Segregation Dynamics with Reinforcement Learning and Agent Based Modeling

TL;DR

This paper integrates Reinforcement Learning with Agent-Based Models to simulate social segregation dynamics, revealing how incentives influence spatial integration and demographic patterns in complex societies.

Contribution

It introduces a novel approach combining Deep Q-Networks with ABMs to explore social segregation and interdependencies among agents with different incentives.

Findings

Spatial integration can emerge despite segregation incentives.

Older populations tend to occupy segregated areas, while younger ones prefer diverse regions.

RL-ABM framework offers a tool for policy simulation and analysis.

Abstract

Societies are complex. Properties of social systems can be explained by the interplay and weaving of individual actions. Incentives are key to understand people's choices and decisions. For instance, individual preferences of where to live may lead to the emergence of social segregation. In this paper, we combine Reinforcement Learning (RL) with Agent Based Models (ABM) in order to address the self-organizing dynamics of social segregation and explore the space of possibilities that emerge from considering different types of incentives. Our model promotes the creation of interdependencies and interactions among multiple agents of two different kinds that want to segregate from each other. For this purpose, agents use Deep Q-Networks to make decisions based on the rules of the Schelling Segregation model and the Predator-Prey model. Despite the segregation incentive, our experiments show that spatial integration can be achieved by establishing interdependencies among agents of different kinds. They also reveal that segregated areas are more probable to host older people than diverse areas, which attract younger ones. Through this work, we show that the combination of RL and ABMs can create an artificial environment for policy makers to observe potential and existing behaviors associated to incentives.