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

TL;DR

This study shows that multi-agent reinforcement learning can effectively model human reputation-based cooperation in complex public good games, emphasizing the importance of identity visibility for success.

Contribution

The paper introduces a new multi-agent reinforcement learning model that captures human reputation-seeking behavior in complex spatial-temporal public good scenarios.

Findings

Humans succeed in the game when identities are visible, but fail under anonymity.

The RL model replicates the human success and failure patterns based on visibility.

Both humans and agents use turn-taking strategies to solve the game.

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.