ComputationalModellingofPublicPolicyReflectionsonPractice.docx

ComputationalModellingofPublicPolicy:
ReflectionsonPractice
Nigel Gilbert1, Petra Ahrweiler2, Pete Barbrook-Johnson1,
Kavin
PreethiNarasimhan1,HelenWilkinson3
1Department of Sociology, University of Surrey Guildford, GU2
7XH United Kingdom
2InstituteofSociology,JohannesGutenbergUniversityMainz,Jako
b-Welder-Weg20,55128Mainz,Germany
3Risk
Solution
s, Dallam Court, Dallam Lane, Warrington, Cheshire, WA2
7LT, United Kingdom
Correspondence should be addressed to [email protected]
Journal of Artificial Societies and Social Simulation 21(1) 14,
2018
Doi: 10.18564/jasss.3669 Url:
http://jasss.soc.surrey.ac.uk/21/1/14.html
Received: 11-01-2018 Accepted: 11-01-2018 Published: 31-01-
2018

Abstract: Computationalmodelsare
increasinglybeingusedtoassist indeveloping,
implementingandevalu-
ating public policy. This paper reports on the experience of the
authors in designing and using computational
models of public policy (‘policy models’, for short). The paper
considers the role of computational models in
policy making, and some of the challenges that need to be
overcome if policy models are to make an e�ec-
tive contribution. It suggests that policy models can have an
important place in the policy process because
they could allow policy makers to experiment in a virtual world,
and have many advantages compared with
randomised control trials and policy pilots. The paper then
summarises some general lessons that can be ex-
tractedfromtheauthors’experiencewithpolicymodelling.
Thesegeneral lessonsincludetheobservationthat
o�enthemainbenefitofdesigningandusingamodelisthatitprovides
anunderstandingofthepolicydomain,
rather than the numbers it generates; that care needs to be taken
that models are designed at an appropriate
level of abstraction; that although appropriate data for
calibration and validation may sometimes be in short
supply, modelling is o�en still valuable; that modelling
collaboratively and involving a range of stakeholders

from the outset increases the likelihood that the model will be
used and will be fit for purpose; that attention
needstobepaidtoe�ectivecommunicationbetweenmodellersandsta
keholders;andthatmodellingforpub-
lic policy involves ethical issues that need careful
consideration. The paper concludes that policy modelling
will continue to grow in importance as a component of public
policy making processes, but if its potential is to
befullyrealised,
therewillneedtobeameldingoftheculturesofcomputationalmodelli
ngandpolicymaking.
Keywords: Policy Modelling, Policy Evaluation, Policy
Appraisal, Modelling Guidelines, Collaboration, Ethics
Introduction
1.1 Computationalmodelshavebeenusedtoassist indeveloping,
implementingandevaluatingpublicpoliciesfor
at least threedecades,but theirpotential
remainstobefullyexploited(Johnston&Desouza2015;Anzolaetal.
2017;Barbrook-Johnsonetal.2017).
Inthispaper,usingaselectionofexamplesofcomputationalmodelsus
ed
in public policy processes, we (i) consider the roles of models

in policy making, (ii) explore policy making as a
typeofexperimentationinrelationtomodelexperiments,and(iii)sug
gestsomekeylessonsforthee�ectiveuse
ofmodels. Wealsohighlightsomeof
thechallengesandopportunities facingsuchmodelsandtheiruse
inthe
future.
Ouraimistosupportthemodellingcommunitythatreadsthisjournalin
itse�orttobuildcomputational
models of public policy that are valuable and useful.
1.2
Webelievethise�ortistimelygiventhatcomputationalmodels,ofthe
typethisjournalregularlyreportson,are
now increasingly used by government, business, and civil
society as well as in academic communities (Hauke
etal.2017).
Therearemanyguidestocomputationalmodellingproducedfordi�er
entcommunities, forexam-
ple in UK government the ‘Aqua Book’ (reviewed for JASSS in
Edmonds (2016)), but these are o�en aimed at
practitioner and government audiences, can be highly
procedural and technical, generally omit discussion of
failure and rarely include deeper reflections on how best to
model for public policy. Our aim here is to fill gaps

JASSS, 21(1) 14, 2018
http://jasss.soc.surrey.ac.uk/21/1/14.html Doi:
10.18564/jasss.3669
[email protected]
le�bytheseformalguides, toprovidereflectionsaimedatmodellers,
touseaselectionofexamplestoexplore
issues in an accessible way, and acknowledge failures and
learning from them.
1.3 We focus only on computational models that aim to model,
or include some modelling of, social processes.
Although some of the discussion may apply, we are not directly
considering computational models that are
purelyecologicalortechnical intheir
focus,orsimplermodelssuchasspreadsheetswhichmayimplicitlyco
ver
socialprocessesbuteitherdonotrepresentthemexplicitly,ormakeext
remelysimpleandstrongassumptions.
Although ‘computational models of public policy’ is the full
and accurate term, and others o�en use ‘compu-
tational policy models’, for the sake of brevity, we will use the

term ‘policy models’ throughout the rest of this
paper.
1.4 Based on our experience, our main recommendations are
that policy modelling needs to be conducted with
a strong appreciation of the context in which models will be
used, and with a concern for their fitness for the
purposes for which they are designed and the conclusions drawn
from them. Moreover, policy modelling is
almost always likely to be of low or no value if done without
strong and iterative engagement with the users
of the model outputs, i.e. decision makers. Modellers must
engage with users in a deep, meaningful, ethically
informed and iterative way.
1.5 In the remainder of this paper, Section 2 introduces the role
of policy models in policy making. Section 3 ex-
ploresthe ideaofpolicymakingasatypeofexperimentation
inrelationtopolicymodelexperiments. Wethen
discusssomeexamplesandexperiencesofpolicymodelling(Section
4)anddrawoutsomekeylessonstohelp
make policy modelling more e�ective (Section 5). Finally,
Section 6 concludes and discusses some key next
steps and other opportunities for computational policy
modellers.

TheRoleofModels inPolicyMaking
2.1 Thestandard, butnow somewhatdiscreditedview ofpolicy
making is that itoccurs incycles (forexample, see
the seminal arguments made in Lindblom 1959 and Lindblom
1979; and more recently o�icial recognition in
HM Treasury 2013). A policy problem comes to light, perhaps
through the occurrence of some crisis, a media
campaign, or as a response to a political event. This is the
agenda setting stage and is followed by policy for-
mulation, gathering support for the policy, implementing the
policy, monitoring and evaluating the success of
the policy and finally policy maintenance or termination. The
cycle then starts again, as new needs or circum-
stancesgeneratedemandsfornewpolicies.
Althoughtheideaofapolicycyclehasthemeritofbeingaclearand
straightforwardwayofconceptualisingthedevelopmentofpolicy,
ithasbeencriticisedasbeingunrealisticand
oversimplifying what happens, which is typically highly
complex and contingent on multiple sources of pres-
sureandinformation(Cairney2013;Moran2015),andevenself-
organising (Byrne&Callaghan2014;Teisman&
Klijn 2008).

2.2 The idea of a cycle does, however, still help to identify the
many components that make up the design and
implementationofpolicy. Thereareat
leasttwoareaswheremodelshaveaclearandimportantroletoplay: in
policydesignandappraisal,andpolicyevaluation.
Policyappraisal(asdefinedinHMTreasury2013,sometimes
referred to as ex-ante evaluation, consists of assessing the
relative merits of alternative policy prescriptions in
meetingthepolicyobjectives. Appraisal findingsareakey input
intopolicydesigndecisions. Policyevaluation
either takes a summative approach, examining whether a policy
has actually met its objectives (i.e. ex-post),
or a more formative approach to see how a policy might be
working, for whom and where (HM Treasury 2011).
In the formative role, the key goal is learning to inform future
iterations of the policy, and others with similar
characteristics.
Modellingtosupportpolicydesignandappraisal
2.3 Whenusedex-
ante,apolicymodelmaybeusedtoexploreapolicyoption,helpingtoid
entifyandspecify inde-
tailaconsistentpolicydesign(HMTreasury2013), forexampleby
locatingwherebestapolicymight intervene,

or by identifying possible synergies or conflicts between the
mechanisms of multiple policies. Policy models
can also be used to appraise alternative policies, to see which of
several possibilities can be expected to yield
the best or most robust outcome. In this mode, a policy model is
in essence used to ‘experiment’ with alterna-
tive policy options and assumptions about the system in which
it is intervening, by changing the parameters
or the rules in the model and observing what the outcomes are.
This is valuable because it saves the time and
costassociatedwithhavingtorunexperimentsorpilots
intheactualpolicydomain. Thisconceptofthemodel
as an experimental space is discussed in more detail in Section
3 below.
JASSS, 21(1) 14, 2018
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2.4 Thecommonassumptionisthatonebuildscomputationalmodels
inordertomakepredictions. However,pre-
diction,inthesenseofpredictingthefuturevalueofsomemeasure,isin
facto�enimpossibleinpolicydomains.

Socialandeconomicphenomenaareo�encomplex
(inthetechnicalsense,seee.g. Sawyer2005). Thismeans
that how some process evolves depends on random chance, its
previous history (‘path dependence’) and the
e�ect of positive and negative feedback loops. Just as with the
weather, for which exact forecasting is impos-
sible more than a few days ahead, the future course of many
social processes may be literally unknowable in
detail, no matter how detailed the model may be. Secondly, a
model is necessarily an abstraction from real-
ity, and since it is impossible to isolate sections of society,
from outside influences, there may be unexpected
exogenous factors that have not been modelled and that a�ect
the outcome.
2.5 Forthesereasons, theabilitytomake‘pointpredictions’, i.e.
forecastsofspecificvaluesataspecifictimeinthe
future, is rarelypossible. Morepossible
isapredictionthatsomeeventwillorwillnot takeplace,orqualitative
statements about the type or direction of change of values.
Understanding what sort of unexpected outcomes
can emerge and something of the nature of how these arise also
helps design policies that can be responsive
tounexpectedoutcomeswhentheydoarise.
Itcanbeparticularlyhelpful inchangingenvironmentstousethe

model to explore what might happen under a range of possible,
but di�erent, potential futures - without any
commitment about which of these may eventually transpire.
Even more valuable is a finding that the model
shows that certain outcomes could not be achieved given the
assumptions of the model. An example of this is
the use of a whole system energy model to develop scenarios
that meet the decarbonisation goals set by the
EU for 2050 (see, for example, RAENG 2015.)
2.6 Ratherdi�erent fromusingmodels
tomakepredictionsorgeneratescenarios is theuseofmodels to
formalise
and clarify understanding of the processes at work in some
domain. If this is done carefully, the model may be
valuable as a training or communication tool, demonstrating the
mechanisms at work in a policy domain and
how they interact.
Modellingtosupportpolicyevaluation
2.7 To evaluate a policy ex-post, one needs to compare what
happened a�er the policy has been implemented
against what would have happened in the absence of the policy
(the ‘counterfactual’). To do this, one needs

data about the real situation (with the policy evaluation) and
data about the situation if the policy had not
been implemented (the so-called ‘business as usual’ situation).
To obtain the latter, one can use a randomised
control trial (RCT) or quasi-experiment (HM Treasury 2011),
but this is o�en di�icult, expensive and sometimes
impossibletocarryoutduetothenatureoftheinterventionbarringposs
ibilityofcreatingcontrolgroups(e.g. a
schemewhichisaccessibletoall,orapolicy inwhichlocal
implementationdecisionsare impossibletocontrol
and have a strong e�ect).
2.8 Policy models o�er some alternatives. One is to develop a
computational model and run simulations with and
without implementation of a policy, and then compare formally
the two model outcomes with each other and
with reality (with the policy implemented), using quantitative
analysis. This avoids the problems of having to
establish a real-world counterfactual. Once again, the policy
model is being used in place of an experiment.
2.9 Anotheralternative is
tousemorequalitativeSystemMappingtypeapproaches(e.g.
FuzzyCognitiveMapping;
seeUprichard&Penn2016),

tobuildqualitativemodelswithdi�erentstructuresandassumptions(
torepresent
the situation with and without the intervention), and again
interrogate the di�erent outcomes of the model
analyses.
2.10 Finally, another use in ex-post evaluation is to use models
to refine and test the theory of how policies might
have a�ected an outcome of interest, i.e. to support common
theory-based approaches to evaluation such as
Theory of Change (see Clark & Taplin 2012), and Logic
Mapping (see Hills 2010).
2.11 Interrogation of models and model results can be done
quantitatively (i.e. through multiple simulations, sen-
sitivity analysis, and ‘what if’ tests), but may also be done in
qualitative and participatory fashion with stake-
holders, with stakeholders involved in the actual analysis (as
opposed to just being shown the results). The
choice should be driven by the purpose of the modelling
process, and the needs of stakeholders. In both ex-
ante and ex-post evaluation, policy models can be powerful
tools to use as a route for engaging and informing
stakeholders, including the public, about policies and their
implications (Voinov & Bousquet 2010). This may

beby includingstakeholders intheprocess, decisions,
andvalidationofmodeldesign; or itmaybe later in the
process, inusingtheresultsofamodel
toopenupdiscussionswithstakeholders,and/orevenusingthemodel
‘live’ to explore connections between assumptions, scenarios,
and outcomes (Johnson 2015a).
JASSS, 21(1) 14, 2018
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Di�iculties intheuseofmodelling
2.12 While, in principle, policy models have all these roles and
potential benefits, experience shows that it can be
di�icult to achieve them (see Taylor 2003; Kolkman et al. 2016,
and Section 4 for some examples). The policy
process has many characteristics that can make it di�icult to
incorporate modelling successfully, including1:
• The need for acceptability and transparency: policy makers
may fall back on more traditional and more
widelyacceptedformsofevidence,especiallywheretherisksassociat

edwiththedecisionarehigh. Mod-
elsmayappeartoactasblackboxesthatonlyexpertscanunderstandoru
se,withoutputshighlyreliant
on assumptions that are di�icult to validate. Analysts and
researchers in government o�en have little
autonomy, and although they may see the value of policy
models, it can be di�icult for them to commu-
nicate this to the decision-makers.
• Changeanduncertainty: theenvironment
inwhichthepolicywillbe implemented,maybehighlyuncer-
tain, this can undermine model development when beliefs or
decisions shi� as a result of the modelling
process (although this is equally an important outcome and
benefit of the modelling process), or other
factors.
• Shorttimescales:
thetimescalesassociatedwithpolicydecisionmakingarealmostalwa
ysrelativelyfast,
and needs can be di�icult to predict, meaning it can be di�icult
for computational modellers to provide
timely support.
• Procurement processes: o�en departments lack the capability

and su�iciently flexible processes to pro-
cure complex modelling.
• Thepoliticalandpragmaticrealitiesofdecisionmaking:
individuals’valuesandpoliticalvaluescanhold
hugesway,eveninthefaceofempiricalevidence(letalonemodelling)
thatmaycontradict theirview,or
point towards policy which is politically impossible.
• Stakeholders: There will be many di�erent stakeholders
involved in developing, or a�ected by, policies.
It will not be possible to engage all of these in the policy
modelling process, and indeed policy makers
may be wary of closely involving them in a participatory
modelling process.
2.13 These characteristics may also apply more widely to
evidence and other forms of research and analysis. It is
not our suggestion that these characteristics are inherently
negative; they may be important and reasonable
parts of the policy making process. The important thing to
remember, as a modeller, is that a model can only,
and should only, provide more information to the process, not a
final decision for the policy process to simply
implement.

PolicyExperimentsandPolicyModels
3.1 Although the roles and uses of policy models are relatively
well-described and understood, our perception is
that there are still many areas where more use could be made of
modelling and that a lack of familiarity with,
and confidence in, policy modelling, is restricting its use.
Potential users may question whether policy mod-
elling in their domain is su�iciently scientifically established
and mature to be safely applied to guiding real-
world policies. The di�erence between applying policies to the
real world and making experimental interven-
tions in a policy model might be too big to generate any
learning from the latter to inform the former.
Policypilots
3.2 One response is to argue that actual policy implementations
are themselves experimental interventions and
are therefore of the same character as interventions in a policy
model. Boeschen et al. (2017) propose that
we live in “experimental societies" and that implementing
policies is nothing but conducting “real-world ex-
periments". Real-world experiments are “a more or less

legitimate, methodically guided or carelessly adopted
social practice to start something new" (Krohn 2007, p. 344;
own translation). Their outcomes immediately
display “success or failure of a design process" (ibid., p. 347).
3.3 A real-world experiment implements one solution for the
policy design problem. It does not check for other
possible solutions or alternative options, but at best monitors
and responds to what is emerging in real time.
JASSS, 21(1) 14, 2018
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Implementing policies as a real-world experiment is therefore
far from ideal and far removed from the idea of
reversibility in the laboratory. In laboratory experiments the
experimental system is isolated from its environ-
ment in such a way that the e�ects of single parameters can be
observed.
3.4 One approach that tries to bridge the gap between the real-
world and laboratory experiments is to conduct

policypilots.
Theuseofpolicypilots(Greenberg&Shroder1997;CabinetO�ice20
03;Martin&Sanderson1999)
as social experiments is fairly widespread. In a policy pilot, a
policy change can be assessed against a coun-
terfactual in a limited context before rolling it out for general
implementation. In this way (a small number of)
di�erent solutions can be tried out and evaluated, and learning
fed back into policy design.
3.5 A dominant method for policy pilots is the Randomised
Control Trial (RCT) (Greenberg & Shroder 1997; Boruch
1997), well-known from medical research, where a carefully
selected treatment group is compared with a con-
trol group that is not administered the treatment under scrutiny.
RCTs can thus present a halfway house be-
tween an idealised laboratory experiment and a real-world
experiment. However, the claim that an RCT is ca-
pableofreproducingalaboratorysituationwhererigoroustestingagai
nstacounterfactual ispossiblehasalso
been contested (Cabinet O�ice 2003, p. 19). It is argued that in
principle there is no possibility of social experi-
mentsduetotherequirementofceterisparibus (i.e.
inthesocialworld, it is impossibletohavetwoexperiments
with everything equal but the one parameter under scrutiny);

that the complex system-environment interac-
tions that are necessary to adequately understand social systems
cannot be reproduced in an RCT; and that
random allocation is impossible in many domains, so that a
‘neutral’ counterfactual cannot be established.
Moreover, itmaybeariskypoliticalstrategyorevenunethical
toadministeracertainbenefit insomepilotcon-
textbutnottothecorrespondingcontrolgroup. This
isevenmorethecase if thepolicywouldputtheselected
recipients at a disadvantage (Cabinet O�ice 2003, p. 17).
3.6
Whileapilotcanbegoodforgatheringevidenceaboutasinglecase,
itmightnotserveasagood‘one-size-fits-
all’ role model for other cases in other contexts. Furthermore, it
cannot say much about why or how the policy
workedordidnotwork,ordecomposethe‘whatworks’questions into,
‘whatworks,where, forwhom,atwhat
costs,andunderwhatconditions’?
Therearealsomorepracticalproblemstoconsider,amongthemtime,s
ta�
resources and budget. There is general agreement that a good
pilot is costly, time-consuming, “administra-
tively cumbersome" and in need of well-trained managing sta�
(Cabinet O�ice 2003, p. 5). There is “a sense of

pessimismanddisappointmentwiththewaypolicypilotsandevaluati
onsarecurrentlyusedandwereusedin
the past (...): poorly designed studies; weak methodologies;
impatient political masters; time pressures and
unrealistic deadlines" (Seminar on Policy Pilots and Evaluation
2013, p. 11).
3.7
Thus,policypilotscannotmeettheclaimtobeahappymediumbetwee
nlaboratoryexperiments,withtheiriso-
lationstrategiescapableofparametervariation,andKrohn’sreal-
worldexperimentsinvolvingcomplexsystem-
environment interactions in real time. This is where
computational policy modelling comes in.
Policymodels forpolicyexperimentation
3.8 Unlike policy pilots, computational policy models are able
to work with ceteris paribus rules, random control,
andnon-contaminatedcounterfactuals(seebelow).
Usingpolicymodels,wecanexplorealternativesolutions,
simplybytryingoutparametervariations inthemodel,
andexperimentwithcontext-specificmodelsandwith
short,mediumandlongtimehorizons.
Furthermore,policymodelsareethicallyandpoliticallyneutraltobui

ld
and run, though the use of their outcomes may not be.
3.9 Unlikereal-
worldandpolicypilots,policymodelsallowtheusertoinvestigatethef
uture. Initiallythemodellers
will seektoreproducethedatabasedescribingthe
initialstateofareal-worldexperimentandthenextrapolate
simulatedstructuresanddynamicsintothefuture.
Atfirstabaselinescenariocanbederived: whatiftherewere
no changes in the future? This is artificial and, for
methodological reasons, boring: nothing much happens but
incremental evolution, no event, no surprise, no intervention;
changes can then be introduced.
3.10 As with real-world experiments, modelling experiments
enable recursive learning by stakeholders. Stakehold-
ers can achieve system competence and practical skills through
interacting with the model to learn ‘by doing’
how to act in complex situations. With the model, it is not only
possible to simulate the real-world experiment
envisagedbutalsototestmultiplescenariosforpotential real-
worldexperimentsviaextensiveparametervari-
ations. The whole solution space can be checked, where future
states are not only accessible but tractable.

3.11 Thisdoesnotimplythatit
ispossibletoobtainexactpredictionsforfuturestatesofcomplexsocia
lsystems(see
the discussion on prediction above). Deciding under uncertainty
has to be informed di�erently:
“Experimenting under conditions of uncertainties of this kind, it
appears, will be one of the most
distinctivecharacteristicsofdecision-makinginfuturesocieties[...],
theyimportandusemethods
JASSS, 21(1) 14, 2018
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of investigation and research. Among these are conceptual
modelling of complex situations, com-
puter simulation of possible futures, and – perhaps most
promising – the turning of scenarios into
‘real-world experiments’" (Gross & Krohn 2005, p. 77).
3.12 Regarding the continuum between the extremes of giving

no consideration (e.g. with laboratory experiments)
and full consideration (e.g. real-world experiments) to complex
system-environment interactions, policy mod-
elling experiments indeed sit somewhere in the (happy) middle.
We would argue that, where the costs or risks
associated with a policy change are high, and the context is
complex, it is not only common sense to carry out
policy modelling, but it would be unethical not to.
ExamplesofPolicyModels
4.1 Wehavediscussedtheroleofpolicymodels
inabstractatsomelength, it isnowimportantto illustratetheuse
of policy models using a number of examples of policy
modelling drawn from our own experience. These have
beenselectedtoo�erawiderangeoftypesofmodelandcontextsofapp
lication. Inthespiritofrecordingfailure
as well as success, we mention not only the ultimate outcomes,
but also some of the problems and challenges
encountered along the way. In the next section, we shall draw
out some general lessons from these examples.
Tell-Me
4.2 TheEuropean-fundedTELLMEproject

focusedonhealthcommunicationassociatedwithinfluenzaepidemic
s.
One output was a prototype agent-based model, intended to be
used by health communicators to understand
the potential e�ects of di�erent communication plans under
various influenza epidemic scenarios (Figure 1).
4.3 The basic structure of the model was determined by its
purpose: to compare the potential e�ects of di�er-
entcommunicationplansonprotectivepersonalbehaviourandhence
onthespreadofaninfluenzaepidemic.
This requires two linked models: a behaviour model that
simulates the way in which people respond to com-
munication and make decisions about whether to vaccinate or
adopt other protective behaviour, and an epi-
demic model that simulates the spread of influenza. The key
model entities are: (i) messages, which together
implement the communication plans; (ii) individuals, who
receive communication and make decisions about
whethertoadoptprotectivebehaviour;and(iii)regions,whichholdin
formationaboutthelocalepidemicstate.
The major flow of influence is the e�ect that communication
has on attitude and hence behaviour, which af-
fects epidemic transmission and hence incidence. Incidence
contributes to perceived risk, which influences

behaviourandestablishesafeedbackrelationship(seeBadham&Gilb
ert2015forthedetailedspecification). A
fullerdescriptionofthemodelanddiscussiononitusescanbefoundin
Barbrook-Johnsonetal. (2017). Amore
technicalpaperonanovelmodelcalibrationapproachusingtheTellM
easanexampleispresentedinBadham
et al. (2017).
4.4
Drawingonfindingsfromstakeholderworkshopsandtheresultsofthe
modelitself,themodellingteamsuggest
the TELL ME model can be useful: (i) as a teaching tool, (ii) to
test theory, and (iii) to inform data collection
(Barbrook-Johnson et al. 2017).
HOPES
4.5 Practice theories provide an alternative to the theory of
planned behaviour and the theory of reasoned action
to explore sustainability issues such as energy use, climate
change, food production, water scarcity, etc. The
central argument is that the routine activities (aka practices)
that people carry out in the service of everyday
living(e.g. waysofcooking,eating,
travelling,etc.),o�enwithsomelevelofautomaticitydevelopedover

time,
shouldbethefocusof inquiryandinterventionif thegoal
istotransformenergy-andemissions-intensiveways
of living.
4.6 The Households and Practices in Energy use Scenarios
(HOPES) agent-based model (Narasimhan et al. 2017)
was developed to formalise key features of practice theories and
to use the model to explore the dynamics of
energyuseinhouseholds. Akeytheoretical
featurethatHOPESsoughttoformalise is theperformanceofprac-
tices, enabled by the coming together of appropriate meanings
(mental activities of understanding, knowing
how and desiring, Reckwitz 2002), materials (objects, body and
mind) and skills (competences). For example,
JASSS, 21(1) 14, 2018
10.18564/jasss.3669
Figure 1: A screenshot of the Tell Me model interface. The
interface houses key model parameters related to
individuals’ attitude towards influenza, their consumption of

di�erent media types, the epidemiological pa-
rameters of the strain of influenza, and their social networks.
Key outputs shown include changes in people’s’
attitude, actual behaviour, and the progression of the epidemic.
The world view shows the spread of the epi-
demic (blue = epidemic not yet reached, red = high levels of
infection, green = most people recovered).
a laundry practice could signify a desire for clean clothes
(meaning) realised by using a washing machine (ma-
terial) and knowing how to operate the washing machine (skill);
performance of the practice then results in
energy use.
4.7 HOPES has two types of agents: households and practices.
Elements (meanings, materials and skills) are en-
tities in the model. The model concept is that households choose
di�erent elements to perform practices de-
pendingonthesocio-technicalsettingsuniquetoeachhousehold.
Theperformanceofsomepracticesresult in
energy use while some do not, e.g., using a heater to keep warm
results in energy use whereas using a jumper
or blanket does not incur energy use. Furthermore, the repeated
performance of practices across space and
time causes the enabling elements to adapt (e.g. some elements

are used more popularly than others), which
subsequently a�ects the future performance of practices and
thereby energy use. A rule-based system, devel-
oped based on empirical data collected from 60 UK households,
was included in HOPES to enable households
to choose elements to perform practices. The rule-based
approach allowed organising the complex contex-
tual information and socio-technical insights gathered from the
empirical study in a structured way to choose
the most appropriate actions when faced with incomplete and/or
conflicting decisions. HOPES also includes
sub-models to calculate the energy use resulting from the
performance of practices, e.g. a thermal model of a
house is built in to consider the outdoor temperature, the type
and size of heater, and the thermostat setpoint
to estimate the energy used for thermal comfort practices in
each household.
4.8 The model is used to test di�erent policy and innovation
scenarios to explore the impacts of the performance
of practices on energy use. For example, the implementation of
a time of use tari� demand response scenario
shows that while some demand shi�ing is possible as a
consequence of pricing signals, there is no significant
reductioninenergyuseduringpeakperiodsasmanyhouseholdscanno

tputo�usingenergy(Narasimhanetal.
2017). The overall motivation is that by gaining insight into the
trajectories of unsustainable energy consum-
ing practices (and underlying elements) under di�erent
scenarios, it might be possible to propose alternative
pathways that allow more sustainable practices to take hold.
SWAP
4.9 TheSWAPmodel (Johnson2015b,a) isanagent-basedmodelof
farmers’makingdecisionsaboutadoptingsoil
and water conservation (SWC) practices on their land.
Developed in NetLogo (Wilensky 1999), the main agents
inthemodelarefarmers,whoaremakingdecisionsaboutwhethertopr
acticeSWCornot,andextensionagents
who are government and non-governmental actors who
encourage farmers to adopt. Farmers can also be en-
couraged or discouraged to change their behaviour depending on
what those nearby and in their social net-
worksaredoing.
Theenvironmentisasimplemodelofthesoilquality(Figure2).
Themainoutcomesofinterest
JASSS, 21(1) 14, 2018

10.18564/jasss.3669
are the temporal and spatial patterns of SWC adoption. A full
description can be found in Johnson (2015b) and
Johnson (2015a).
Figure 2: Screenshot of the SWAP model key outputs and
worldview in NetLogo. The outputs show the per-
centagesof farmerspracticingSWCandsimilarly thenumberof
fieldswithSWC. Intheworldview,circlesshow
farmer agents in various decision states, triangles show
‘extension’ agents, and the patches’ colour denotes
the presence of conservation (green or brown) and soil quality
(deeper colour means higher quality). Source:
Adapted from Johnson (2015a).
4.10 The SWAP model was developed: (i) as an ‘interested
amateur’ to be used as a discussion tool to improve the
qualityof
interactionbetweenpolicystakeholders;and(ii)asanexplorationoft
hetheoryonfarmerbehaviour
in the SWC literature.

4.11 The model’s use as an ‘interested amateur’ was explored
with stakeholders in Ethiopia. Using a model as an
interestedamateur isaconcept inspiredbyDennett(2013).
Dennettsuggestsexpertso�entalkpasteachother,
make wrong assumptions about others’ beliefs, and/or do not
wish to look stupid by asking basic questions.
These failings can o�en mean experts err on the side of under-
explaining issues, and thus fail to come to con-
sensus or agreeable outcomes in discussion. For Dennett, an
academic philosopher, the solution is to bring
undergraduate students – interested amateurs – into discussions
to ask the simple questions, and generally
force experts to err on the side of over-explanation.
4.12 The SWAP model was used as an interested amateur with a
di�erent set of experts, policy makers and o�icials
in Ethiopia. This was done because policies designed to
increase adoption of SWC have generally been un-
successful due to poor calibration to farmers’ needs. This is
understood in the literature to be a result of poor
interactionbetweenthevariousstakeholdersworkingonSWC.When
used,participantsrecognisedthevalueof
the model and it was successful in aiding discussion. However,
participants described an inability to innovate
in their work, and viewed stakeholders ‘lower-down’ the policy

spectrum as being in more need of discussion
tools. A full description of this use of the model can be found in
Johnson (2015a).
INFSO-SKIN
4.13 The European Commission was expecting to spend
arounde77 billion on research and development through
its Horizon 2020 programme between 2014 and 2020. It is the
successor to the previous, rather smaller pro-
gramme,calledFramework7.
WhenHorizon2020wasbeingdesigned,
theCommissionwantedtounderstand
how the rules for Framework 7 could be adapted for Horizon
2020 to optimise it for current policy goals, such
as increasing the involvement of small and medium enterprises
(SMEs).
4.14 An agent-based model, INFSO-SKIN, was built to evaluate
possible funding policies. The model was set up to
reproducethefundingrules,
thefundedorganisationsandprojects,andtheresultingnetworkstruct
uresofthe
Framework 7 programme. This model, extrapolated into the
future without any policy changes, was then used

asabenchmarkfor furtherexperiments.
Againstthisbaselinescenario,severalpolicychangesthatwereunder
consideration for the design of the Horizon 2020 programme
were then tested, to understand the e�ect of a
rangeofpolicyoptions:
changestothethematicscopeoftheprogramme;thefunding
instruments; theoverall
JASSS, 21(1) 14, 2018
10.18564/jasss.3669
amount of programme funding; and increasing SME
participation (Ahrweiler et al. 2015). The results of these
simulations ultimately informed the design of Horizon 2020.
SilentSpreadandExodis-FMD
4.15 Following the 2001 outbreak of Foot and Mouth Disease
(FMD), the UK Department of the Environment, Food
and Rural A�airs (Defra) imposed a 20-day standstill period
prohibiting any livestock movements o�-farm fol-
lowingthearrivalofananimal. The20-

dayrulecausedsignificantdi�iculties for farmers.
TheLessonsLearned
Inquiry, which reported in July 2002, recommended that the 20-
day standstill remain in place pending a de-
tailed cost-benefit analysis (CBA) of the standstill regime.
4.16 Defra commissioned the CBA in September 2002 and a
report was required in early 2003 in order to inform
changes to the movement regime prior to the spring movements
season. This timescale was challenging due
totheshorttimescalesandlimiteddataavailabletoinformthecostrisk
benefitmodellingrequired. Atopdown
model was therefore developed that captured only the essential
elements of the decision, combining them in
an influence diagram representation of the decision to be made.
As wide a range of experts as possible were
involved in model development, helping inform the structure of
the model, its parameterisation, validation
and interpretation of the results. An Agile approach was
adopted with detail added to the model in a series of
development cycles guided by a steering group.
4.17
Theresultant‘SilentSpread’modelshowedthatfactorssuchastimeto
detectionofdisease,aremuchmoreim-

portantthanlengthofstandstill
indeterminingthesizeofanoutbreak(Risk

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