Complexity in Governance Network Theory

In this article, we discuss how complexity is viewed in governance network theory. The article provides a systematic elaboration of the notion of complexity, distinguishing three types: substantive, strategic , and institutional complexity. We argue that dealing with these types of complexity in networks is essentially a matter of mutual adaption and cooperation. An important explanation for the occurrence of deadlocks, breakthroughs and outcomes is the presence and the quality of attempts to manage complex interaction processes in networks.


Complexity in Governance Network Theory
One aspect that is clearly emphasized in most of the literature on governance networks is the complexity of the governance processes within these networks.We define governance networks as sets of autonomous yet interdependent actors (individuals, groups, organizations) that have developed enduring relationships in governing specific public problems or policy programs.The complexity of these networks is implied in one of the most important books on networks: Hanf and Scharpf's Interorganizational Policy Making: Limits to Coordination and Central Control (1978).The main argument throughout this seminal work, which is more frequently cited in Europe than in the US, is that although the actors who deal with policy problems are interdependent to each other for resources, there usually are no governance structures set up that deal with these interdependencies.
Network theory did not start with the publication of Hanf and Scharpf's book; it has a long tradition in both political science and (inter-)organizational science, which goes back to the early 1960s (for an overview, see Klijn & Koppenjan, 2012).Hanf and Scharpf were, however, among the first to use a network perspective to address the "wicked" character of policy problems-a term that was first used by Rittel and Webber (1973) and has been the most prominent concept in the network studies in the field of public administration.Hanf and Scharpf direct attention to where network theory, which at that time differed significantly from many other perspectives in public administration: that network theory tends to downplay the central control possibilities of policy and decision-making processes and instead emphasizes the complexity of these processes.In this article, we explore how complexity is conceptualized and analyzed in the network perspective and how it is used to understand and explain decision-making processes within networks.

Three Types of Complexity in Governance Networks
Complexity, most of the network literature emphasizes, is not simply caused by the fact that multiple actors are present within governance, although this is an important condition.The presence of diverse components in a system makes a system complicated, but not complex (Gerrits, 2012).Complicatedness can be tamed by the development of knowledge on these components and their relationships.Complexity cannot be tamed, however.Complexity reflects the dynamic nature of a system's components and their relationships, making it very hard to predict how the system will behave and which outcomes will be produced (Kickert, Klijn, & Koppenjan, 1997;Koliba, Meek, & Zia, 2010;Morçöl, 2012;, van Buuren, & Gerrits, 2009).Governance networks are multi-actor systems that are not simply complicated, but complex.If we look at the wide literature on networks, and the literature that strongly influenced the network literature (for an extensive elaboration of the latter literature, see Klijn & Koppenjan, 2012), we can identify three types of complexity in these networks: substantive complexity, strategic complexity, and institutional complexity (Koppenjan & Klijn, 2004).

Substantive Complexity
Substantive complexity is about the content of the problem addressed and the nature of solutions under consideration.In most of the mainstream Public Administration literature, substantive complexity is first of all attributed to the lack of knowledge and information.Complexity then is considered to be the result of the absence of data, or the absence of research or access thereto.This, so is often argued, is a result of the state of the scientific knowledge available at the time, and will be solved by further scientific research and development.The increase in the levels of CO 2 in the atmosphere causes the temperature on earth to rise (the greenhouse effect) but, as long as it is not known what specific concentrations have what kinds of effects, it is impossible to assess the magnitude of this rise in temperatures.
Substantive complexity, however, is not caused only by the absence of information and knowledge.What is often more important is that information is available, but its validity is contested.Since actors have different perceptions of problems and view them from different frames of reference, they interpret the available information differently (Schön & Rein, 1994;Fischer, 2003).
The presence of different perceptions is really what makes policy problems wicked (Rittel & Webber, 1973;Head, 2008;Weber & Khademian, 2008)."Wicked problems" are complex, not only because they are technical in nature, or involve many components or actors, but more so because the actors involved in them have different perceptions of their nature and its solutions.Often wicked problems can be found in the areas of physical planning, environmental issues, and social problems (e.g.elderly care, social welfare issues, crime).The value differences among actors and the need for coordinated action among them make the policy problem complex (Head, 2008;Provan & Kenis, 2008).
The substantive complexity of wicked problems cannot be resolved by collecting more information, because this complexity is not caused by information shortages, but by the lack of a joint frame of reference and shared meaning among actors.As a result, new information can be interpreted in different ways.Also, since all actors may engage in information gathering in their own way, information may be diverse, conflicting, or hard to understand, which may result in information overload and the articulation of conflicting truths.Information gathering in order to deal with wicked problems in networks will not reduce complexity, but on the contrary, will contribute to it (Klijn & Koppenjan, 2004).

Strategic Complexity
Most network theorists would argue that governance networks are characterized by strategic complexity, in addition to substantive complexity (e.g.Scharpf, 1978Scharpf, , 1997;;Agranoff & McGuire, 2003).Strategic complexity is a result of the strategic choices actors make when they articulate complex problems (Allison, 1971;Kingdon, 1984).Because actors are autonomous and networks lack clear hierarchical control forms, each actor choses his/her own strategy.As a result, various or even conflicting, strategies may develop around a complex issue (Koppenjan & Klijn, 2004).Furthermore, actors anticipate each other's strategic moves and respond to them (Scharpf, 1997).Because of these interactions, it is difficult to predict what strategies actors will choose, how strategies will evolve during the process, and how the interactions of these strategies will influence the process of problem-solving.
Complexity and indeterminacy characterize the interactions within governance networks.It is not easy to reduce or eliminate the strategic complexity that is created by these interactions.In a complex society characterized by interdependencies (Castells, 1997), actors have discretion to make their own choices.Unexpected strategic turns thus are an intrinsic characteristic of processes in networks.

Institutional Complexity
Governance networks are also characterized by institutional complexity.Institutions can be defined as sets of rules regulating behavior (Scharpf 1997;Ostrom, 1990).Networks are enduring relationships between actors that have resulted in the emergence of sets of rules that characterize these relationships.Each network will have a unique set of rules.Network rules may reduce complexity and enhance cooperation, since they make the behaviors of actors more predictable.However network rules may compete with other sets of rules stemming from informal groups, specific professional roles, organizations, national laws, etc. (March & Olsen, 1989;Scott, 1995).If the number of rules grow, become inconsistent, opaque, and not well understood, they may generate complexity instead of predictability.What is more, wicked problems cut across existing demarcations between organizations, administrative levels, and networks.As a result, interactions become more difficult because the behavior of actors representing various networks will be guided by the different rules and frames of reference; they will have other routines and speak another professional language (Ostrom, 2005;Baumgartner & Jones, 2009).

Coping with Complexity: The Need for Interaction
Network theorists suggest that public policymaking and service delivery in governance networks require coping with complexity, because problem definitions, solutions, and knowledge are contested, strategic interactions of actors are hard to anticipate.All of these factors lead to unpredictable outcomes, and different institutional regimes produce ambiguity about which rules to follow.
The assumption that underlies network theory is that handling the complexity of difficult societal problems requires mutual adaption and cooperation among network actors (Scharpf, 1978(Scharpf, , 1997;;Marin & Mayntz, 1991;Rhodes, 1997;Mandell, 2001).As a result of the resource dependencies among network actors, some actors may have opportunities to "veto" the decisions made by others.If actors do not negotiate their strategies with others and focus solely on the accomplishment of their own goals, by using so-called goalone strategies, these strategies are likely to lead to blockades and stagnation and hence to inefficient and ineffective decision making (Koppenjan & Klijn, 2004;Agranoff & McGuire, 2003).
Go-alone strategies in networks generally lead to substantively poor and sub-optimal problem solving.They lead to poor solutions because knowledge is dispersed across many actors (Head, 2007;Hess & Ostrom, 2006) and therefore these strategies result in solutions that fall substantively short in tackling complex problems (Fischer, 2003).They lead to sub-optimal solutions because these strategies usually start with the goal of optimizing particular values, but often, multiple values are involved in decision making, and solutions must reflect this multiplicity of values (Koppenjan & Klijn, 2004).Go-alone strategies tend to optimize particular values at the expense of others in the decision-making process and outcome and therefore contradict this multiplicity of values.
Cooperation is necessary in networks, but it is not easy and it comes at a cost.An interaction process may stagnate or be blocked because actors have different perceptions, they pursue conflicting interests or strategies, they lose interest in an issue, or the transaction costs of cooperation are too high (Williamson, 1996).Yet, because of their resource dependencies, actors who deal with wicked problems may not have any other choice but to keep on looking for ways to cooperate.

Explanations of the Processes and Outcomes of Interactions within Governance Networks
Due to their complexity, processes within networks aimed at dealing with wicked problems are characterized by stagnation and blockades.An important question, therefore, is how stagnations and blockage on the one hand, and breakthroughs and outcomes on the other, can be explained.In this section we group the explanations provided by the network literature into three categories: cognitive, social, and institutional explanations.In addition, we introduce a fourth explanation: the presence and quality of network management.

Cognitive Explanations: Substantive Complexity as Cause
The stagnation in a network may originate from the varying perceptions or frames about the nature, causes, and effects of problems and their solutions (Fischer, 2003;Hajer & Wagenaar, 2003).There may be differences of opinion about the nature of a problem and the quality of the available knowledge and solutions.If actors are not aware of the fact that they have different perceptions , this may result in misinterpretations and discussion in which they talk past each other, without reaching an agreement.In extreme cases, this may result in an enduring "dialogue of the deaf" (Wildavsky & Tenenbaum, 1981;Van Eeten, 1999).If actors try to convince one another, and involve experts or commission research in order to strengthen their arguments, the cognitive differences may be deepened.Under these conditions, debates on environmental problems or the construction of infrastructural projects, for instance, may result in a war of reports.Scientific knowledge may serve to enhance knowledge conflicts and thus substantive complexity, instead of decreasing them (Nowotny, Scott, & Gibbons, 2001;Bijker, Bal, & Hendriks, 2009).
A substantive breakthrough is required to break cognitive impasses.Overcoming ambiguity, misunderstandings, and differences of opinion requires a convergence of ideas and perceptions and the development of a mutual understanding of situations and events.This calls for frame reflection and a cross-frame debate, in which problems and solutions are formulated anew (Fischer, 2003;Schön & Rein, 1994;Hajer & Wagenaar, 2003).Cognitive variety in terms of solutions, problem definitions, and the scope within which solutions are sought is an important precondition for frame reflection and learning.The turnover of key persons in networks and changes in actor constellations may result in social variation with similar effects (Sabatier & Jenkins-Smith, 1993).Another important factor that will help frame reflection and learning is the degree to which actors are able to include expertise and organize research in a way that helps to identify joint knowledge questions and supports the process of joint sense making and learning (Hajer & Wagenaar, 2003;Head, 2008).This section shows that substantive complexity is not the same as the presence of a variety of perceptions.Cognitive variety is actually a condition for substantive breakthroughs.It is the absence of a joint frame of reference and a mutual understanding that causes stagnations and deadlocks.

Social Explanations: Strategic Complexity as Cause
Deadlocks and stagnation emerge in networks when the strategies of those actors whose resources are indispensable for dealing with the problem are uncoordinated or in conflict, or when there is no interaction among actors.Actors are often insufficiently aware of their mutual dependencies or they fail to discover a mutual interest.As a result, they choose go-alone strategies that bring them into conflict with one another.Governance networks are not "cozy places of harmony"; they can be characterized by a high degree of conflict.A lack of dedication to solving a problem may also underlie stagnation.Actors may simply not be interested in investing their resources.
Conflicts and stagnation may result in transaction costs and make it difficult to achieve coordination (Williamson, 1996;Huxham & Vangen, 2005).A breakthrough can emerge if parties are able to align their strategies.For this to occur, strategic uncertainties must be reduced.This may be achieved by the formulation of process agreements with ad hoc rules that help to make the behavior of actors more predictables.Furthermore, when one or more actors operate as brokers, facilitators, conflict managers, or arbiters, there is an increased chance of preventing, or at least limiting, the destructive influence of deadlocks, realizing breakthroughs, and making decisions (Agranoff & McGuire, 2003;Mandell, 2001).If actors succeed in formulating a proposal for a solution that links various objectives and offers the perspective of an improved situation for most of the actors, a strong incentive may be created to cooperate.A well-known example of such a win-win-solution in the Netherlands is the breakthrough reached in an enduring conflict on the construction of a road within an urban area: the case of the Sijtwende tunnel.The municipality opposed the road that was planned by the central government, since it wanted to develop real estate in the area.A private developer suggested that it be allowed to build a tunnel.In this case the extra costs for tunnel construction would be partly covered by a contribution paid from the profits of real estate development (Koppenjan, 2005).
Our discussion in this section shows that the presence of many actors is not the same as social complexity.It is the lack of coordination mechanisms that causes stagnation and deadlocks.The presence of social variety is one of the conditions to realize breakthroughs.

Institutional Explanations: Institutional Complexity as Cause
Stagnation or deadlocks in network processes can be caused by a weakly developed institutional structure, i.e. the absence of a clear set of mutually shared rules.Mutually shared rules help to reduce the risks involved in participating in interactions in networks.They also often have a mitigating effect upon conflicts, and they provide procedures for enhancing interaction and managing conflict.
A weakly developed institutional structure does not imply that there are no institutional rules.The problem is more one of institutional complexity, i.e. many different rules coming from various institutional backgrounds.For instance, attempts at building new integrated health care networks (like initiatives to reduce alcohol use among young people, or to reduce the problem of overweight) encounter problems because the initiators have to deal with institutional rules from various policy sectors.The incompatibility of orientations, rules, and languages that guide the parties' behaviors makes the process of dealing with wicked problems something like building the Tower of Babel (March & Olsen, 1989;Ostrom, 2005).
Institutional characteristics, such as the nature of rules and trust among actors, can affect stagnation and cooperation as well.Cooperation is more difficult in networks with rigid rules than in networks with less rigid rules.Various empirical studies have shown that a high level of trust in networks is related to better network performance, indicating that trust is a very important for cooperation in networks (Provan, Huang, & Milward, 2009;Klijn, Edelenbos, & Steijn, 2010).Networks with a strong institutional structure, such as recognizable rules and relatively strong trust relations between actors, may result in lower transaction costs because provisions that further cooperation do not need to be developed from scratch, and parties can rely on existing arrangements.
Institutional breakthroughs occur when dysfunctional rules change or are replaced.As a result, new behaviors may emerge, which result in breakthroughs in interaction processes.Also, the creation of new organizational structures and formal rules can enhance the cooperation between parties involved and thus, indirectly, help to realize a breakthrough.The creation of new rules that guide actors' behaviors or the creation of more trust is not something that can be accomplished in the short run, however.Institutional change is often the result of unintended developments or events.(Ostrom, 2005;Koppenjan & Klijn, 2004).The uncertainty on institutional changes is a source of institutional complexity and a cause for stagnations in network processes itself.

Network Management as an Explanation
We propose an additional explanation for the occurrence of deadlocks, breakthroughs, and the emergence of policy outcomes in governance networks: the presence or absence of attempts to manage complex interaction processes in networks and the quality of these management efforts.Network management can be seen as a way to address the complexity in networks.It may be focused on substantive complexity by enhancing variety, supporting learning processes, and arranging joint research.Network management can address strategic complexity by initiating and strengthening interactions, arranging relationships, and mediating conflicts.Network management may be used to deal with institutional complexity by engaging in institutional redesign (changing rules, resource distributions, and organizational relationships).
Because these three forms of complexity are interrelated, the efforts to manage them cannot be isolated from each other.A dialogue of the deaf (cognitive complexity) may be address by introducing a new actor (addressing strategic complexity).Institutional complexity may be overcome by initiating process agreements (addressing strategic complexity).
Network management activities may have an incidental, ad hoc nature.It may be a onetime response of one of the actors to a deadlock.Network management may also be a continuous and planned activity.Furthermore, network management may be in the hands of one actor, a governmental agency or another organization, but it may also be a role that alternates among actors.And, last but not least, network management may be contestedboth in terms of who performs the function and what strategies are performed-thus adding to the complexity that characterizes governance networks, rather than reducing it.

Conclusions
In this article, we have elaborated on the network perspective of complexity.In particular we identified three types of complexity in networks.We also showed how these types of complexities can be used as explanations for stagnations and breakthroughs in network processes.Attempts at managing complexity do not automatically lead to solving wicked problems.They are, rather, focused on achieving conditions under which it becomes possible to deal with wicked problems.It involves enhancing learning processes between parties aimed at substance, process and institutions.Network management is focused on improving cognitive learning processes, on joint image building, enrichment and goal entanglement, and on strategic and institutional learning processes, aimed at enhancing cooperation between parties with diverging interests, perceptions and objectives, roles (experts, citizens, users, civil servants, entrepreneurs, politicians) and diverging institutional backgrounds.