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Agent‐based models in sociology

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This article looks at 20 years of applications of agent‐based models (ABMs) in sociology and, in particular, their explanatory achievements and methodological insights. These applications have helped sociologists to examine agent interaction in social outcomes and have helped shift analyses away from structural and aggregate factors, to the role of agency. They have improved the realism of the micro‐behavioral foundations of sociological models, by complementing analytic modeling and game theory–inspired analyses. Secondly, they have helped us to dissect the role of social structures in constraining individual behavior more precisely than in variable‐based sociology. Finally, simulation outcomes have given us a more dynamic view of the interplay between individual behavior and social structures, thus promoting a more evolutionary and process‐based approach to social facts. Attention here has been paid to social norms, social influence, and culture dynamics, across different disciplines such as behavioral sciences, complexity science, sociology, and economics. We argue that these applications can help sociology to achieve more rigorous research standards, by promoting a modeling environment and providing tighter cross‐disciplinary integration. Recently, certain methodological improvements toward model standardization, replication, and validation have been achieved. As a result, the impact of these models in sociology is expected to grow even more in the future. WIREs Comput Stat 2015, 7:284–306. doi: 10.1002/wics.1356 This article is categorized under: Statistical Models > Agent-Based Models Algorithms and Computational Methods > Computational Complexity Data: Types and Structure > Social Networks
Coexistence of behaviors and shirking rate in a typical simulation run (Reprinted with permission from Ref , p. 21 Copyright 2004 Elsevier Ltd).
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Residential segregation in the NetLogo Schelling's segregation model with household threshold preferences of similar neighbors at (a) 25%, (b) 33%, and (c) 50%.
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Convergence of the population on a shared convention for each habituation level and for different error probabilities (Reprinted with permission from Ref , p. 29 Copyright 2004 Elsevier Ltd). The higher the convergence value, the larger the diffusion of any given right/left convention.
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Networks after 30 rounds of a typical simulation run of the ‘Dynamic2couples’ (a) and the ‘Dynamic2k10couples’ (b) scenarios (Reprinted with permission from Ref , p. 488 Copyright 2012 Elsevier Ltd).
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Average number of links per agent in the ‘Dynamic2couples’ (initial random coupling and broken ties were replaced by only one of the two formerly linked agents) and ‘Dynamic2k10couples’ (the same but starting from a regular network of degree 10) scenarios (Reprinted with permission from Ref , p. 489 Copyright 2012 Elsevier Ltd).
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Statistical Models > Agent-Based Models
Data: Types and Structure > Social Networks
Algorithms and Computational Methods > Computational Complexity

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