Generating Mixed Patterns of Residential Segregation: An Evolutionary Approach

Chathika Gunaratne, Erez Hatna, Joshua M. Epstein, Ivan Garibay

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish
Article number7
JournalJASSS
Volume26
Issue number2
DOIs
StatePublished - Mar 31 2023

Funding

Notice: This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov /downloads/doe-public-access-plan).

FundersFunder number
DOE Public Access Plan
United States Government
U.S. Department of Energy

    Keywords

    • Agent-Based Model
    • Genetic Programming
    • Random Forests
    • Residential Satisfaction
    • Schelling
    • Segregation

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