Naive calibration
研究非贝叶斯决策者如何基于历史数据校准简单预测规则，发现该过程收敛于稳态并产生由反馈循环加剧的悲观行为，应用于项目选择和拍卖问题。
We develop a model of non‐Bayesian decision‐making in which an agent obtains a signal about a relevant economic fundamental and subsequently takes an action. To interpret the signal, the agent calibrates a simple prediction rule based on a data set that consists of previous signals and state realizations. Her subsequent action affects the probability with which the current signal and the corresponding state realization will be observed and recorded in the data set that will be used in future decisions. We show that this procedure converges to a steady state and that it results in a seemingly pessimistic behavior that is exacerbated by feedback loops. We apply our model to project selection problems and second‐price internet protocol version auctions.