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Segregation dynamics with reinforcement learning and agent based modeling

Societies are complex. Properties of social systems can be explained by the interplay and weaving of individual actions. Rewards 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,...

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Autores principales: Sert, Egemen, Bar-Yam, Yaneer, Morales, Alfredo J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7367354/
https://www.ncbi.nlm.nih.gov/pubmed/32678127
http://dx.doi.org/10.1038/s41598-020-68447-8
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author Sert, Egemen
Bar-Yam, Yaneer
Morales, Alfredo J.
author_facet Sert, Egemen
Bar-Yam, Yaneer
Morales, Alfredo J.
author_sort Sert, Egemen
collection PubMed
description Societies are complex. Properties of social systems can be explained by the interplay and weaving of individual actions. Rewards 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 Modeling (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 rewards. Our model promotes the creation of interdependencies and interactions among multiple agents of two different kinds that segregate from each other. For this purpose, agents use Deep Q-Networks to make decisions inspired on the rules of the Schelling Segregation model and rewards for interactions. Despite the segregation reward, 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 ABM can create an artificial environment for policy makers to observe potential and existing behaviors associated to rules of interactions and rewards.
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spelling pubmed-73673542020-07-20 Segregation dynamics with reinforcement learning and agent based modeling Sert, Egemen Bar-Yam, Yaneer Morales, Alfredo J. Sci Rep Article Societies are complex. Properties of social systems can be explained by the interplay and weaving of individual actions. Rewards 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 Modeling (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 rewards. Our model promotes the creation of interdependencies and interactions among multiple agents of two different kinds that segregate from each other. For this purpose, agents use Deep Q-Networks to make decisions inspired on the rules of the Schelling Segregation model and rewards for interactions. Despite the segregation reward, 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 ABM can create an artificial environment for policy makers to observe potential and existing behaviors associated to rules of interactions and rewards. Nature Publishing Group UK 2020-07-16 /pmc/articles/PMC7367354/ /pubmed/32678127 http://dx.doi.org/10.1038/s41598-020-68447-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Sert, Egemen
Bar-Yam, Yaneer
Morales, Alfredo J.
Segregation dynamics with reinforcement learning and agent based modeling
title Segregation dynamics with reinforcement learning and agent based modeling
title_full Segregation dynamics with reinforcement learning and agent based modeling
title_fullStr Segregation dynamics with reinforcement learning and agent based modeling
title_full_unstemmed Segregation dynamics with reinforcement learning and agent based modeling
title_short Segregation dynamics with reinforcement learning and agent based modeling
title_sort segregation dynamics with reinforcement learning and agent based modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7367354/
https://www.ncbi.nlm.nih.gov/pubmed/32678127
http://dx.doi.org/10.1038/s41598-020-68447-8
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