发表机构
Yale University（耶鲁大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对智能体从数据学习时的参数选择问题，提出奇异均衡概念，定义叙事并以其复杂度与稳健性表征，将Berk-Nash等均衡精炼，刻画了受行动影响数据时的长期稳定行动。
AI 中文摘要
智能体从数据中得知，其模型的诸多参数能同等良好地解释现象。Berk定理指出，智能体的信念会集中于最适配数据的参数，但未在这些参数间做出选择。我们证明，当参数为连续统时，后验分布会集中于具有最小局部学习系数的最优适配参数。我们提出叙事的定义：最优适配参数的子集，其中学习系数为常数。叙事由其所需巧合的数量（复杂度）及每个巧合必须成立的精确程度（稳健性）来表征。当智能体的行动影响其数据时，每个长期行动都是奇异均衡，即对支撑于复杂度最低且最稳健叙事上的信念的最优反应。奇异均衡通过限制过于复杂或极端的偏离路径信念，对Berk-Nash均衡和自我确认均衡进行了精炼，其严格统一版本刻画了一致稳定的行动。
英文摘要
An agent learns from data that many parameters of her model explain equally well. Berk's theorem states that her belief concentrates on the parameters that best fit the data, but does not select between them. We show that, with a continuum of parameters, the posterior concentrates on the best-fitting parameters with the smallest local learning coefficient. We propose a definition of narratives, subsets of the best-fitting parameters on which the learning coefficient is constant. Narratives are characterized by the number of coincidences they require (complexity) and how exactly each must hold (robustness). When the agent's actions affect her data, every long-run action is a singular equilibrium, a best reply to a belief supported on the least complex and most robust narratives. Singular equilibrium refines Berk-Nash and self-confirming equilibrium by restricting overly complex or knife-edge off-path beliefs, and its strict uniform version characterizes uniformly stable actions.
Comments68 pages. Feedback is welcome
