Modeling Complex Governance Networks

Combining elements of case study analysis with the quantifiable elements of hypothesis testing, the tools of network analysis and computer-based modeling are employed in the study of governance networks. The most extensive applications of social network analysis to the study of governance networks within the public administration literature have been undertaken by Louise Comfort (2002, 2007) and Naim Kapucu (2006a, 2006b), who have applied the tools of social network analysis (and their related software applications) to the study of emergency management networks. These tools are particularly useful in studying the nature of the ties occurring between network actors. Social network analysis allows for the coding of ties based on strength, types of resource flows, and formality. The position of actors vis-a-vis their networks may be studied, providing a capacity to not only test the kind of hypotheses discussed above, but also re-create holistic systems models of existing governance networks. Dynamic models may be employed to anticipate the emergence of future structures and functions (Miller and Page, 2007).

Miller and Page describe modeling as an “attempt to reduce the world to a fundamental set of elements (equivalent classes) and laws (transition functions), and on this basis ... understand and predict key aspects of the world” (2007, p. 40). Social network analysis provides one set of elements that may be relied upon to build a model. “Modeling proceeds by deciding what simplifications to impose on the underlying entities and then, based on those abstractions, uncovering their implications” (Miller and Page, 2007, p. 65). Social network analysis simplifies the structures of networks into a series of nodes and ties.

Those who are viewing social networks in terms of complex adaptive systems have begun to ascribe agency to actors in the network into agent-based models (ABMs). Miller and Page describe the difference between ABMs and forms of complex systems dynamics models:

The agent-based object approach can be considered “bottom up” in the sense that the behavior that we observe in the model is generated from the bottom of the system by the direct interactions of the entities

Table 11.2 Computations as Theory

Simple Structure

Complicated Structure

Agent-Based Objects

Bottom-up modeling (e.g., artificial adaptive agents)

Bottom-up simulation (e.g., artificial life)



Top-down modeling (e.g., computable general equilibrium)

Top-down simulation (e.g., global warming)

Source: Miller and Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press, Princeton, NJ, 2007, p. 67. Permission granted by Princeton University Press.

that form the basis of the model. This contrasts with the “top-down” approach to modeling where we impose high-level rules on the system—for example, that the system will equilibrate or that all firms profit maximize—and then trace the implications of such conditions.

Thus, in top-down modeling we abstract broadly over the entire behavior of the system, whereas in bottom-up modeling we focus our abstractions over the lower-level individual entities that make up the system.

(2007, p. 66)

They relate the complexity of the structure to the bottom-up or top-down nature of the objects studied in Table 11.2.

Bottom-up models of governance networks will start with the characteristics of each actor in the network, including the roles that individuals, groups, and organizations play. In dynamic agent-based models, the behaviors of these agents are ascribed certain characteristics or some ranges of intensity around certain characteristics, with the system virtually “taking on a life of its own.” Top-down models deduce the essential properties to be modeled and construct nonlinear models to predict outcomes.

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