Generating Mixed Patterns of Residential Segregation: An Evolutionary Approach

Chathika Gunaratne (), Erez Hatna (), Joshua M. Epstein () and Ivan Garibay
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Chathika Gunaratne: https://www.ornl.gov/staff-profile/chathika-s-gunaratne
Erez Hatna: https://publichealth.nyu.edu/faculty/erez-hatna
Joshua M. Epstein: https://publichealth.nyu.edu/faculty/joshua-epstein

Journal of Artificial Societies and Social Simulation, 2023, vol. 26, issue 2, 7

Abstract: The Schelling model of residential segregation has demonstrated that even the slightest preference for neighbors of the same race can be amplified into community-wide segregation. However, these models are unable to simulate mixed, coexisting patterns of segregation and integration, which have been seen to exist in cities. Using evolutionary model discovery we demonstrate how including social factors beyond racial bias when modeling relocation behavior enables the emergence of strongly mixed patterns. Our results indicate that the emergence of mixed patterns is better explained by multiple factors influencing the decision to relocate; the most important being the interaction of nonlinear, rapidly diminishing racial bias with a recent, historical tendency to move. Additionally, preference for less isolated neighborhoods or preference for neighborhoods with longer residing neighbors may produce weaker mixed patterns. This work highlights the importance of exploring the influence of multiple hypothesized factors of decision making, and their interactions, within agent rules, when studying emergent outcomes generated by agent-based models of complex social systems.

Keywords: Agent-Based Modeling; Residential Segregation; Genetic Programing; Simulation; Complex Systems; Machine Learning (search for similar items in EconPapers)
Date: 2023-03-31
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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2022-149-3

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