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Survey of evolutionary computation methods in social agent-based modeling studies

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Abstract

Agent-based modeling is a well-established discipline today with a rich and vibrant research community. The field of evolutionary computation (EC) is also well recognized within the larger family of computational sciences. In the past decades many agent-based modeling studies of social systems have used EC methods to tackle various research questions. Despite the relative frequency of such efforts, no systematic review of the use of evolutionary computation in agent-based modeling has been put forth. Here, we review a number of prominent agent-based models of social systems that employ evolutionary algorithms as a method. We comment on some theoretical considerations, the state of current practice, and suggest some best practices for future work.

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Acknowledgements

This study was funded in part by the Center for Social Complexity at GMU, by the US National Science Foundation, CDI Program, Grant no. IIS-1125171, and by ONR-Minerva Grant no. N00014130054.

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  1. Center for Social Complexity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA

    Peter Revay & Claudio Cioffi-Revilla

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Correspondence to Peter Revay.

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Revay, P., Cioffi-Revilla, C. Survey of evolutionary computation methods in social agent-based modeling studies. J Comput Soc Sc 1, 115–146 (2018). https://doi.org/10.1007/s42001-017-0003-8

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