论文标题
游戏中的稳定结果和信息：经验框架
Stable Outcomes and Information in Games: An Empirical Framework
论文作者
论文摘要
从经验上讲，许多战略环境的特征是稳定的结果，在这些结果中，球员的决定是公开观察的，但没有玩家抓住机会偏离。为了在存在不完整的信息的情况下分析此类情况，我们通过引入一种新的解决方案概念来构建经验框架，我们称之为贝叶斯稳定平衡。我们的框架使研究人员对玩家的信息和均衡选择规则不可知。贝叶斯稳定的平衡确定了集合崩溃到完整的信息纯策略nash平衡，该均衡识别在强烈的假设对玩家信息的假设下设置。此外，所有其他等同的东西都比贝叶斯相关的平衡弱弱。我们还提出了可估计和推理的计算方法。在一个应用程序中，我们研究了美国麦当劳和汉堡王的战略入境决策。我们的结果突出了信息假设的识别能力，并表明贝叶斯稳定的识别均衡设置可能比确定的贝叶斯相关平衡集更高。在反事实实验中，我们研究了增加健康食品对密西西比州食品沙漠市场结构的影响。
Empirically, many strategic settings are characterized by stable outcomes in which players' decisions are publicly observed, yet no player takes the opportunity to deviate. To analyze such situations in the presence of incomplete information, we build an empirical framework by introducing a novel solution concept that we call Bayes stable equilibrium. Our framework allows the researcher to be agnostic about players' information and the equilibrium selection rule. The Bayes stable equilibrium identified set collapses to the complete information pure strategy Nash equilibrium identified set under strong assumptions on players' information. Furthermore, all else equal, it is weakly tighter than the Bayes correlated equilibrium identified set. We also propose computationally tractable approaches for estimation and inference. In an application, we study the strategic entry decisions of McDonald's and Burger King in the US. Our results highlight the identifying power of informational assumptions and show that the Bayes stable equilibrium identified set can be substantially tighter than the Bayes correlated equilibrium identified set. In a counterfactual experiment, we examine the impact of increasing access to healthy food on the market structures in Mississippi food deserts.
