大模型在拍卖中无需沟通就能默契抬价，揭示了AI协同风险。
Tacit Bidder-Side Collusion: Artificial Intelligence in Dynamic Auctions
- 用简化模型推导出维持合谋的阈值条件
- 小规模拍卖中出现明显高于竞争水平的价格
- 适合关注AI市场风险与监管的研究者
我们研究大型语言模型作为自主竞标者，在重复荷兰式拍卖中能否通过协调接受平台报价的时间点实现隐性合谋，且不进行任何通信。提出一个最小化的重复拍卖模型，得出简单的激励相容条件及子博弈完美纳什均衡下的可持续合谋闭式阈值。在多个语言模型的受控模拟中，观察到小规模拍卖环境中系统性出现高于竞争水平的价格，而当竞标者数量增加时行为回归竞争状态，符合理论预测。还发现语言模型采用多种机制实现隐性协调，如聚焦点接受时机与追踪理论激励的耐心策略。据我们所知，这是首个关于语言模型在竞标方实现隐性合谋的实证证据，并表明市场结构调控比能力限制更有效于缓解风险。
原文摘要 · Abstract (English)
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.
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