Agent-based evolutionary game dynamics uncover the dual role of resource heterogeneity in the evolution of cooperation

• Published in: Journal of theoretical biology Vol. 595; p. 111952
• Main Authors: Yang, Qin, Tang, Yi, Gao, Dehua
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 07.12.2024
• Subjects: Agent-based model; Environmental feedback; Game theory; Multi-agent reinforcement learning; Selection pressure; Multi-agent reinforcement learning; Selection pressure; Game theory; Environmental feedback; Agent-based model
• ISSN: 0022-5193; 1095-8541; 1095-8541
• DOI: 10.1016/j.jtbi.2024.111952

Cooperation is a cornerstone of social harmony and group success. Environmental feedbacks that provide information about resource availability play a crucial role in encouraging cooperation. Previous work indicates that the impact of resource heterogeneity on cooperation depends on the incentive to act in self-interest presented by a situation, demonstrating its potential to both hinder and facilitate cooperation. However, little is known about the underlying evolutionary drivers behind this phenomenon. Leveraging agent-based modeling and game theory, we explore how differences in resource availability across environments influence the evolution of cooperation. Our results show that resource variation hinders cooperation when resources are slowly replenished but supports cooperation when resources are more readily available. Furthermore, simulations in different scenarios suggest that discerning the rate of natural selection acts on strategies under distinct evolutionary dynamics is instrumental in elucidating the intricate nexus between resource variability and cooperation. When evolutionary forces are strong, resource heterogeneity tends to work against cooperation, yet relaxed selection conditions enable it to facilitate cooperation. Inspired by these findings, we also propose a potential application in improving the performance of artificial intelligence systems through policy optimization in multi-agent reinforcement learning. These explorations promise a novel perspective in understanding the evolution of social organisms and the impact of different interactions on the function of natural systems.