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Article

Promoting the Emergence of Behavior Norms in a Principal–Agent Problem—An Agent-Based Modeling Approach Using Reinforcement Learning

1
Laboratory of Environmental Geosimulation (LEDGE), Department of Geography, Université de Montréal, 1375 Ave. Thérèse-Lavoie-Roux, Montreal, QC H2V 0B3, Canada
2
Centre de Recerca Ecològica i Aplicacions Forestals (CREAF), Bellaterra, E-08193 Cerdanyola de Vallès, Catalonia, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Paola Pellegrini
Appl. Sci. 2021, 11(18), 8368; https://doi.org/10.3390/app11188368
Received: 23 July 2021 / Revised: 25 August 2021 / Accepted: 6 September 2021 / Published: 9 September 2021
(This article belongs to the Special Issue Multi-Agent Systems 2021)
One of the complexities of social systems is the emergence of behavior norms that are costly for individuals. Study of such complexities is of interest in diverse fields ranging from marketing to sustainability. In this study we built a conceptual Agent-Based Model to simulate interactions between a group of agents and a governing agent, where the governing agent encourages other agents to perform, in exchange for recognition, an action that is beneficial for the governing agent but costly for the individual agents. We equipped the governing agent with six Temporal Difference Reinforcement Learning algorithms to find sequences of decisions that successfully encourage the group of agents to perform the desired action. Our results show that if the individual agents’ perceived cost of the action is low, then the desired action can become a trend in the society without the use of learning algorithms by the governing agent. If the perceived cost to individual agents is high, then the desired output may become rare in the space of all possible outcomes but can be found by appropriate algorithms. We found that Double Learning algorithms perform better than other algorithms we used. Through comparison with a baseline, we showed that our algorithms made a substantial difference in the rewards that can be obtained in the simulations. View Full-Text
Keywords: complex systems; emergence; reinforcement learning; temporal difference learning; social status complex systems; emergence; reinforcement learning; temporal difference learning; social status
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MDPI and ACS Style

Harati, S.; Perez, L.; Molowny-Horas, R. Promoting the Emergence of Behavior Norms in a Principal–Agent Problem—An Agent-Based Modeling Approach Using Reinforcement Learning. Appl. Sci. 2021, 11, 8368. https://doi.org/10.3390/app11188368

AMA Style

Harati S, Perez L, Molowny-Horas R. Promoting the Emergence of Behavior Norms in a Principal–Agent Problem—An Agent-Based Modeling Approach Using Reinforcement Learning. Applied Sciences. 2021; 11(18):8368. https://doi.org/10.3390/app11188368

Chicago/Turabian Style

Harati, Saeed, Liliana Perez, and Roberto Molowny-Horas. 2021. "Promoting the Emergence of Behavior Norms in a Principal–Agent Problem—An Agent-Based Modeling Approach Using Reinforcement Learning" Applied Sciences 11, no. 18: 8368. https://doi.org/10.3390/app11188368

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