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Evolutionary Computation and Agent-based Modeling: Biologically-inspired Approaches for Understanding Complex Social Systems

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dc.contributor.author Cioffi-Revilla, Claudio
dc.contributor.author De Jong, Kenneth
dc.contributor.author Bassett, Jeffrey
dc.date.accessioned 2015-03-27T19:20:56Z
dc.date.available 2015-03-27T19:20:56Z
dc.date.issued 2012-06-18
dc.identifier.citation Cioffi-Revilla, Claudio, Jong, Kenneth De, & Bassett, Jeffrey. 2012. Evolutionary Computation and Agent-based Modeling: Biologically-inspired Approaches for Understanding Complex Social Systems. Computational and Mathematical Organizational Theory, Vol. 18, no. 3, pp. 356–373. DOI 10.1007/s10588-012-9129-7. en_US
dc.identifier.other DOI 10.1007/s10588-012-9129-7
dc.identifier.uri https://hdl.handle.net/1920/9238
dc.description The authors are solely responsible for any errors in this study. Special thanks to Mark Rouleau for initial development of the MASON RebeLand model with the first author, to Sean Luke for support with the MASON system, and to members of the Mason-HRAF Joint Project on Eastern Africa for comments and discussions. en_US
dc.description.abstract Computational social science in general, and social agent-based modeling (ABM) simulation in particular, are challenged by modeling and analyzing complex adaptive social systems with emergent properties that are hard to understand in terms of components, even when the organization of component agents is know. Evolutionary computation (EC) is a mature field that provides a bio-inspired approach and a suite of techniques that are applicable to and provide new insights on complex adaptive social systems. This paper demonstrates a combined EC-ABM approach illustrated through the RebeLand model of a simple but complete polity system. Results highlight tax rates and frequency of public issue that stress society as significant features in phase transitions between stable and unstable governance regimes. These initial results sug- gest further applications of EC to ABM in terms of multi-population models with heterogeneous agents, multi-objective optimization, dynamic environments, and evolving executable objects for modeling social change.
dc.description.sponsorship Funding for this study was provided by the Center for Social Complexity at George Mason University and by grant no. N00014-08-1-0378 from the Office of Naval Research. en_US
dc.language.iso en_US en_US
dc.publisher Kluwer en_US
dc.rights Copyright © 2012, Springer Science+Business Media, LLC en_US
dc.subject Agent-based model en_US
dc.subject Evolutionary computation en_US
dc.subject Social sciences--Simulation methods en_US
dc.title Evolutionary Computation and Agent-based Modeling: Biologically-inspired Approaches for Understanding Complex Social Systems en_US
dc.type Article en_US


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