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|Abstract:||Agent-based modeling (ABM) constitutes a powerful computational tool for the exploration of phenomena involving emergent dynamic behavior in the social sciences. This paper demonstrates a computer-assisted approach that bridges the significant gap between the single-agent microscopic level and the macroscopic (coarse-grained population) level, where fundamental questions must be rationally answered and policies guiding the emergent dynamics devised. Our approach will be illustrated through an agent-based model of civil violence. This spatiotemporally varying ABM incorporates interactions between a heterogeneous population of citizens [active (insurgent), inactive, or jailed] and a population of police officers. Detailed simulations exhibit an equilibrium punctuated by periods of social upheavals. We show how to effectively reduce the agent-based dynamics to a stochastic model with only two coarse-grained degrees of freedom: the number of jailed citizens and the number of active ones. The coarse-grained model captures the ABM dynamics while drastically reducing the computation time (by a factor of approximately 20). © 2012 American Physical Society.|
|Citation:||Zou, Y, Fonoberov, VA, Fonoberova, M, Mezic, I, Kevrekidis, YG. (2012). Model reduction for agent-based social simulation: Coarse-graining a civil violence model. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 85 (6), 10.1103/PhysRevE.85.066106|
|Pages:||066106-1 - 066106-13|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||Physical Review E - Statistical, Nonlinear, and Soft Matter Physics|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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