As multi-agent AI systems become increasingly autonomous, evidence shows they can develop collusive strategies similar to those long observed in human markets and institutions. While human domains have accumulated centuries of anti-collusion mechanisms, it remains unclear how these can be adapted to AI settings. This paper addresses that gap by (i) developing a taxonomy of human anti-collusion mechanisms, including sanctions, leniency & whistleblowing, monitoring & auditing, market design, and governance and (ii) mapping them to potential interventions for multi-agent AI systems. For each mechanism, we propose implementation approaches. We also highlight open challenges, such as the attribution problem (difficulty attributing emergent coordination to specific agents), identity fluidity (agents being easily forked or modified), the boundary problem (distinguishing beneficial cooperation from harmful collusion), and adversarial adaptation (agents learning to evade detection).

翻译：随着多智能体AI系统日益自主化，证据表明它们可能发展出类似人类市场和机构中长期观察到的共谋策略。尽管人类领域积累了数百年的反共谋机制，但如何将这些机制适用于AI环境仍不明确。本文通过以下方式填补这一空白：（i）构建人类反共谋机制的分类体系，包括制裁、宽大与举报制度、监控与审计、市场设计及治理机制；（ii）将这些机制映射到多智能体AI系统的潜在干预措施中。针对每种机制，我们提出实施路径。同时，本文强调当前存在的开放性挑战，例如归因问题（难以将涌现的协调行为归因于特定智能体）、身份流动性（智能体容易被分支或修改）、边界问题（区分有益合作与有害共谋）以及对抗性适应（智能体学会规避检测）。