Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning
Atsushi Ueshima, Shayegan Omidshafiei, Hirokazu Shirado

TL;DR
This study uses multi-agent reinforcement learning to explore how strategic network rewiring influences cooperation and ostracism dynamics in iterated Prisoner's Dilemma games, revealing that learning about ostracism and cooperation fosters mutual cooperation.
Contribution
It introduces a novel multi-policy RL framework to disentangle the causal relationship between network rewiring, ostracism, and cooperation in multi-agent systems.
Findings
Network rewiring promotes mutual cooperation even with free-riding agents.
Ostracism emerges from learned cooperation and reinforces it.
Learning about ostracism is crucial for cooperation emergence.
Abstract
Cooperation is challenging in biological systems, human societies, and multi-agent systems in general. While a group can benefit when everyone cooperates, it is tempting for each agent to act selfishly instead. Prior human studies show that people can overcome such social dilemmas while choosing interaction partners, i.e., strategic network rewiring. However, little is known about how agents, including humans, can learn about cooperation from strategic rewiring and vice versa. Here, we perform multi-agent reinforcement learning simulations in which two agents play the Prisoner's Dilemma game iteratively. Each agent has two policies: one controls whether to cooperate or defect; the other controls whether to rewire connections with another agent. This setting enables us to disentangle complex causal dynamics between cooperation and network rewiring. We find that network rewiring…
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Taxonomy
TopicsEvolutionary Game Theory and Cooperation · Death Anxiety and Social Exclusion · Evolutionary Psychology and Human Behavior
