Peer-Reviewed Assignment

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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

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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

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(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

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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

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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

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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

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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

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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

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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

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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

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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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