By Rense Corten
Computational ways to learning the Co-evolution of Networks and behavior in Social Dilemmas indicates scholars, researchers, and pros find out how to use computation tools, instead of mathematical research, to respond to learn questions for a neater, extra efficient approach to trying out their types. Illustrations of normal method are supplied and discover how laptop simulation is used to bridge the distance among formal theoretical versions and empirical applications.
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Extra resources for Computational Approaches to Studying the Co-evolution of Networks and Behavior in Social Dilemmas
In many different contexts, people are influenced by those with whom they interact (Erickson 1988; Marsden and Friedkin 1993; Merton 1968). Empirical examples of such processes include peer pressure among adolescents (Davies and Kandel 1981), diffusion of innovations (Coleman et al. 1957; ∗ This chapter is based on an article written in collaboration with Vincent Buskens and Jeroen Weesie. The original version appeared in the Journal of Peace Research (Buskens, Corten and Weesie 2008). Computational Approaches to Studying the Co-evolution of Networks and Behavior in Social Dilemmas, First Edition.
Otherwise, the actor does not change behavior. Clearly, other rules for changing behavior and relations can be conceived. We have chosen some of the most straightforward options on who might change what and when. However, further investigation of how the dynamics depend on these options is only called for if outcomes differ dramatically between them. 5 For each of the 13, 597 + 95, 729 = 109, 326 networks and each of the three versions of the dynamics, we varied the initial conditions in the following way.
Which behavior is chosen depends on the tie costs. In their analysis, conflict situations are excluded as possible long-term outcomes because they are less stable than nonconflict situations. In contrast with their study, we do not include “trembles” but analyze how the likelihood of emerging structures in a deterministic dynamic environment depends on initial conditions. In a deterministic environment networks with polarized behavior can be stable. This allows us to address the likelihood of conflict.