Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game

2025/06/06 by Edward Hughes, Hughes, Edward, Tina Zhu +15 · 3 citations
Decision Sciences · Social Sciences · #Evolutionary Game Theory and Cooperation #Experimental Behavioral Economics Studies #FOS: Computer and information sciences #Game Theory and Applications #Multiagent Systems (cs.MA)

paper · pdf · doi:10.48550/arxiv.2506.06032

openalex publication_date 2025/06/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Multi-agent reinforcement learning algorithms are useful for simulating social behavior in settings that are too complex for other theoretical approaches like game theory. However, they have not yet been empirically supported by laboratory experiments with real human participants. In this work we demonstrate how multi-agent reinforcement learning can model group behavior in a spatially and temporally complex public good provision game called Clean Up. We show that human groups succeed in Clean Up when they can see who is who and track reputations over time but fail under conditions of anonymity. A new multi-agent reinforcement learning model of reputation-based cooperation demonstrates the same difference between identifiable and anonymous conditions. Furthermore, both human groups and artificial agent groups solve the problem via turn-taking despite other options being available. Our results highlight the benefits of using multi-agent reinforcement learning to model human social behavior in complex environments.

Citations

• Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL
• Learning to Participate through Trading of Reward Shares
• The Importance of Credo in Multiagent Learning
• Deep Multiagent Reinforcement Learning: Challenges and Directions
• A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings
• A multi-agent reinforcement learning model of reputation and cooperation in human groups
• Open Problems in Cooperative AI
• Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences
• D3C: Reducing the Price of Anarchy in Multi-Agent Learning
• Learning to Incentivize Other Learning Agents
• Should I tear down this wall? Optimizing social metrics by evaluating\n novel actions
• Social diversity and social preferences in mixed-motive reinforcement\n learning
• Emergent Tool Use From Multi-Agent Autocurricula
• Learning Reciprocity in Complex Sequential Social Dilemmas
• Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research
• Evolving intrinsic motivations for altruistic behavior
• Inequity aversion improves cooperation in intertemporal social dilemmas
• IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
• Learning with Opponent-Learning Awareness
• A multi-agent reinforcement learning model of common-pool resource appropriation
• Maintaining cooperation in complex social dilemmas using deep reinforcement learning
• Multi-agent Reinforcement Learning in Sequential Social Dilemmas
• Asynchronous Methods for Deep Reinforcement Learning
• Long Short-Term Memory
• Vegetation of the Siskiyou Mountains, Oregon and California
• Agents Incorporating Identity and Dynamic Teams in Social Dilemmas
• Reinforcement Learning: An Introduction

Cited by

• Evaluating Cooperative Resilience in Multiagent Systems: A Comparison Between Humans and LLMs
• A Theory of Appropriateness That Accounts for Norms of Rationality
• Virtual Agent Economies

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