Robust Predictions in Games with Incomplete Information
Robust Predictions in Games with Incomplete Information
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DOI：
10.2139/ssrn.2163606
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发表时间：
2011-09
期刊：
影响因子：
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通讯作者：
D. Bergemann;S. Morris
中科院分区：
文献类型：
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作者：
D. Bergemann;S. Morris
我们分析不完整信息的博弈，并提供均衡预测，该预测对于代理可能拥有的所有可能的私人信息结构都是有效的，并且在这个意义上是稳健的。对于某些信息结构，在均衡中可能出现的结果集等于贝叶斯相关均衡集。我们完全描述了一类具有二次收益和正态分布不确定性的博弈中的贝叶斯相关均衡集，对平衡动作状态分布的一阶矩和二阶矩的限制。我们得出有关私人信息的先验知识如何完善均衡预测集的精确界限。我们考虑需求不确定性下企业之间的信息共享，并通过贝叶斯相关均衡找到新的最优信息策略。我们还扭转了视角，在考虑到私人信息的稳健性的情况下研究了身份识别问题。私人信息的存在导致游戏结构参数的确定而不是点识别。
We analyze games of incomplete information and offer equilibrium predictions which are valid for, and in this sense robust to, all possible private information structures that the agents may have. The set of outcomes that can arise in equilibrium for some information structure is equal to the set of Bayes correlated equilibria. We completely characterize the set of Bayes correlated equilibria in a class of games with quadratic payoffs and normally distributed uncertainty in terms of restrictions on the first and second moments of the equilibrium action-state distribution. We derive exact bounds on how prior knowledge about the private information refines the set of equilibrium predictions. We consider information sharing among firms under demand uncertainty and find new optimal information policies via the Bayes correlated equilibria. We also reverse the perspective and investigate the identification problem under concerns for robustness to private information. The presence of private information leads to set rather than point identification of the structural parameters of the game.