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Agent-based models meet network analysis: the policy-making perspective

Such models can be usefully employed at two different levels: to help in deciding (policy-maker level) and to empower the capabilities of people in evaluating the effectiveness of policies (citizen level).

Being easier to have network data (i.e. social network data) than detailed behavioral individual information, we can try to understand the links between the dynamic changes of the networks emerging from agent-based models and the behavior of the agents. As we understand these links, we can apply them to actual networks, to guess about the content of the behavioral black boxes of real-world agents.


Agent-based models. One great challenge in understanding organizational responses to diffusion is to anticipate the organizational changes that will emerge as a result of the combined processes of influence and selection. To explore the combined effects one can use agent-based models which simulate processes based on rules for behavior and interactions among actors

  • (e.g., Brown et al. 2005; Lim et al. 2002; Parker et al. 2003; Maroulis et al. 2010; Wilensky 1999, 2001).

For example, one can use agent-based models to examine the ultimate distribution of teaching practices after diffusion through a network (e.g., Frank & Fahrbach 1999).

Graphical representations of such processes can also be found in animated movies of network processes (Moody, McFarland & Bender-DeMoll 2005).

As such, agent based models hold great promise for educational research to explore the systemic implications of non-linear processes.

Дополнение: Two-mode social networks. Another new trend in social network analysis is the analysis of two-mode network data, or bipartite graphs - биграф

  • (see the May 2013 special issue of Social Networks, 35(2), pp.145–278) - http://www.sciencedirect.com/science/journal/03788733/35/2
    • Malinick T.E., Tindall D.B., Diani M. Network centrality and social movement media coverage: A two-mode network analytic approach // Social Networks. 2013. Т. 35. № 2. С. 148–158.


For example, Frank, Muller, et al. (2008) represented high school transcript data in terms of clusters of students and the courses they took. In this figure each dot represents a student and each square represents a course, with lines indicating courses taken by students.

The boundaries were identified by Field et al.’s (2006) adaptation of Frank’s algorithm, which maximizes the concentration of event participation within ovals relative to between the ovals.


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