隐性投标方合谋：动态拍卖中的人工智能
Tacit Bidder-Side Collusion: Artificial Intelligence in Dynamic Auctions
- New York University（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文研究了大型语言模型在动态拍卖中通过隐性协调实现投标方合谋的可能性，并展示了市场结构对缓解合谋的影响。
AI中文摘要：
我们研究了大型语言模型作为自主投标者是否能在重复的荷兰拍卖中通过协调接受平台发布的付款时间而隐性合谋，而无需任何沟通。我们提出了一个最小的重复拍卖模型，该模型产生了一个简单的激励相容条件和一个可持续合谋的闭合形式阈值，适用于子博弈完美纳什均衡。在受控模拟中，使用多个语言模型时，我们观察到在小型拍卖设置中存在系统性的超竞争性价格，而当市场中的投标者数量增加时，又会回归竞争性行为，这与理论模型一致。我们还发现LLMs使用各种机制来促进隐性协调，例如焦点接受时间与耐心策略，这些策略跟踪理论激励。结果提供了我们所知的第一份证据，证明LLMs的投标方隐性合谋，并表明市场结构杠杆比能力限制更有效于缓解。
英文摘要：
We study whether large language models acting as autonomous bidders can tacitly collude by coordinating when to accept platform posted payouts in repeated Dutch auctions, without any communication. We present a minimal repeated auction model that yields a simple incentive compatibility condition and a closed form threshold for sustainable collusion for subgame-perfect Nash equilibria. In controlled simulations with multiple language models, we observe systematic supra-competitive prices in small auction settings and a return to competitive behavior as the number of bidders in the market increases, consistent with the theoretical model. We also find LLMs use various mechanisms to facilitate tacit coordination, such as focal point acceptance timing versus patient strategies that track the theoretical incentives. The results provide, to our knowledge, the first evidence of bidder side tacit collusion by LLMs and show that market structure levers can be more effective than capability limits for mitigation.
