F O R U MComplexity and TransitionManagementJan Rotman.docx

F O R U M
Complexity and Transition
Management
Jan Rotmans and Derk Loorbach
Keywords:
complex adaptive systems
emergence
governance
industrial ecology
sustainable development
transitions
Summary
This article presents a framework, transition management, for
managing complex societal systems. The principal contribu-
tion of this article is to articulate the relationship between
transition management and complex systems theory. A better
understanding of the dynamics of complex, adaptive systems
provides insight into the opportunities, limitations, and con-
ditions under which it is possible to influence such systems.
Transition management is based on key notions of complex
systems theory, such as variation and selection, emergence,
coevolution, and self-organization. It involves a cyclical
process
of phases at various scale levels: stimulating niche develop-
ment at the micro level, finding new attractors at the macro
level by developing a sustainability vision, creating diversity by
setting out experiments, and selecting successful experiments

that can be scaled up.
Address correspondence to:
Jan Rotmans
DRIFT: Dutch Research Institute For
Transitions
Erasmus University Rotterdam
P.O. Box 1738
3000 DR Rotterdam
[email protected]
www.drift.eur.nl
c© 2009 by Yale University
DOI: 10.1111/j.1530-9290.2009.00116.x
Volume 13, Number 2
184 Journal of Industrial Ecology
www.blackwellpublishing.com/jie
F O R U M
Introduction
Our society faces a number of persistent prob-
lems whose symptoms are becoming more and
more apparent. Persistent problems are complex
because they are deeply embedded in our societal
structures; uncertain due to the hardly reducible
structural uncertainty they include; difficult to
manage, with a variety of actors with diverse in-
terests involved; and hard to grasp in the sense
that they are difficult to interpret and ill struc-

tured (Dirven et al. 2002). Persistent problems
are the superlative form of what Rittel and Web-
ber (1973, 160) refer to as “wicked problems.”
An example of a persistent problem is the energy
problem, with anthropogenic climate change as a
manifestation (Energy Council 2004). Persistent
problems cannot be solved through only current
policies (Ministry of Housing, Spatial Planning
and Environment 2002; Social and Economic
Council of the Netherlands 2001). Persistent
problems are related to the system failures that
crept into our societal systems and that, contrary
to market failures, cannot be corrected by the
market or current policies. System failures are
locked-in flaws in our societal structures, such as
technological bias, weak or dominant networks,
institutional barriers, and path dependencies.
Combating system failures requires a restruc-
turing of societal systems—that is, a transition. A
transition is a radical, structural change of a so-
cietal (sub)system that is the result of a coevolu-
tion of economic, cultural, technological, ecolog-
ical, and institutional developments at different
scale levels (Rotmans et al. 2001; Rotmans 2006).
In “transition language,” we call the deep struc-
ture the incumbent regime: a conglomerate of
structure (institutional and physical setting), cul-
ture (prevailing perspective), and practices (rules,
routines, and habits). And we denote an emer-
gent structure as a niche: a structure formed by
a small group of agents that deviate from the
regime and that might build up a new regime
that is able to break down and replace the in-
cumbent regime. This differs somewhat from the
common definition of a niche as individual tech-

nologies, practices, and actors outside or periph-
eral to the regime, as loci for radical innovation
(Geels 2005).
The idea is that a better insight into the func-
tioning of societal systems provides insight into
the possibilities for directing these systems. We
use complex systems theory to study the dynamics
of societal systems to derive a collection of basic
guidelines that can be used to direct those sys-
tems. Obviously, societal systems, because of their
complexity, cannot be directed in command and
control terms. We do, however, hypothesize that
it is possible to use the understanding of tran-
sition dynamics to influence the direction and
pace of a transition of a societal system into a
more sustainable direction. The explicit norma-
tive orientation of sustainability is important, be-
cause historical transitions often have not led to
a more sustainable society (Rotmans 2005). Fos-
tering sustainability transitions is what we call
transition management (Rotmans et al. 2001).
In this article, we first treat basic principles of
complex systems theory and of managing com-
plex adaptive systems. That results in the formu-
lation of core theoretical principles for transition
management, on the basis of which we present a
framework that contains guidelines for applying
transition management in practice.
Complex Systems Theory
Complexity theory, otherwise known as com-
plex systems theory, has its roots in the general
systems theory that Von Bertalanffy (1968) pub-

lished in the 1930s. Systems theory is an interdis-
ciplinary field of science that studies the nature of
complex systems in society, nature, science, and
technology. It provides a framework by which a
group of interrelated components that influence
each other can be analyzed. That group can be
a sector, branch, city, organism, or even a soci-
ety. Systems theory evolved over the last century
from deterministic to probabilistic, from a control
engineering to a soft systems approach, and from
partial to integrated. In the 1970s and 1980s, in-
tegral systems theory became an important field,
focusing on the integration of social, economic,
and ecological processes (Holling 1978; Hordijk
1985; Rotmans 1990). During this time, soft sys-
tems theory emerged; it takes a qualitative ap-
proach rather than a quantitative approach and
is mostly applied to companies and organizations
Rotmans and Loorbach, Complexity and Transition Management
185
F O R U M
(Senge 1990). In the 1990s, complex systems the-
ory was introduced; it focuses on the coevolu-
tionary development of systems (Holland 1995;
Kauffman 1993, 1995). Although the theory is
far from mature, it has attracted a great deal of
attention and has many applications in diverse
research fields: in biology (Kauffman 1995), eco-
nomics (Arthur et al. 1997), ecology (Kay et al.
1999; Gunderson and Holling 2002), public ad-
ministration (Kickert 1991; Teisman 1992), and

policy analysis (Geldof 2002; Rotmans 2003). A
single complex systems theory does not exist:
There are multiple manifestations of it. There
are (1) formalized and computational modeling
approaches, (2) a set of “understandings” of the
behavior of complex systems, (3) metaphorical
use to describe social phenomena, and (4) philo-
sophical considerations about the ontology and
epistemology of complex systems. We take the
second and, to a lesser extent, the first man-
ifestation as a starting point for our transition
research. Within this context, complex systems
theory attempts to better understand the behav-
ior of complex systems that run through cycles of
relatively long periods of equilibrium, order, and
stability interspersed with relatively short periods
of instability and chaos. The primary focus is on
complex systems, which have the following char-
acteristics, as drawn from the work by Prigogine
and Stengers (1984), Holling (1987), Holland
(1995), and Kauffman (1995).
Complex systems are open systems that in-
teract with their environment and constantly
evolve and unfold over time. Complex systems
contain many diverse components and interac-
tions between components. These interactions
are nonlinear: A small stimulus may cause a large
effect or no effect at all. Conversely, a big stim-
ulus may cause a small effect. Complex systems
contain feedback loops. Both negative (damping)
and positive (amplifying) feedbacks are key in-
gredients of complex systems. Complex systems
have a history; prior states have an influence on
present states, which have an influence on future
states. This creates path dependence, whereby cur-

rent and future states depend on the path of pre-
vious states. Complex systems are nested and en-
compass various organizational levels. They have
emergent properties—that is, higher level struc-
tures arise from interaction between lower level
components. Complex systems have multiple at-
tractors. An attractor is a preferred steady system’s
state set, to which a complex system evolves after
a long enough time.
Complex adaptive systems are special cases
of complex systems. They are adaptive in the
sense that they have the capacity to change and
learn from experience. Expressed differently, they
are able to respond to and adjust themselves to
changes in their environment. What makes a
complex adaptive system special is the set of con-
stantly adapting nonlinear relationships. Com-
plex adaptive systems contain special objects—
agents that interact with each other and adapt
themselves to other agents and changing condi-
tions. This is why complex adaptive systems have
unique features, such as coevolution, emergence,
and self-organization.
In the biological or economic context, coevo-
lution refers to mutual selection of two or more
evolving populations (van den Bergh and Stagl
2004). In the complex systems context, however,
coevolution is used to indicate the interaction
between different systems that influences the dy-
namics of the individual systems, leading to ir-
reversible patterns of change within each of the
systems (Kemp et al. 2007). The irreversibility
aspect distinguishes coevolution from coproduc-

tion, which indicates mere interaction. Coevolu-
tion means that a complex system coevolves with
its environment—that is, there are interdepen-
dencies and positive feedbacks between the com-
plex system and its environment (Mitleton-Kelly
2003). In such a coevolutionary process, both
competition and cooperation have a role to play.
Emergence can be defined as the arising of
novel and coherent structures, patterns, and
properties during the process of self-organization
in complex systems (Goldstein 1999). Behind
the notion of emergence is the basic idea that
there may be autonomous properties at a higher
(macro) level that cannot be understood by re-
duction to lower (micro) levels (Sawyer 2005).
Here we speak of emergent properties if a group
of components has varying properties showing
deviant behavior at a higher scale level than the
individual components at a lower scale level. De
Haan (2006) distinguishes among three differ-
ent types of emergence: discovery, mechanistic
emergence, and reflective emergence. In systems
F O R U M
exhibiting the latter type of emergence, the ob-
servers are among the objects of the system and
have some reflective capacity, which enables
them to observe the emergence they produce.
Self-organization is a process in which the in-

ternal organization of a complex system increases
in complexity without being guided or managed
by an outside source. This self-organization refers
to the ability to develop a new system struc-
ture as a result of the system’s internal constitu-
tion and not as a result of external management
(Prigogine and Stengers 1984). The notion of
organization is related to an increase in the struc-
ture or order of the system behavior. The new
structures are called dissipative because they fall
apart unless energy is fed from outside to main-
tain them (Prigogine and Stengers 1984). Emer-
gence and self-organization are related to each
other, but they are different. Self-organizing sys-
tems usually display emergence, but not always.
Self-organization exists without emergence, and
emergence exists without self-organization. But
in complex, adaptive systems, emergence and
self-organization occur together.
Complex adaptive systems continuously adapt
to their changing environment. Any kind of
adaptation and all self-organization (see below)
involves variation and selection that is internal to
the system but may well be external to compo-
nents of that system. Complex adaptive systems
constantly create variety, in terms of creating
new components and relations, which provides
a source of novelty in these systems. Selection
then maintains the system in a dynamic equi-
librium by preventing variation or by pushing it
into a certain direction (Green 1994). The selec-
tion process means that the system preferentially
retains or discards variations that enhance or de-
crease its fitness (the internalized system’s mea-
sure for success and failure). Most of the time,

complex adaptive systems are in a period of dy-
namic equilibrium, with ongoing variation and
selection but with selection as the predominating
mechanism. External stimuli can force the system
to shift (across the chaotic edge) to a relatively
short phase of instability and chaos (punctuated
equilibriums), where variation predominates. We
can express system variation in terms of diversity
and heterogeneity. Diversity and heterogeneity are
key features of complex adaptive systems: diver-
sity of components, of relations, of systems be-
havior, and so forth.
Complex Systems and
Industrial Ecology
Without explicit reference to complex systems
theory, industrial ecology (IE) can be consid-
ered as a systems approach to societal, predomi-
nantly production−consumption, systems (Ayres
and Ayres 1996; Ehrenfeld 1997). A modest IE
literature explicitly discusses complexity (e.g.,
Allenby 1999; Kay 2002; Spiegelman 2003). IE,
loosely based on the analogy with ecosystems,
views industrial systems in terms of material and
energy flows and offers a comprehensive perspec-
tive, along with concepts and methods, for in-
depth analysis. It has drawn attention to the need
to minimize energy and material flows and of-
fers models to design ideal−typical “closed-loop
systems” (Ehrenfeld and Gertler 1997). In its
systemic view, IE tends to be somewhat techno-
cratic in that it fixates on measurable and physi-
cal streams and much less or not at all on culture,
governance, agency, and power. It certainly offers

a fruitful basis for debate about (un)sustainable
production (e.g., De Vries and Te Riele 2006),
but it does not shed light on the institutional and
societal embeddedness of these industrial systems.
Although IE thus offers an analytical frame and a
future vision, it is much less concerned with the
process of change in-between and how to orga-
nize that (Green and Randles 2006).
The complex adaptive system and transition
perspectives would consider production and con-
sumption rather as subsystems of a societal system
(Van der Brugge and Van Raak 2007). Produc-
tion of agricultural goods is, for example, largely
determined by financial and institutional regu-
latory schemes, whereas production of mobility
technologies might be much more embedded in
a liberalized, consumer-driven market. In terms
of sustainable development, it is clear that sus-
tainable production and industrial ecology are
concepts that push an increased eco-efficiency in
production (Korhonen 2004). Herein also lies a
danger of optimizing the “wrong” systems by not
fundamentally questioning the need for certain
industrial production or the levels of consump-
tion associated with these systems (Braungart and
187
F O R U M
McDonough 2002). A complex, adaptive view
also needs to include at least the possibility for

structural change, along with the influence of out-
side forces that could evoke such a transition.
Managing Complex, Adaptive
Systems
What does complexity, as described above,
mean in terms of management? Management—in
the context of complexity theory—means influ-
encing the process of change of a complex, adap-
tive system from one state to another. Greater
insight into the dynamics of a complex, adap-
tive system leads to improved insight into the
feasibility of directing it. In other words, appli-
cation of complexity theory can result in a col-
lection of basic principles or guidelines that can
be used to direct complex, adaptive systems. Re-
flexivity (i.e., reflection on the starting principles
defined) is inbuilt with respect to the assump-
tions presumed as well as the possible effects of
such a form of direction. This results in an under-
standing of the limitations of and scope for the
management of complex, adaptive systems and,
at the same time, provides insight into the oppor-
tunities and conditions under which it is possible
to direct such systems. On the basis of theoretical
knowledge and practical experience with com-
plexity theory, we present a number of guide-
lines for management below. These guidelines
are partly descriptive, in the sense of basic princi-
ples, and partly prescriptive, in terms of rules for
management.
• Management at the system level is impor-
tant. Unintended side effects and adverse
boomerang effects can only be recognized

at the system level. A system-level perspec-
tive helps one to get a better insight into
spillovers of the complex problem. This
implies management at various scale lev-
els: Emergent properties might be hidden
at a higher (or lower) scale level but are
already beginning to emerge at other scale
levels.
• The status (in terms of its performance) of the
system determines the way it is managed. The
dynamics of the system create feasible and
nonfeasible means for management: This
implies that content and process are insep-
arable. Insight into how the system works is
an essential precondition for effective man-
agement.
• Objectives should be flexible and adjustable at
the system level. The complexity of the sys-
tem is at odds with the formulation of fixed
objectives. With flexible, evolving objec-
tives, one is in a better position to react to
changes from inside and outside the system.
While being directed, the structure and or-
der of the system are also changing, so the
objectives set should change, too.
• Managing a complex, adaptive system means
using disequilibria rather than equilibriums. In
the long term, equilibrium will lead to stag-
nation and will, in fact, hinder innovation.
Nonequilibrium (the period in-between
multiple equilibriums) means instability
and chaos, which form an important impe-

tus for fundamental change. The relatively
short periods of nonequilibrium therefore
offer opportunities to direct the system
in a desirable direction (toward a new
attractor).
• Creating space for agents to build up alterna-
tive regimes is crucial for innovation. Stimulat-
ing emergence and divergence is crucial for
innovation. A diversity of emerging niche
agents at a certain distance from the regime
can effectively create a new regime in a pro-
tected environment. For this to happen, a
certain degree of protection is needed to
permit agents time, energy, and resources.
Managing Societal Systems
The management principles underlying tran-
sition management are built around the paradox
that societal change is too complex to handle
in terms of managing, but still we have formu-
lated a set of relatively simple rules regarding
how to influence societal change. The rationale
for handling this management paradox is that
gaining insight into societal complexity by tak-
ing a complex systems approach can help one
to fathom the possibilities for influencing soci-
etal complexity. This logically connects content
and process, which are explicitly linked in tran-
sition management: The complexity analysis of a

F O R U M
societal system under observation also determines
the opportunities for managing such a system
(Loorbach 2007, 86). Analytical lenses such as
the multistage, multilevel (Rotmans et al. 2001),
and multipattern concepts (de Haan and Rot-
mans 2008) provide us with opportunities for
identifying patterns and mechanisms of transi-
tional change. Once we have identified transi-
tional patterns and mechanisms, we can deter-
mine process steps and instruments to influence
these patterns and mechanisms. Our approach
differs from earlier attempts to use a complex
systems approach for management of policy is-
sues (e.g., Kickert 1991; Kooiman 1993; Stacey
1996) in that it is more oriented toward reflexive
planning—not deterministic but reflexive rules.
We have formulated rules for managing societal
change, but we realize that once we apply these
rules in a process context, they need to be ad-
justed because the conditions and dynamics (con-
tent) will change as a result of the application of
these rules. Therefore, learning, searching, and
experimenting are crucial in transition manage-
ment. In that sense, it has similarities with strate-
gic niche management—that is, experimenting
with new technologies in an experimental space
(Kemp, Schot and Hoogma 1998).
Principles of Transition
Management
Here we briefly describe the theoretical prin-
ciples of transition management that arise from
complexity theory. The first principle is that of

creating space for niches in so-called transition are-
nas. The notion of arena originates from that part
of complexity theory that indicates that a small
initial change in the system may have a great im-
pact on the system in the long run. In systems
terms, this is called an emergent structure: an en-
vironment that offers some protection for a small
group of agents. The self-organizing capacity of
the system generates new, dissipative structures
in the form of niches. A niche is a new structure,
a small core of agents, that emerges within the
system and that aligns itself with a new configu-
ration. The new alignment is often the emergent
property of the system. An emergent structure
forms around niches, stimulating the further de-
velopment of these niches and the emergence of
niche regimes.
The focus on frontrunners is a key aspect
of transition management. In complex system
terms, frontrunners are agents with the capac-
ity to generate emergent structures and operate
within these deviant structures. They can only do
that without being (directly) dependent on the
structure, culture, and practices of the regime. In
the context of transition management, we mean
by frontrunners agents with peculiar competen-
cies and qualities: creative minds, strategists, and
visionaries. If a new regime is to be created ef-
fectively, agents are needed at a certain distance
from that regime.
Another principle of transition management
is guided variation and selection. This is rooted in
the notions of diversity and coherence within

complexity theory. Diversity helps avoid rigidity
within the system; without it, the system could
respond flexibly to changes in its environment.
Coherence refers to the level of interrelatedness
among the entities of a complex system. In the
equilibrium phase, there is continuous variation
and selection, but when a regime settles, it be-
comes the dominant selection environment and
thus decreases the diversity. But a certain amount
of diversity is required for us to explore a variety of
innovative options instead of looking for the op-
timal solution. Rather than selecting innovations
in a too early stage, we keep options open to learn
about the pros and cons of available alternatives
before making a selection. Through experiment-
ing, we can reduce some aspects of the high level
of uncertainty, which leads to better informed
decisions.
The principle of radical change in incremental
steps is a paradox that is derived from complex-
ity theory. Radical, structural change is needed
to erode the existing deep structure (incumbent
regime) of a system and ultimately dismantle
it. Immediate radical change, however, would
lead to maximal resistance from the deep struc-
ture, which cannot adjust to a too fast, radical
change. Abrupt forcing of the system would dis-
rupt the system and would create a backlash in
the system because of its resilience. Incremental
change allows the system to adjust to the new cir-
cumstances and to build up new structures that
align to the new configuration. Radical change in
189

F O R U M
incremental steps implies that the system heads
in a new direction toward new attractors, but in
small steps.
Empowering niches is an important principle of
transition management. By empowering, we mean
providing with resources, such as knowledge, fi-
nances, competences, lobby mechanisms, exemp-
tions of rules and laws, and space for experiment-
ing (Avelino 2007). An empowered niche may
cluster with other empowered niches and emerge
into a niche regime. Multiple regimes coevolve
with each other—a dominant regime and one or
more niche regimes. Crucial is the coevolution of
a regime within the existing power structure and
a niche regime outside the power realm. Coevolv-
ing regimes influence each other in an irreversible
manner, with an unknown outcome. The niche
regime may take over the incumbent regime but
may also be absorbed and encapsulated by the
incumbent regime.
Anticipation of future trends and develop-
ments, with account taken of weak signals and
seeds of change that act as the harbingers of the
future, is a key element of a proactive, long-term
strategy of transition management. This future
orientation is accompanied by a strategy of adap-
tation, which means adjusting while the struc-
ture of the system is changing. This requires ad-
equate insight into the dynamics of a complex

system. Although in general, complex system dy-
namics are highly nonlinear and unpredictable,
there are periods when the system behaves in a
relatively orderly manner and, to a limited ex-
tent, is predictable. But there are also periods in
which chaos rules and the behavior of the system
is quite unpredictable. So although the degree of
predictability is rather small, transitions do imply
generic patterns that indicate the future pathway.
Path dependency is an example of such a pattern.
A transition is the result of a coevolution
of economic, cultural, technological, ecological,
and institutional developments at different scale
levels. So transitions, by definition, cross multiple
domains and scales (Rotmans et al. 2001). Com-
plex systems also involve multiple domains and
scales. They are nested and encompass various
organization levels, where higher level structures
arise from interaction between lower level com-
ponents. The transition literature often makes
clear that there is a macro level at which novel
emergent structures arise from the interactions
between components at the micro level. Every
transition domain has its own dynamics: Cul-
tures only change slowly, but economic changes
take place in the short term, whereas institutional
and technological changes are somewhere in be-
tween. The various domains shift over each other
and constantly influence each other through in-
teractions and feedbacks. The resulting dynamics
are a hybrid picture of alternating fast and slow
change. Analyzing the interactions and feedbacks
across levels and domains is of importance for
identifying patterns and mechanisms of transi-

tional change and for determining instruments to
influence these patterns and mechanisms.
Through experimental implementation of the
complex adaptive systems approach to transitions
in societal systems, we have translated the theo-
retical principles underlying transition manage-
ment into so-called systemic instruments. Table 1
summarizes the main insights from complexity
theory and their translation into theoretical prin-
ciples of transition management as well as these
system instruments. The next section describes
a framework for doing transition management in
practice, using theoretical principles of complex
systems theory.
Transition Management: The
Framework
The challenge with transition management
is to translate the above, relatively abstract
management rules into a practical management
framework without losing too much of the com-
plexity involved and without becoming too pre-
scriptive (Rotmans and Kemp 2008). We have
attempted this by delineating transition manage-
ment as a cyclical process of development phases
at various scale levels. In complex system terms,
transition management can be described as con-
sisting of the following steps (Loorbach 2007;
Loorbach and Rotmans 2006):
1. Stimulate niche development (emergence,
variation) at the micro level and try to
interconnect niches with the same di-
rection. In the transition management

framework, one does this by establish-
ing and organizing a transition arena, a
F O R U M
Table 1 Linking of complexity characteristics, theoretical
principles of transition management, and systemic
instruments for transition management
Complexity Theoretical principles of Systemic instruments for
characteristics transition management transition management
Emergence Creating space for niches Transition arena
Dissipative structures Focus on frontrunners Transition arena
and competence
analysis
Diversity and coherence Guided variation and selection
Transition experiments and transition
pathways
New attractors, punctuated
equilibriums
Radical change in incremental
steps
Envisioning for sustainable futures
Coevolution Empowering niches Competence development
Variation and selection Learning by doing and doing by

learning
Deepening, broadening, scaling up
experiments
Interactions, feedbacks Multilevel approach,
multidomain approach
Complex systems analysis
Patterns, mechanisms Anticipation and adaptation Multipattern
and multilevel analysis
quasi-protected area for frontrunners
(niche players and change-inclined regime
players).
2. Try to find new attractors for the system
by developing a sustainability vision and
derived pathways at the macro level that
can act as guidance for niche development.
3. Try to stimulate the formation of niche
regimes by creating coalitions and new net-
works around the transition agenda and
the different pathways.
4. Create diversity by setting out transition
experiments that are related to specific
pathways onto the vision.
5. Select the most promising ones that can
be scaled up to a higher level as you learn
from these experiments and develop an up-
scaling strategy.
6. Try to further modulation between the mi-

cro and macro levels (coevolution) by ad-
justing the vision, agenda, and coalitions,
if necessary, by monitoring and evaluat-
ing (analyzing patterns and mechanisms)
the transition management process, after
which the cycle starts again.
For the sake of simplicity, we present the cy-
cle of transition management as a sequence of
steps, as presented in figure 1. In practice, how-
ever, there is no fixed sequence of steps in tran-
sition management, and the steps can differ in
importance in each cycle. In the real world, the
transition management activities are carried out
partially and completely in sequence, in parallel,
and in a random sequence.
In effect, transition management comes down
to creating space for frontrunners (niche play-
ers and change-inclined regime players in tran-
sition arenas), forming new coalitions around
these arenas, driving the activities in a shared
and desired direction, and developing coalitions
and networks into a movement that puts soci-
etal pressure on regular policy. In the transition
management framework, activities related to the
content (integrated systems analysis, envision-
ing, agenda building, and experiments) are linked
to activities related to the process (network and
coalition building, execution of experiments, and
process structuring). The preferred actors to be
involved (based on the necessary competencies)
and instruments (e.g., scenarios, transition agen-
das, monitoring instruments) are derived from
this framework. The four activity clusters de-

picted in figure 1 are described in more detail
below.
Integrated Systems Analysis and Actor
Selection
An integrated systems analysis forms the basis
of every transition management process, provid-
ing a common ground for a variety of actors and
191
F O R U M
Figure 1 The transition management cycle.
Source: Loorbach (2007)
enough information for informed debates and dis-
cussions. Informed insight into the complexity
of the system, its major defining subsystems, the
dominant causal relations, feedback loops, the
roots, and the nature of structural problems estab-
lish a baseline as well as conditions for discussing
visions, strategies, and actions in the future. In
addition, such a preliminary assessment yields
knowledge about the main actors influencing the
system in both a conservative and an innovative
way and helps to guide the selection of partici-
pants for the transition arena. Such a selection
is of vital importance. Participants need to have
some basic competencies at their disposal: They
need to be visionaries and frontrunners, and they
must have the ability to look beyond their own

domain or working area and be open-minded.
Problem Structuring and Envisioning:
Establishment of a Transition Arena
The transition arena is best viewed as a vir-
tual network, which is a legitimate experimen-
tal space in which the actors involved use social
learning processes to acquire new knowledge and
understanding that leads to a new perspective on
a transition issue. Such a transition arena has to
be supported by political actors or regime powers
but not dictated by them—for example, through
the support of a minister or a director. In gen-
eral, around 15 to 20 frontrunners (i.e., pioneer-
ing individuals) are involved in the beginning of
the transition arena, although, over time, only
around 5 will become the core group.
Within the transition arena, multiple in-
depth discussions take place, structured accord-
ing to the system approach. Facilitators synthesize
discussions and work toward convergence of per-
spectives, assumptions, and ambitions. The tran-
sition arena develops a shared understanding of
the persistence of a problem at the level of a soci-
etal system, the necessity of a transition or radical
change, and the definition of the challenge this
poses. Key outcomes are a new, shared perspec-
tive; language to discuss the transition; and the
definition of a set of guiding principles for the
envisaged transition. This relates to the earlier
mentioned notion of emergence (De Haan 2006):
The awareness of and insight into the complexity
of their environment helps individuals to better

F O R U M
understand the complexity and realize that they
can, on a small scale, exert influence.
Development of Sustainability Images,
Pathways, and a Transition Agenda
Transition images are the “translation” of the
generic guiding principles or “sustainability cri-
teria” to specific concrete settings, subsectors,
or themes. These images must be appealing and
imaginative so as to be supported by a broad range
of actors and inspire and guide short-term action.
Inspiring images are useful for mobilizing social
actors and represent a consensus among different
actors on what sustainability means for a specific
transition theme, which could evolve over time as
new insights emerge. Transition images embrace
multiple transition pathways to represent a vari-
ety of possible options. They include transition
goals, which are qualitative rather than quan-
titative and are multidimensional, representing
the three dimensions of sustainability: economic,
ecological, and sociocultural.
Various transition pathways lead to a partic-
ular transition image (i.e., a sustainability vision
comprises various transition images), and from
various transition images a particular transition
pathway may be derived. The transition images

can be adjusted as a result of what has been
learned by the players in transition experiments.
The transition process is thus a goal-seeking pro-
cess, in which the transition visions and images,
as well as the underlying goals, change over time.
During the course of the transition process, the
actors will choose the visions and images that
appear to them as the most innovative, promis-
ing, and feasible. The transition agenda contains
content objectives, process objectives, and learn-
ing objectives. Although the transition visions,
images, and objectives form the guidelines for
the transition agenda, the transition agenda it-
self is the compass for the frontrunners, to which
they can refer during their search and learning
process.
Initiation and Execution of Transition
Experiments and Mobilization of Actors
From the transition vision, images, and path-
ways, transition experiments can be derived that
are either related to or combined with existing
activities. Transition experiments are high-risk
experiments with a social learning objective that
are supposed to contribute to the sustainability
goals at the systems level and should fit within
the transition pathways. It is important to for-
mulate sound criteria for the selection of exper-
iments and to make the experiments mutually
coherent. The crucial point is to measure to what
extent the experiments and projects contribute
to the overall system sustainability goals and to
measure in what way a particular experiment re-
inforces another experiment. Are there specific

niches for experiments that can be identified?
What is the attitude of the current regime toward
these niche experiments? The aim is to create a
portfolio of transition experiments that reinforce
each other and contribute to the sustainability
objectives in significant and measurable ways.
Around and between these experiments, all sorts
of actors can be involved that will not engage
regularly in debates about long-term issues: small
businesses, consumers, citizens, local groups, and
so on. Here, as well, the emphasis is on involving
frontrunners.
Monitoring and Evaluating the Transition
Process
Continuous monitoring is a vital part of the
search and learning process of transitions. We
distinguish between monitoring the transition
process itself and monitoring transition manage-
ment. Monitoring the transition process involves
attending to physical changes in the system in
question, slowly changing macro-developments,
fast niche developments, seeds of change, and
movements of individual and collective actors at
the regime level. Monitoring of transition man-
agement involves different aspects. First, the ac-
tors within the transition arena must be moni-
tored with regard to their behavior, networking
activities, alliance forming, and responsibilities
and also with regard to their activities, projects,
and instruments. Next, the transition agenda
must be monitored with regard to the actions,
goals, projects, and instruments that have been
agreed on. Transition experiments need to be
monitored with regard to specific new knowl-

edge and insight and how these are transferred
193
F O R U M
but also with regard to the aspects of social and
institutional learning. Finally, the transition pro-
cess itself must be monitored with regard to the
rate of progress, the barriers, and the points to be
improved, for example. Evaluating these moni-
toring aspects within each phase may stimulate a
process of social learning that arises from the in-
teraction and cooperation between different ac-
tors involved.
In each of the above activity clusters, coali-
tion and network formation are of vital impor-
tance, combined with the systemic structuring
and synthesizing of discussions. The transition
arena is meant to stimulate the formation of new
coalitions, partnerships, and networks that to-
gether create a new way of thinking. Mostly,
coalitions emerge around transition pathways or
experiments or around specific subthemes, where
subarenas arise. The very idea behind transition
management is to create a societal movement
through new coalitions, partnerships, and net-
works around arenas that allow for building up
continuous pressure on the political and market
arena to safeguard the long-term orientation and
goals of the transition process.

Conclusions
In this article, we have presented a transition
management framework for addressing persistent
societal problems that is grounded in complex
systems theory. Variation and selection, emer-
gence, coevolution, attractors, diversity and co-
herence, and interactions and feedbacks are key
elements of transition management. The under-
lying premise is that a better understanding of the
dynamics of complex, adaptive systems provides
insight into the opportunities, limitations, and
conditions under which it is possible to influence
such systems. This implies a strong linkage of con-
tent and process: The combination of analytic in-
sights into systems complexity and understanding
of the process of governance complexity is new
and has resulted in a set of management principles
that forms the basis for the management frame-
work. The management principles are reflexive
rather than deterministic, reflecting a belief that
transitions toward sustainability can be directed
to a limited degree. Applying these principles im-
plies adjusting them to the new conditions and
dynamics, which will change when these princi-
ples are applied. On the basis of this approach,
the management framework itself has been the
result of experiences within testing grounds and
has evolved in the past several years. The concept
of transition management and the derived frame-
work is promising but still needs to largely prove
itself empirically. It is a great challenge to empir-
ically validate the partly descriptive and partly
prescriptive parts of transition management in
such a manner that the framework can be further

developed and used in a broad international so-
cietal context. One of its potential contributions
lies in application to nonenvironmental domains,
such as health care and city restructuring. In
this sense, transition management can be consid-
ered as an extension of and a step beyond indus-
trial ecology into broad societal (socioeconomic)
systems.
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About the Authors
Jan Rotmans is professor in transition man-
agement at the Erasmus University Rotterdam,
Rotterdam, the Netherlands, and scientific di-
rector of the Dutch Research Institute for Tran-
sitions (Drift) at the same university. Derk Loor-
bach is a senior researcher at Drift and received
his doctorate in transition management in
2007.

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