寻找失落的相关性：通过边际行动实现相关均衡
In Search of Lost Correlation: Correlated Equilibrium via Marginal Actions
AI总结：
本文通过边际行动分析相关均衡的诱导数据，提出无套利条件来识别可能的均衡分布，并扩展到纳什均衡的分析。
摘要：
本文研究在已知有限同时行动博弈的情况下，哪些数据可以由相关均衡诱导。我们假设分析人员可以获取每个参与者行动的频率，但无法获取联合行动配置的分布。我们通过一种无套利条件来刻画哪些边际行动分布集源于某些相关均衡。外部观察者无法通过独立与每个参与者签约并获取通过单方面偏离获得的总效用部分来获得预期利润。这种刻画自然扩展到纳什均衡。
英文摘要：
In this paper, we study which data can be induced by a correlated equilibrium given a known finite simultaneous move game. We assume that an analyst has access to the frequency of each agent's actions but does not have access to the distribution over joint action profiles. We characterize which sets of marginal distributions over actions arise from some correlated equilibria via a type of no arbitrage condition. An outside observer is unable to make a profit in expectation by independently contracting with each agent and collecting a portion of the total utility gained via unilateral deviation. This characterization naturally extends to Nash equilibria.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
