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This study used Reinforcement Learning (RL) and Agent Based Modeling (ABM) to explore social segregation. Findings show interdependencies can foster integration, with younger people preferring diverse areas and older people segregated ones.

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Area of Science:

  • Computational Social Science
  • Artificial Intelligence
  • Sociology

Background:

  • Social systems dynamics arise from individual actions and preferences.
  • Rewards significantly influence decision-making and emergent social patterns.
  • The Schelling Segregation model demonstrates how individual preferences can lead to segregation.

Purpose of the Study:

  • To investigate self-organizing social segregation dynamics using computational modeling.
  • To explore how different reward structures impact segregation and integration.
  • To combine Reinforcement Learning (RL) and Agent Based Modeling (ABM) for social simulation.

Main Methods:

  • Developed an Agent Based Model (ABM) incorporating Deep Q-Networks for agent decision-making.
  • Agents were programmed with rules inspired by the Schelling Segregation model.
  • Introduced rewards for interactions to influence agent behavior and explore interdependencies.

Main Results:

  • Spatial integration is achievable even with segregation rewards, by fostering interdependencies between agent types.
  • Segregated areas were found to be more likely inhabited by older individuals.
  • Diverse areas attracted younger individuals, indicating age-based spatial preferences.

Conclusions:

  • Combining RL and ABM provides a powerful tool for simulating and understanding social dynamics.
  • Policy makers can utilize such models to observe behaviors related to interaction rules and reward systems.
  • Computational models can reveal nuanced outcomes of social interactions, such as age-related spatial distribution.