Dynamic Models of Mobilization in Political Networks
Proceedings of 2010 Political Networks Conference, Duke University
Posted: 3 Sep 2010
Date Written: August 31, 2010
The Eastern European color revolutions, and the recent post-election unrest in Iran pose a pressing question: how can local organization networks facilitate large-scale collective action? The final result of a collective action is contingent upon two factors, the relational structure of the network of the individuals involved, and their mutual learning, imitation, and belief-updating dictated by the network structure. I propose a formalization of the Granovetter threshold model for participation in collective action in networks, which takes both the network structure and belief updating into account. In order to make verifiable predictions, I outline a graph theoretical model for threshold updating using the DeGroot learning model. I demonstrate that full connectivity in a social network sometimes can hinder collective action. Later I will show that with some assumptions on the structure of the social network, repeated threshold updating takes the network to an equilibrium on the network graph; hence, the updating procedure acts as an equilibrium selection mechanism based on network parameters and initial participation thresholds. When these assumptions do not hold, cycles of participation and disengagement can occur. Furthermore, using this model one could find the network structure that brings about a particular asymptotic action equilibrium. Unlike the Granovetter/Kuran model, this model predicts non-monotone participation levels and heterogeneous outcomes at the final equilibrium, where some individuals act and some do not. Hence, it provides a more realistic model of mobilization dynamics, which can explain the ebb and flow in large-scale political demonstrations.
Keywords: cascade, collective action, diffusion, mobilization, network, structure, threshold
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