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• W3043075313 abstract "Abstract 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." @default.
• W3043075313 created "2020-07-23" @default.
• W3043075313 creator A5034827139 @default.
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• W3043075313 date "2020-07-16" @default.
• W3043075313 modified "2023-10-18" @default.
• W3043075313 title "Segregation dynamics with reinforcement learning and agent based modeling" @default.
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• W3043075313 doi "https://doi.org/10.1038/s41598-020-68447-8" @default.
• W3043075313 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7367354" @default.
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