Emergence of cooperation: A reputation-modulated reinforcement learning

2026-08-20

AI Summary

研究通过引入基于声誉调整的强化学习方法，在空间囚徒困境游戏中促进合作行为的出现，展示了声誉不仅提供直接激励还重塑了社会信息环境。

Abstract

Reputation is widely recognized as a key mechanism for sustaining cooperation. However, most existing game-theoretic models treat reputation primarily as an external factor that modulates payoffs, interaction structures, or strategy update rules. In many social contexts, though, reputation operates primarily as information -- it shapes how individuals interpret their own experiences and assess the behavior of others. To bridge this gap, we propose a spatial prisoner's dilemma game grounded in the reinforcement learning paradigm, in which agents equipped with Q-learning integrate both individual and social information via a locally defined reputation metric to guide their decisions. Our results reveal that reputation-modulated learning significantly promotes the emergence of cooperative behavior, and we observe a discontinuous phase transition from full cooperation to full defection as the temptation increases. Cooperation spreads through the nucleation of cooperative clusters, whereas the disintegration of these clusters drives the system into an absorbing state of complete defection. Overall, this study demonstrates that reputation facilitates cooperation not only by providing direct incentives but also by reshaping the social information landscape that agents rely on for learning and adaptation.

Problem

• reputation
• cooperation
• reinforcement learning
• social information

Innovation

• reputation-modulated reinforcement learning
• spatial prisoner's dilemma game
• Q-learning
• social information landscape