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Entropy profiles of Schelling's segregation model from the Wang-Landau algorithm
1Division of Liberal Arts and Sciences, Gwangju Institute of Science and Technology, Gwangju 61005, South Korea.
Chaos (Woodbury, N.Y.)
|December 1, 2022
Summary
Schelling
Area of Science:
- Computational Social Science
- Network Science
- Agent-Based Modeling
Background:
- Thomas Schelling's model demonstrates how individual preferences can lead to large-scale segregation.
- Understanding segregation dynamics is crucial for urban planning and social policy.
- Network structures significantly influence emergent social phenomena.
Purpose of the Study:
- To quantitatively evaluate Schelling's segregation model across different network topologies.
- To analyze the relationship between individual satisfaction and segregation levels.
- To explore the impact of network clustering on segregation outcomes.
Main Methods:
- Utilized the Wang-Landau algorithm for efficient exploration of the outcome space.
- Calculated entropy and the number of states as functions of satisfaction and segregation.
- Analyzed segregation ratios on square lattice, random, and clustered random networks.
Main Results:
- Satisfaction generally increases with segregation, regardless of network structure.
- Maximized satisfaction almost surely leads to segregation, confirmed algebraically.
- Clustering amplifies segregation in random networks, increasing like-neighbor ratios.
- Schelling's model shows sharper segregation than random configurations, indicating potential over-optimization.
Conclusions:
- Segregation is an emergent property strongly linked to maximizing individual satisfaction.
- Network clustering is a key factor exacerbating segregation.
- Modifying individual choice sets offers a potential strategy to mitigate segregation without sacrificing satisfaction.
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