Cooperation And Reputation Dynamics With Reinforcement Learning

Abstract

Creating incentives for cooperation is a challenge in natural and artificial systems. One potential answer is reputation, whereby agents trade the immediate cost of cooperation for the future benefits of having a good reputation. Game theoretical models have shown that specific social norms can make cooperation stable, but how agents can independently learn to establish effective reputation mechanisms on their own is less understood. We use a simple model of reinforcement learning to show that reputation mechanisms generate two coordination problems: agents need to learn how to coordinate on the meaning of existing reputations and collectively agree on a social norm to assign reputations to others based on their behavior. These coordination problems exhibit multiple equilibria, some of which effectively establish cooperation. When we train agents with a standard Q-learning algorithm in an environment with the presence of reputation mechanisms, convergence to undesirable equilibria is w

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keyanastassacos2021cooperation

Related papers

• Bottom-up Reputation Promotes Cooperation With Multi-agent Reinforcement Learning (2025)0.00
• Intrinsic Fluctuations Of Reinforcement Learning Promote Cooperation (2022)9.23
• Reciprocal Reward Influence Encourages Cooperation From Self-interested Agents (2024)1.91
• Emergent Cooperation Under Uncertain Incentive Alignment (2024)2.26
• Modeling Human Reputation-seeking Behavior In A Spatio-temporally Complex Public Good Provision Game (2025)0.00
• Cooperation Dynamics In Multi-agent Systems: Exploring Game-theoretic Scenarios With Mean-field Equilibria (2023)0.00
• Improved Cooperation By Balancing Exploration And Exploitation In Intertemporal Social Dilemma Tasks (2021)0.00
• Learning Multiagent Coordination In The Absence Of Communication Channels (2018)0.00