Governance Networks as Complex Adaptive Systems
Governance networks are not simply systems, but rather complex systems capable of emergent qualities, adaptive to changing conditions, with the capacity to self- organize. Complex systems are “one whose component parts interact with sufficient intricacy that they cannot be predicted by standard linear equations; so many variables are at work in the system that its overall behavior can only be understood as an emergent consequence of the holistic sum of all the myriad behaviors embedded within” (Levy, 1993). Systems theory and complexity theory find a common denominator in the role that “feedback and interactions” central factor to the understanding of society and its organizations” (Haynes, 2003, p. 90). Complex systems are understood as dynamic spaces governed by nonlinear processes. Donella Meadows notes, “Nonlinearities are important not only because they confound our expectations about the relationship between action and response. They are even more important because they change the relative strength of feedback loops. They can flip a system from one mode of behavior to another” (2008, p. 92). Thompson observes that “the non-linearity of complex systems means that small amounts of changes in inputs can have dramatic and unexpected effects on outputs” (2003, p. 136).
A central feature of complexity theory hinges on the notion that a few relatively simple rules can have tremendous effects on the behaviors of a system. These simple rules serve as the foundations of the development of “wildly diverse self-organizing structures” (Meadows, 2008, p. 80).
The consideration of governance networks as complex systems must allow for the development of the network-wide capacity to exhibit self-organizing qualities. According to Meadows, “the capacity of a system to make its own structure more complex is called self-organization” (2008, p. 79). Drawing on studies of complex adaptive systems in natural and social networks, Miller and Page suggest instances in which we “find robust patterns of organization and activity in systems that have no central control or authority. We have corporations—or, for that matter, human bodies and beehives—that maintain a recognizable form and activity over long periods of time, even though their constituent parts exist on time scales that are orders of magnitude less long lived” (Miller and Page, 2007, p. 7). In essence, we may understand self-organization as a property of both whole governance networks and particular subsystems of network actors.
Thompson applies a biological and evolutionary lens to the description of complex systems:
Multistranded clumped networks that form a kind of non-linear (rhizome-like) organizational structure, containing different relational principles of connectivity and heterogeneity, are always “pregnant” with the possibility of breakdown and breakup, leading to new trajectories and transformations in a self-organizing framework that overcomes the twin obstacles presented by “necessary evolutionary advance” and “path-dependency.” (Thompson, 2003, p. 11)
A picture of governance networks as organic, ever-evolving ecosystems of organizations, groups (which we have been referring to as communities of practice), and individuals emerges. Considering governance networks as not simply complicated social structures, but as complex social structures, brings certain distinct features into focus. Complexity, in this context, “is equated with the number of different items or elements that must be dealt with simultaneously by the organization (Anderson, 1999). But its distinctive feature is to stress the world as a system in construction, a dynamic formulation encouraging the notion of continual process of spontaneous emergence (Thrift, 1999)” (Thompson, 2003, p. 136).
Although some traditional applications of social network analysis view networks as static systems, or at least treats them as a one point in time or snap shot of reality, we have been describing governance networks as being “relatively stable and complex pattern[s] of relationships among multiple interdependent and selforganizing element which also constitutes a self-organizing system as a whole” (Morcol and Wachhaus, 2009, p. 45). Goktug Morcol and Aaron Wachhaus have compared networks and complex systems and noted their conceptual similarities “(a) ... networks and complex systems are composed of multiple interdependent components (actors or agents); (b) both are relatively stable patterns of relationships, although complex systems are defined in more dynamic terms; (c) they are [both] self-organizing.” (2009, p. 46). The value of applying the network structures of nodes and ties to the relationships between active agents operating within complex adaptive systems helps to make “the abstractions used by complexity theory concrete (Carroll and Burton, 2000; Costa et al., 2007)” (Mischen and Jackson, 2008, p. 316), meaning that the application of network metaphors are particularly useful in developing a deeper understanding of complex governance networks parts and whole. This is an important consideration as we contemplate building our capacities to model complex governance networks (see Chapter 11).
The body of literature that has applied complex adaptive systems to the study of social phenomena is long and growing (see for instance Axelrod and Cohen, 1999; Marion, 1999; Haynes,2003; Miller and Page, 2007) and there are a number of key complexity concepts that are relevant to our discussion of governance networks. These concepts include the role of feedback discussed earlier in this chapter, as well as the principle of holism introduced on chapter two (Degenne and Forse, 1999). Feedback loops operating within complex adaptive systems give shape to the interactions between agents (which is different from the interactions between variables as found in traditional stock and flow systems analysis (Newell and Meek, 2005)). Those wishing to study complex adaptive systems will be careful to avoid reductionism. Marion observes that, “Reductionism does not work with complex systems, and it is now clear that a purely reductionist approach cannot be applied when studying life; in living systems the whole is more than the sum of its parts. (Marion, 1999, p. 27-28). For the study of governance networks, the implication of the complex systems approach is that both reductionist and holistic approaches can yield improved understanding of governance operations and outcomes. Both approaches can be embraced but not at the expense of the other. In addition to feedback and holism, we may describe governance networks as complex adaptive systems by noting the latent capacity for self-organization and emergence, and the potential for designing robust and resilient governance systems.
Donnella Meadow defines self-organization as the “ capacity of a system to make its own structure more complex” (2008, p. 79). Complex adaptive systems scientists understand that “... just a few simple organizing principles can lead to wildly diverse self-organizing structures” (Meadows, 2008, p. 80). These self-organizing capacities are a characteristic of the non-linearity of their dynamics. Unlike the linear cause and effect models of standard systems analysis (as well as many of our statistical modeling methods), “... nonlinearity means a disproportionate relationship between variables [and agents]: a small change in one may trigger a large, disproportionate change in the other.” (Morcol and Wachhaus, 2009, p. 49). Self-organization is understood as the emergence of new structures and functions. Miller and Page suggest that “. emergence is a phenomenon whereby well-formulated aggregate behavior arises from localized, individual behavior. More over, such aggregate patterns should be immune to reasonable variations in the individual behavior” (2007, p. 46). Thus, the emergence of new patterns of organization and behavior begin “from the bottom up” at the micro level, or in the case of governance networks, interpersonal level. This is why we are quick to privilege the roles of individual network administrators (Chapter 8), “accounters and accountees” (Chapter 9), and communities of practice designed to learn from performance data (Chapter 10). Our discussion of governance network administration is very much grounded in the view that a central role of individuals is to serve as the midwives of emergent properties through the use of certain skills and strategies, accountability relationships, and performance standards.
Self-organization is also characterized as the emergence of higher level order in otherwise chaotic systems (e.g. Holland 1995, Kaufman 2004). In conventional notions of systems, more chaos is equated with more disorder at all scales. In complexity theory, unanticipated orderliness and patterns of self-organization among the interacting elements in the system could emerge out of the chaotic behavior of individual elements (nodes) in the governance systems.
It is important to note that within social systems like governance networks, emergence “occurs when learning processes exist (Holland, 1995)” (Mischen and Jackson, 2008, p. 316). The relationship between emergent forms of self-organization and learning become critically important when we consider the role that performance management systems play within governance networks. In Chapter 10 following Moynihan (2008), we argue that effective performance management systems are intentionally designed to operate within the context of network learning processes. In essence, effective performance management systems will be designed with a view to “harness complexity.” Axelrod and Cohen, in their classic book Harnessing Complexity: Organizational Implications of the Science Frontier, describe this process as “. deliberately changing the structure of a system in order to increase some measure of performance, and to do so by exploiting an understanding that the system itself is complex. Putting it more simply, the idea is to use our knowledge of complexity to do better. To harness complexity typically means living with it, and even taking advantage of it, rather than trying to ignore or eliminate it” (Axelrod and Cohen, 1999, p. 9).
The picture of governance networks as complex adaptive systems is a model of constant dynamism, with some components of the network (what we may construe as its subsystem) are embarked on a processes of emergence and adaptation, with other components of the network remaining relatively stable and perhaps even actively resisting emergent functions and structures. Meadows reminds us that “Complex systems can evolve from simple systems only if there are stable intermediate forms” (2008, p. 83). These stable intermediate forms most likely exist at the meso levels of established organizations and institutions, and long standing, institutionalized communities of practice.
The stability of some network actors can influence the stability of the network as a whole, or some portion of the governance network’s subsystem. Viewed through the lens of complex adaptive systems, these stable actors have more “fitness” than other network actors. Marion observes that “... fitness accrues to those who are best able to garner resources and that ability goes to organizations that can create mutually supportive networks with other systems; it does not accrue to those whose sole goal is to serve self at the expense of others. The motivation to elaborate, then, could be as simple as survival, and cooperation is the best tool for achieving it” (1999, p. 55). We may understand fitness of actors in terms of the need for interdependence. It is this structure of stability that allows for governance networks to maintain a certain measure of resilience.
Meadows observes that the resilience of complex adaptive systems “ is something that may be very hard to see, unless you exceed the limits, overwhelm and damage the balancing loops, and the system structure breaks down” (2008, p. 77). Ascertaining the resilience of what appears to be a stable governance network becomes a critical feature in managing uncertainty and anticipating risk (Koppenjan and Klijn, 2004). This is why feedback becomes such an important dimension of network governance. “Large organizations of all kinds, from corporations to governments, lose their resilience simply because the feedback mechanisms by which they sense and respond to their environment have to travel through too many layers of delay and distortion” (Meadows, 2008, p. 78), and the same may be said for governance networks on the whole.
The resilience of complex governance networks becomes important for two reasons: when governance networks fail to be resilient (as in the recent cases of failed emergency management networks); and when governance networks fail to adapt to changing conditions (as in the recent cases of financial regulation networks ). Sorensen and Torfing observe that “. the learning-based adaptiveness of governance networks might be impeded by the lack of capacities for experimentation, the conservative identities of actors who want to preserve the status quo, and the failure to resolve the internal conflicts between the actors that struggle over the assessment of experiments and the formulation of strategies for institutional reform” (2008, p. 105).
To view governance networks through the lens of complexity theory offers very important theoretical and practical potential. Addressing governance networks as pattern of relationships that evolve in ways that are unknown and uncertain gives rise to the notion of adaptive qualities of human relationships in governance. This governance network adaptation, as we noted early in this work, can work in positive and negative ways and gives leads to serious questions about governance network performance and democratic anchorage. As governance networks evolve, so will the need for an evolutionary pattern of democratic anchorage. On the pragmatic side, emergent and learning governance networks are exciting enterprises but they will also call upon new kinds of leadership and managerial practices to be viewed as both productive and accountable.
CRITICAL QUESTIONS AND CONSIDERATIONS
Based on the material presented in this chapter, we can assess governance networks from a systems perspective. Return once again to your governance network and consider the extent to which is it shaped by systems dynamics.
? What are the boundaries of the network? Are they open or closed? What distinguishes one component of the network from the other?
To what extent is the network bifurcated into subsystems?
- ? Has your governance network been studied using a systems model (e.g., in terms of a system of inputs, processes, outputs, and outcomes)? If not, what would such an analysis look like? Who is contributing resources to the network? What processes does the network undertake? What are the outputs and outcomes of the network?
- ? Examine the extent to which the governance network is governed by feedback loops. What policy tools, elected representatives, citizens, and interest groups influence the network’s behavior? The behaviors of specific actors in the network?