AIMAMay 8, 2025

Beyond the Tragedy of the Commons: Building A Reputation System for Generative Multi-agent Systems

arXiv:2505.05029v24 citationsh-index: 7
Originality Incremental advance
AI Analysis

It addresses a pervasive challenge in generative multi-agent systems, offering a solution to prevent collective disasters from individual self-interest.

This paper tackles the problem of the tragedy of the commons in generative multi-agent systems by proposing RepuNet, a dynamic reputation framework, which effectively mitigates this issue and promotes cooperation, as demonstrated in two scenarios.

The tragedy of the commons, where individual self-interest leads to collectively disastrous outcomes, is a pervasive challenge in human society. Recent studies have demonstrated that similar phenomena can arise in generative multi-agent systems (MASs). To address this challenge, this paper explores the use of reputation systems as a remedy. We propose RepuNet, a dynamic, dual-level reputation framework that models both agent-level reputation dynamics and system-level network evolution. Specifically, driven by direct interactions and indirect gossip, agents form reputations for both themselves and their peers, and decide whether to connect or disconnect other agents for future interactions. Through two distinct scenarios, we show that RepuNet effectively mitigates the 'tragedy of the commons', promoting and sustaining cooperation in generative MASs. Moreover, we find that reputation systems can give rise to rich emergent behaviors in generative MASs, such as the formation of cooperative clusters, the social isolation of exploitative agents, and the preference for sharing positive gossip rather than negative ones.

Foundations

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