Games of Incomplete Information Played by Statisticians
Games of Incomplete Information Played by Statisticians
复制标题
统计学家玩的不完全信息游戏
DOI：
10.2139/ssrn.2931873
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Annie Liang
中科院分区：
文献类型：
--
作者：
Annie Liang
本文提出了游戏中异质信念的基础，其中产生分歧不是因为玩家观察到不同的信息，而是因为他们以不同的方式从共同的信息中学习。玩家可能会被错误地指定，更有可能被错误地指定其他人如何学习。关键的假设是，玩家仍然对如何解释数据有一些共同的理解；形式上，玩家对一类学习规则的预测具有共同的确定性。常见的先验假设嵌套为该类为单例的特殊情况。主要结果描述了当代理观察到有限数量的数据时，可以预测哪些合理行为和纳什均衡，以及需要多少数据来预测不同的解决方案。观测值的数量取决于解的严格程度和从数据推断的“复杂性”。
This paper proposes a foundation for heterogeneous beliefs in games, in which disagreement arises not because players observe different information, but because they learn from common information in different ways. Players may be misspecified, and may moreover be misspecified about how others learn. The key assumption is that players nevertheless have some common understanding of how to interpret the data; formally, players have common certainty in the predictions of a class of learning rules. The common prior assumption is nested as the special case in which this class is a singleton. The main results characterize which rationalizable actions and Nash equilibria can be predicted when agents observe a finite quantity of data, and how much data is needed to predict different solutions. This number of observations depends on the degree of strictness of the solution and the "complexity" of inference from data.
竞争型号
DOI：
10.1093/qje/qjac015
发表时间：
2022
期刊：
The Quarterly Journal of Economics
影响因子：
--
作者：
Montiel Olea, José Luis;Ortoleva, Pietro;Pai, Mallesh M;Prat, Andrea
通讯作者：
Prat, Andrea