific characteristics of policy analysis.
[email protected]
Fig. 15.9 Summary of case studies
15.5 Conclusion
In this work, we presented a novel approach to tackle the
challenges of the policy
paradox. This paradox describes the fact that despite the
acknowledged importance
of scientific evidence for political decision making, the
knowledge gained from
scientific disciplines is seldom considered in policy making. In
our approach, we

proposed a concept that addresses this problem by introducing
tion technologies to the policy-analysis field. Therefore, we
described the disciplines
of information visualization and policy analysis. We also
identified capabilities
provided by information visualization and challenges faced by
policy analysis.
Information visualization is defined as “the use of computer-
supported interac-
tive, visual representations of abstract data to amplify
cognition.” Its purpose is the
exploration, sensemaking, and communication of knowledge
hidden in data. Policy
analysis deals with the analysis of societal problems, and
alternative policy options
to be chosen by policy makers that may serve as solutions to
these problems. For the
generation of these policy options, scientific advice is proposed.
The main challenges
of policy analysis lie in an effective exploration, and
sensemaking of policy options
by the policy analysts, as well as a comprehensible
communication of the analysis
results to the policy makers who finally decide upon the options
to be chosen.
From the capabilities of information visualization on the one
hand, and the chal-
lenges of policy analysis on the other hand, we identified
synergy effects resulting
from the combination of these two fields. With this motivation,
we proposed a method
how to apply information visualization to the field of policy
analysis. Therefore, we

[email protected]
identified relevant stakeholders in the policy-making process.
We defined possible
collaborations between these stakeholders and hurdles that have
to be faced. Finally,
we sketched a methodology how to structure the development of
such science–policy
interfaces supported by information visualization.
As a last facet of our contribution, we presented three case
studies that have been
conducted in two European research projects dedicated to the
field of policy mod-
eling. These studies basically implemented the concept
described in this approach.
In the case studies, technologies from the scientific fields of
agent-based simulation,
optimization, and geo-spatial data modeling have been applied
to the field of policy
making in order to generate and analyze policy options for a
given societal prob-
lem. All case studies provided access to the computational
models by information
visualization technologies. This enabled even non-IT experts to
interact with com-
plex models and generate policy options. Moreover, the
visualization tools could be
used to communicate and discuss the results derived from the
policy analysis. The
case studies showed that our provided concept can serve as an

approach to further
explore the synergy effects between information visualization
and policy analysis.
We believe that our provided concept stimulates and motivates
further research and
discussions in this new, interesting, and not yet extensively
studied interdisciplinary
field.
Acknowledgments Research presented here is partly carried out
within the project “urbanAPI”
(Interactive Analysis, Simulation and Visualisation Tools for
Urban Agile Policy Implementation),
and “ePolicy”(Engineering the Policy-Making Life Cycle)
funded from the 7th Framework Pro-
gram of the European Commission, call identifier: FP7-ICT-
2011-7, under the grant agreement no:
288577 (urbanAPI), and no: 288147 (ePolicy), started in
October 2011.
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Schneider V, Janning F (2006) Politikfeldanalyse. Akteure,
Diskurse und Netzwerke in der
öffentlichen Politik. VS Verlag für Sozialwissenschaften,
Wiesbaden
Sedlmair M, Meyer MD, Munzner T (2012) Design study
methodology: reflections from the trenches
and the stacks. IEEE Trans Vis Comput Graph 18:2431–2440
Shneiderman B, Bederson B (2003) The craft of information
visualization: readings and reflections.
Morgan Kaufmann, San Francisco
Shulock N (1999) The paradox of policy analysis: if it is not
used, why do we produce so much of
it? J Policy Anal Manag 18(2):226–244
Thomas JJ, Cook KA (2005) Illuminating the path: the research
and development agenda for visual

analytics. IEEE Computer Society Press, Los Alamitos
Weimer DL, Vining AR (2005) Policy analysis: concepts and
practice. Prentice Hall, Upper Saddle
River
Whitley KN (1997) Visual programming languages and the
empirical evidence for and against. J
Vis Lang Comput 8(1):109–142
[email protected]
Chapter 16
Analysis of Five Policy Cases in the Field of
Energy Policy
Dominik Bär, Maria A. Wimmer, Jozef Glova, Anastasia
Papazafeiropoulou
and Laurence Brooks
Abstract While the twentieth century is considered as the
century of population
explosion and burning of fossil fuels, environmental policies
and the transition to
effectively use renewable energy sources have become a priority
of strategies in re-
gions, countries and internationally. Different projects have
been initiated to study
the best suitable transition process towards renewable energy.
In addition, an increas-
ing number of climate change and energy policies is being
formulated and released at
distinct levels of governments. Many of these policies and
projects pursue the aim of

switching from energy sources like fossil fuels or nuclear power
to renewable energy
sources like solar, wind or water. The aim of this chapter is to
provide foundations
of policy implementation and particular methods as well as to
investigate five policy
implementation cases through a comparative analysis.
16.1 Introduction
The twentieth century was the century of population explosion
and burning of fossil
fuels, which led to the highest pollution in history causing
climate change and bio-
diversity loss (Helm 2000). However, the pollution and its
consequences have only
been recognised in the recent decades and environmental
policies are now of high
M. A. Wimmer (�) · D. Bär
D. Bär
J. Glova
Technical University Kosice, Kosice, Slovakia
A. Papazafeiropoulou · L. Brooks
Brunel University, Uxbridge, UK
L. Brooks
10.1007/978-3-319-12784-2_16

[email protected]
356 D. Bär et al.
priority to society, companies and policymakers (Helm 2000).
Owing to this, gov-
ernments all over the world have launched projects to improve
the climate situation.
The problem scope dealt with in this chapter concerns climate
change and policies
dealing with topics like sustainable energy management and the
use of renewable
energy sources. Many projects pursue the aim of switching from
energy sources like
fossil fuels or nuclear power to renewable energy sources like
solar, wind or water. So
on the one hand, the aim of policies is to replace polluting ways
of power production
with green technologies and, on the other hand, to reduce
energy consumption by
using innovative technologies.
Climate change affects the whole world and is a very huge
organisational, tech-
nical and financial challenge, which is why industrial countries
are expected to take
responsibility and initiatives to counteract the current climate
development. In the
cause of this, these countries may serve as role models for other
countries to join in
improving the climate situation.
In this chapter, five projects and cases dealing with policies of

climate change
and energy use are investigated. First, a theoretical ground is
provided about policy
implementation like theories of policy implementation or
methods of implementa-
tion in order to establish a common understanding.
Subsequently, the projects are
investigated and analysed via comparative analysis, performed
in the frame of the
eGovPoliNet1 initiative. The selection of the policy cases was
based on the authors’
access to information of the cases of policy implementation. A
framework2 has been
developed for the comparative analysis that supports pointing
out major aspects and
core information about the projects in order to have a brief
overview and to make the
projects comparable to each other. The framework provides a
set of categories which
need to be filled in to describe and analyse the projects, starting
from an abstract and
objectives, which gives both a brief summary of the policy case
under investigation
and its context and objectives, followed by a tabular overview
structured along (a)
metadata providing general information such as name and
duration of the project,
type of project (piloting case of a project or implementation
project), domain (i.e.
referring to the particular sector the policy is dealing with) and
references; and (b)
conceptual aspects of interest in the comparison providing more
specific informa-
tion such as specific policy domain and particular policy
addressed, targeted users
and/or stakeholders, an indication of the complexity of the

policy case, theories used
to develop the policy, methods used, supporting technology
frameworks and tools
use3, simulation models developed4, project outcome, links to
other projects, trans-
ferability of solutions and techniques as well as concluding
recommendations of the
1 eGovPoliNet—Building a global multidisciplinary digital
governance and policy modelling
research and practice community. See http://www.policy-
community.eu/ (last access: 28 July 2014).
2 The framework is published in Annex I to technical report D
4.2 of eGovPoliNet: Maria A.
Wimmer and Dragana Majstorovic (Eds.): Synthesis Report of
Knowledge Assets, including
Visions (D 4.2). eGovPoliNet consortium, 2014, report
available under http://www.policy-
community.eu/results/public-deliverables/ (last access: 28 July
2014).
3 A comparative analysis of tools and technologies is provided
in Kamateri et al. (2014).
4 A comparative analysis of simulation models is provided in
Majstorovic et al. (2014).
[email protected]
16 Analysis of Five Policy Cases in the Field of Energy Policy
357
project or case. Based on the identified information from the
comparative analysis,
the projects are briefly discussed and compared to each other.
Moreover, the results

and benefits of the projects are described and the possibility of
transferring the used
approaches to other domains, projects or cases is investigated.
The following three research questions drive the comparative
investigations of
projects and cases: (1) what approaches of policy modelling are
used in implementing
public policies, how do these approaches differ and are these
approaches best fit for
the purposes of the policy cases? (2) How can the implications
of selecting a particular
approach to ensure successful policy implementation be
measured and what lessons
can be drawn from the case analyses? (3) How easily can the
approaches of the policy
cases investigated in the chapter be adopted to other countries,
other policy domains
and even other thematic policy areas?
The chapter is structured as follows: In Sect. 16.2, theoretical
grounds and
definitions about policy implementation are given in order to
provide a common
understanding and an overview of the purpose of policy
implementation as well as
of policy instruments used in climate change and renewable
energy policy. Thereafter,
the comparative analysis framework is introduced regarding its
structure and con-
tent. Using this framework, projects and cases of the field are
analysed in Sect. 16.4
and subsequently discussed and compared to each other,
including a brief reflection
of research and practice implications, and further research needs
(Sect. 16.5). The

chapter ends with some concluding remarks in Sect. 16.6.
16.2 Theoretical Grounds of Policy Implementation
Buse et al. argue that policy implementation refers to the
execution of a formulated
policy, which means turning theory into practice. When turning
policy into practice,
it is common to observe a gap between formulated and
implemented policy as the
policymakers hand over the responsibility for the
implementation to policy imple-
menters who may have a different understanding of the policy
(Buse et al. 2012). The
policy formulation is seen as a political activity and the
implementation as techni-
cal, administrative or managerial activity. The gap between
policymakers and policy
implementers causes a lack of control from the policymaker
view regarding the way
the policy is implemented.
Implementation of public policy is always serving a purpose and
is put in place in
order to change things for the better and improve situations that
seem to be problem-
atic. There are different ways that decisions for a policymaking
process to start take
place (Lindblom 1968). An obvious but not always the most
common way is through
public demands. These are demands from the general public
(known as “bottom up”
initiatives) and can be very influential especially for important
issues such as public
health and safety. Nowadays, the general public is educated and
informed at a level

that gives them the power to be able to mobilize and in some
cases demand changes
at a public policy level. Another reason that policy
implementation is starting to for-
mulate is pressure from special interest groups that can
influence policies promoting
[email protected]
358 D. Bär et al.
public welfare. For example, chambers of commerce are
typically supporting interest
of their business members, while Green Peace will express
concerns and will try to
address environmental issues, promoting the implementation of
public policies for
environmental protection (Portney and Stavins 2000).
According to the policy cycle, the implementation of a policy
follows some basic
steps such as agenda setting (problem identification), policy
formulation, decision-
making, implementation and evaluation (Nakamura 1987). The
first stage of this
cycle where the problem is identified is the stage where the
purpose of the policy is
formulated and is recognised as the starting point of the cycle.
During this agenda
setting, all stakeholders are or need to be participating and
voicing concerns as well
as possible remedies for the problem at hand. This stage was
typically initiated in the
past by government agencies but latest studies show that a

number of other entities
influence this stage (Young and Mendizabal 2009). These could
be the media, think
tanks, policy research institutes and other academic or business
organisations.
The final outcome of the agenda setting stage is a purpose
statement where poli-
cymakers state the problem as well as the desired outcome of
the proposed strategy.
Examples of such statements can be details of a costal policy
and its desired out-
comes (NZPCS 2010). The desired outcomes of policymaking
are always aiming
at improving the problem area in question and ultimately
improve the welfare of
citizens at large.
An important but not always well-executed stage of the policy
implementation
cycle is that of the evaluation of the policy outcomes. This is
the time when the
designers of a public policy have finalised the implementation
and are in a posi-
tion to evaluate whether the actions taken improved the
situation and contributed
to the welfare of the target population. Evaluation is a
retrospective assessment of
government initiatives, and it usually measures the success of
activities that they
are still taking place and are ongoing. Evaluation seems to be a
controversial and
hard-to-implement strategy that needs to be based on peoples’
perceptions, opinions
and judgments while at the same time needs to be objective
enough to provide some

insights into the complexities of public interventions (Vedug
1997).
As this chapter focuses on policy cases of climate change and
renewable energy
policies, the next two sections introduce instruments used in
these two policy domains
for steering and governing.
16.2.1 Instruments for Climate Change Policy
For the implementation and application of climate and energy
policies, Oikonomou
and Jepma present different instruments. They acknowledge that
categorizations of
policies differ within literature and therefore they make use of
general studies from
OECD, IPCC, etc. They have identified the following policy
instruments (Oikonomou
and Jepma 2007):
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359
Table 16.1 Typology of policy instruments for global climate
change summarised from Stavins
(1997)
Types of instruments
Categories
Command-and-control (and

voluntary in domestic)
Market-based
Domestic Energy efficiency standards Charges, fees and taxes
(carbon taxes, taxes on fossil
fuels, other energy taxes)
Product prohibitions Tradable rights (tradable
carbon rights, tradable
“emission reduction” credits)
Voluntary agreements
International Uniform energy efficiency
standards
Charges, fees and taxes
(harmonized domestic taxes,
uniform international tax)
Fixed national emission limits Tradable rights (international
tradable permits, joint
implementation)
• Financial measures, where the government can change the cost
of energy through
taxation and subsidy policies. The following types of taxation
are distinguished:
emission charges/taxes, user charges, and product charges/taxes.
• Legal or regulatory instruments, where governments can set
legal requirements
with financial penalties for non-compliance.
• Organisational measures are commitments undertaken by

power producers or
industries in consultation or negotiation.
• Certificates or marketable (tradable) permits or quotas.
Stavins identifies two distinct categories of policy instruments
that are particular in
global climate change: The first category—domestic policy
instruments—enables
individual nations to achieve their specific national or local
targets and goals.
The second category—bilateral, multilateral or global (or in
general international)
instruments—can be employed jointly by groups of nations
(Stavins 1997). The au-
thor developed a typology of these two categories of policy
instruments for global
climate change which is summarised in Table 16.1.
16.2.2 Policy Instruments for Renewable Energy
Energy policy is closely linked to climate change as the energy
sector has high
potential for reducing greenhouse gas (GHG) emissions. There
is no universal policy
prescription for supporting renewable energy. Particular nations
are typically unique.
The most suitable policy instruments in one country may not be
appropriate for
another country. Instead of a single policy to achieve all of the
policy objectives,
it is more useful to consider a policy portfolio approach or a
policy toolkit. Policy
instruments are means by which policy objectives are pursued
(Howlett 2009). Azuela

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360 D. Bär et al.
and Barroso (2011) and IPCC (2012) put forward in the
following five categories of
policy instruments for renewable energy:
• Regulations and standards can promote renewable energy via
direct support (with
policy objectives in removal of non-economic barriers and in
increasing demand
for renewable energy) and indirect support (with policy
objective in restrictions
on fossil fuel power).
• Quantity instruments—market-based instruments that define a
specific target or
absolute quantity for renewable energy production.
• Price instruments—reduce cost- and pricing-related barriers by
establishing
favourable price regimes for renewable energy relative to other
sources of power
generation, e.g. fiscal incentives (production/investment tax
credits, public invest-
ment, loans or grants; capital subsidy, grant or rebate; increase
in taxes on fossil
fuels; reductions in sales, energy, CO2, value-added tax (VAT)
or other taxes)
and feed-in tariffs (a preferential tariff; guaranteed purchase of
the electricity
produced for a specified period; guaranteed access to the grid).

• Public procurement—governments are often a very large
energy consumer,
whereby their purchasing and procurement decisions affect the
market.
• Auction—an auction is a selection process to allocate goods
and services compet-
itively, based on a financial offer. Specifically in a “reverse
auction”, electricity
generators bid their supply to distribution companies and the
process is designed to
select the lowest prices. Auctions can be a very attractive
mechanism for attracting
new renewable energy supply.
16.3 Approaches to Policy Implementation
Policies can be implemented in different ways. Subsequently,
four approaches of pol-
icy implementation are presented: top-down approach, bottom-
up approach, macro-
and micro-implementation and principal–agent theory. They
exemplarily point out
how policies can be implemented, what actors are involved
along the implementation
process and how they affect the policy, its implementation and
outcome.
16.3.1 Top-Down Approach
The top-down approach was developed between the 1960s and
1970s by policy ana-
lysts in order to provide policymakers with a better
understanding of how to minimise
the gap between the formulated and the implemented policy
(Buse et al. 2012).

This approach describes a linear process from policy
formulation to implementa-
tion, where policies are communicated from policymakers to
executing entities like
authorities, which turn the policy into practice. To successfully
implement a pol-
icy, the policy goals need to be clearly described and
understood by all involved
[email protected]
361
actors. Moreover, the required resources for the implementation
need to be avail-
able, a communication and command chain needs to be
established and the whole
implementation process needs to be controlled (Pressman and
Wildavsky 1984).
The top-down approach may be criticised as it focuses mainly
on the decision
and on policymakers while it does not sufficiently include other
involved actors and
factors that are part of the policy implementation process. The
implementation is seen
as an administrative process and does not include the expertise
of local experts who
eventually implement the policy. Thus, the approach is difficult
to apply in situations
that are not driven by a single leading actor but where multiple
actors participate
in the policy implementation process (Buse et al. 2012). For this

approach, Gunn
formulated ten pre-conditions which should be fulfilled to
successfully implement a
policy (Gunn 1978 and Hogwood and Gunn 1984). However,
Buse et al. criticize that
hardly all pre-conditions could be fulfilled at once and that
policy implementation in
reality is too complex and thus cannot be covered with the top-
down approach and
its pre-conditions (Buse et al. 2012).
16.3.2 Bottom-Up Approach
The bottom-up approach was developed from the criticism of
the top-down approach,
which focuses on policymakers and neglects the other actors
involved in the imple-
mentation process. The bottom-up approach focuses on policy
implementers as they
play an important role in the policy implementation process as
active participants
who give feedback to the policymakers and have high influence
on the actual pol-
icy implementation (Buse et al. 2012). Lipsky studied the
behaviour of “street-level
bureaucrats” (teachers, doctors, nurses, etc.) in relation to their
clients in the 1970s
(Lipsky 1980). In his studies, Lipsky showed that even people
in highly rule-bound
environments could reshape parts of public central policy for
their own ends (Buse
et al. 2012). In consequence of these findings, researchers found
that even if all
pre-conditions for the top-down approach (see Gunn 1978) were
fulfilled, policies
could still be implemented in a way which was not planned by

the policymakers
(Buse et al. 2012).
16.3.3 Macro- and Micro-implementation
When governments execute policies in order to influence local
authorities, it is called
macro-implementation (Berman 1978). However, local
authorities need to transfer
governmental policies into their own local policies, which is
then called micro-
implementation. This approach can be understood as a two-
phase implementation
method. In the first phase, the overall policy is made by
governmental policymakers
in order to address certain issues and to pursue defined goals.
Local authorities and
policymakers then need to adopt the overall policy and
transform it into a policy that
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362 D. Bär et al.
is manageable and suited for local application. This
transformation process may lead
to a gap between the formulated governmental policy and the
executed local policy,
which makes this approach quite similar to the bottom-up and
top-down approaches
respectively. All these approaches carry the risk of a mismatch
between formulated
and implemented policy as is extensively elaborated, e.g. in
James et al. (1999).

16.3.4 Principal–Agent Theory
According to Buse et al., the principal–agent theory argues an
“implementation
gap” as the inevitable consequence of the governmental
institution structure. Policy-
and decision-makers (“principals”) delegate responsibility for
the implementation
of policies to their officials (“agents”), whom they cannot
completely monitor and
control. These “agents” have discretion in how they work on
implementing the policy
and may also see themselves from a different view than the
policymakers. Thereby
policy implementers may interpret the policy in a different way
than the policy
formulators which leads to implementing the policy in a
different way than it was
actually meant to be implemented (Buse et al. 2012).
Policy implementation is a complex process that is influenced
by many actors
(Turnpenny et al. 2005). From its formulation until its
implementation, the policy
passes through different levels of authorities and is handled by
different actors. It is
formulated by governmental policymakers and then passed on to
local policymakers
and authorities who have to adapt the overall policy in order to
implement it suc-
cessfully in their local structures. Along this implementation
process, governmental
policymakers are not completely able to monitor and control the
implementation, as
local policymakers need to take care of local policy

peculiarities. Moreover, the local
policymakers may understand the overall policy in a different
way than it is meant
to be understood. These factors, often inevitable, lead to a so-
called implementation
gap (James et al. 1999). This gap is the consequence of the
different understandings
and backgrounds of the actors who are involved in the
implementation process of
the policy. This issue needs to be addressed in order to
minimise the gap between
formulated and implemented policy, so that policies are
implemented the way they
are meant to be implemented.
In the next section, five different cases of policy
implementation are being studied
and compared to investigate the three research questions
formulated in the introduc-
tory chapter. The theoretical foundations presented in Sect. 16.2
and the distinct
approaches introduced in this section provide the underlying
understanding for the
comparative analysis of cases of climate change and renewable
energy policy.
[email protected]
363
16.4 Investigating Five Cases of Climate Change and
Renewable
Energy Policy

Based on the research foundations and investigation of distinct
approaches to policy
implementation, the projects and cases introduced in this
section provide examples of
how policies are being implemented or how the implementation
process of policies
concerning climate change and energy matters is supported. In
order to analyse
the projects and cases and to describe them in detail, the
framework developed
in eGovPoliNet as outlined in the introductory section is used
(i.e. describing the
cases along abstract and objectives, metadata and relevant
conceptual aspects). This
framework offers the possibility to point out major aspects and
characteristics of the
projects and cases and to make them comparable along those
aspects. The five cases
chosen and analysed via the comparative analysis template are:
• Assessing the EU policy package on climate change and
renewables
• German nuclear phase-out and energy transition policy
• KNOWBRIDGE: Cross-border knowledge bridge in the
renewable energy
sources (RES) cluster in East Slovakia and North Hungary
• Kosice Self-governing Region’s (KSR’s) strategy for the use
of renewable energy
resources
• MODEL: Management of domains related to energy in local
authorities
The projects and cases have been selected on the basis of

relevance for the domain
of study of this chapter and of sufficient access to information
to carry out the
comparative analysis.
16.4.1 Assessing the EU Policy Package on Climate Change and
Renewables
Abstract: As stated by Capros et al., the EU aims to reduce
GHG emissions at least by
20 % in 2020 compared to 1990. Likewise, 20 % of energy
needs should be covered
through renewable energy sources—see EU (2007). The
research of Capros et al.
developed an energy model to assess the range of policy options
that were debated
to meet the two targets of the EU policy. Options of the energy
policy explore and
assess trading of renewable targets, carbon trading in power
plants and industry and
the use of the Clean Development Mechanism to improve cost-
efficiency. In the
research assessment of the EU energy policy, the authors also
examined fairness by
analysing the distribution of emission reduction in the non-
emission trading sector,
the distribution of CO2 allowances in the emission trading
sector and the reallocation
of renewable targets across Member States. The authors assess
the overall costs of
meeting both targets being in the range of 0.4–0.6 % of gross
domestic product (GDP)
in 2020 for the EU as a whole. It is also argued that the
redistribution mechanisms
[email protected]

364 D. Bär et al.
employed would significantly improve fairness compared to a
cost-effective solution
(Capros et al. 2011).
The main objectives of the EU policy package on climate
change and renewables
contain (Capros et al. 2011; EU 2007):
• Reducing unilaterally GHG by 20 % in 2020 compared to 1990
levels (including
an offer to increase this target to −30 % given a sufficiently
ambitious international
agreement)
• Supplying 20 % of energy needs by 2020 from RES, including
the use of 10 %
renewable energy in transport
• Giving priority to energy efficiency in all energy domains
The assessment study of Capros et al. aimed at developing and
testing an energy
model to assess the range of policy options discussed to meet
the two targets of the
EU policy (Capros et al. 2011). Table 16.2 outlines the main
aspects of the EU policy
package and the respective assessment study of Capros et al.
(2011).
16.4.2 German Nuclear Phase-Out and Energy Transition Policy

Abstract: Following the Fukushima disaster in Japan in March
2011, the German
chancellor Merkel declared a 3-month moratorium on nuclear
power plants, in which
checks took place and nuclear policy was reconsidered.
Subsequently, all eight nu-
clear power reactors which began operation in 1980 or earlier
were immediately shut
down. Although the Reactor Safety Commission reported that
all German reactors
were basically safe with regard to natural or man-made
dysfunction, the government
decided to shut down the nine remaining reactors until 2022 and
approved construc-
tion of new coal and gas-fired plants despite retaining its CO2
emission reduction
targets, as well as expanding wind energy. Germany was
expected to be dependent on
energy imports after the shutdown of the first eight reactors but
it still kept exporting
energy as the energy production from wind, solar and hydro
keeps growing. So far,
the use of renewable sources is quite expensive and shouldered
by tax payers and
consumers. Moreover, it is dependent on wind and sunlight
which are not always
available (Moore 2013).5
The main objectives of Germany’s nuclear phase-out and energy
transition policy
can be summed up as:6
• To accelerate the transformation of Germany’s energy system
to RES (nuclear
power serving only as “bridging technology” to transform)

5 See also the following two websites (last access: 30 July
2014): http://www.dw.de/power-exports-
peak-despite-nuclear-phase-out/a-16370444 and
http://energytransition.de/.
6 See also the following two websites (last access: 30 July
2014): http://www.dw.de/power-exports-
peak-despite-nuclear-phase-out/a-16370444 and
http://energytransition.de/.
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365
Table 16.2 Analysis of metadata and conceptual aspects of the
study assessing the EU policy
package on climate change and renewables
Metadata
Name Assessment of EU policy package on climate change and
renewables
Duration EU policy package: 2007–2020
Domain Climate change and energy sectors
Project type Policy implementation (of EU policy package) and
assessment (for
the research study)
Reference(s) For the EU climate and energy policy package: EU
(2007, 2008)
and online under:

http://ec.europa.eu/clima/policies/package/index_
en.htm (last access: 30 July 2014)
For the assessment study: Capros et al. (2011), PRIMES model
of
NTUA
(http://www.e3mlab.ntua.gr/index.php?option=com_content
&view=category&id=35%3Aprimes&Itemid=80&layout=default
&
lang=en, last access 30 July 2014), GAINS model of IIASA
(http://www.iiasa.ac.at/rains/C&E_package.html?sb=19, last
access
30 July 2014)
Conceptual aspects
Implementing which
policy
Energy policy (emissions and renewable energy sources)
Users/Stakeholders European Commission, EU Member States,
industry, general public
Complexity Very high due to involvement of different actors
and dependency of
many interrelated factors
Theory(s) useda Macro-modelling with PRIMES energy system
model, which
implements partial equilibrium (energy system and markets);
and
with GAINS model of IIASA, which models non-CO2 GHGs
and
derives emissions of non-CO2 GHGs from a series of activity
indicators, referring among others to agriculture and to specific
industrial processes

Method(s) useda Scenario modelling and simulation modelling
(cross-modelling of
interacting targets) 150 energy scenarios with different carbon
and
RES values were investigated by using the PRIMES model for
the
period 2005–2030 for all EU Member States
Supportive technology
frameworks and tools
useda
PRIMES energy system model of NTUA
GAINS model of IIASA
Model(s) generateda PRIMES model, GAINS (greenhouse gas—
air pollution
interactions and synergies) model
Project outcomea Eleven scenarios with different starting
positions and influences
Analysis of the different scenarios
[email protected]
http://ec.europa.eu/clima/policies/package/index_en.htm
http://ec.europa.eu/clima/policies/package/index_en.htm
http://www.e3mlab.ntua.gr/index.php{?}option=com_content&v
iew=category&id=35{%}3Aprimes&Itemid=80&layout=default
&lang=en
&lang=en

&lang=en
366 D. Bär et al.
Conceptual aspects
Transferability of
solutions and
techniquesa
Partially given as the models can serve as blueprints for similar
models and as source for exploring further aspects of climate
change and energy policy. The transferability is, however,
restricted to the very policy domain
Concluding recommen-
dations of the projecta
Meeting the targets in the EU is an ambitious effort and requires
considerable adjustm

Running head Multi-actor modelling system 1Multi-actor mod.docx

Running head Multi-actor modelling system 1Multi-actor mod.docx

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