The normality assumption in coordination games with flexible information acquisition
研究了线性二次型美式竞赛中，当玩家理性疏忽且基础变量非正态分布时，均衡策略的特征，发现偏离正态性会导致均衡平均行动分布与高斯模型定性不同，且预测误差放大率更高。
Many economic models assume that random variables follow normal (Gaussian) distributions. Yet, real-world variables may be non-normally distributed. How sensitive are these models' predictions to distribution misspecifications? This paper addresses the question in the context of linear-quadratic beauty contests played by rationally inattentive players. It breaks with the assumption that the (common prior) distribution of the fundamental be Gaussian and provides a characterization of the class of equilibria in continuous strategies. The characterization is used to show that small departures from normality can lead to distributions of the equilibrium average action that are qualitatively different from those of Gaussian models. Numerical results show that the rate at which an analyst's errors in determining the fundamental's distribution are amplified in her prediction is higher when the true prior is non-Gaussian than when it is an equally-misspecified Gaussian.