# Segregation Dynamics with Reinforcement Learning and Agent Based Modeling

arXiv: https://arxiv.org/abs/1909.08711 · HTML: https://mlsanity.com/paper/1909.08711/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 9 of 9)

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- [Crafting Desirable Climate Trajectories with RL Explored Socio-Environmental Simulations](https://mlsanity.com/paper/2410.07287/) (2024)
- [Synthetic Social Media Influence Experimentation via an Agentic Reinforcement Learning Large Language Model Bot](https://mlsanity.com/paper/2411.19635/) (2024)
- [Learning Complex Spatial Behaviours in ABM: An Experimental Observational Study](https://mlsanity.com/paper/2201.01099/) (2022)
- [Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning](https://mlsanity.com/paper/2204.14076/) (2022)
- [The impact of moving expenses on social segregation: a simulation with RL and ABM](https://mlsanity.com/paper/2211.12475/) (2022)
- [An Algorithm For Adversary Aware Decentralized Networked MARL](https://mlsanity.com/paper/2305.05573/) (2023)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)

## Abstract (arXiv)

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.