序贯朴素学习
Sequential Naive Learning
摘要：
我们分析了有界理性更新的二元行动和二元状态的模型中的聚合统计。每个代理在观察到先前的无序动作集后，按顺序采取不可逆的动作。每个智能体首先基于总统计量形成她的先验，然后基于贝叶斯规则将她的信号与先验结合起来，最后应用一个决策规则，为每个信念分配一个（混合）动作。如果先验是根据离散化的DeGroot规则形成的，则动作收敛到状态（概率上），即，\emph{asymptotic learning}，当且仅当决策规则满足概率匹配。这一结果推广到未指定的信息设置，其中信息结构不同的代理和代理只知道生成自己的信号的信息结构。此外，主要结果扩展到$n$状态和$n$动作的情况。
英文摘要：
We analyze boundedly rational updating from aggregate statistics in a model with binary actions and binary states. Agents each take an irreversible action in sequence after observing the unordered set of previous actions. Each agent first forms her prior based on the aggregate statistic, then incorporates her signal with the prior based on Bayes rule, and finally applies a decision rule that assigns a (mixed) action to each belief. If priors are formed according to a discretized DeGroot rule, then actions converge to the state (in probability), i.e., \emph{asymptotic learning}, in any informative information structure if and only if the decision rule satisfies probability matching. This result generalizes to unspecified information settings where information structures differ across agents and agents know only the information structure generating their own signal. Also, the main result extends to the case of $n$ states and $n$ actions.
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。
