Mislearning from censored data: The gambler's fallacy and other correlational mistakes in optimal-stopping problems

• 成果类型: Article
• 署名作者: He, Kevin
• 署名单位: University of Pennsylvania
• 刊物名称: THEORETICAL ECONOMICS
• ISSN/ISSBN: 1933-6837
• DOI: 10.3982/TE4657
• 发表日期: 2022-07-01
• 页码: 1269-1312
• 关键词: Misspecified learning; gambler's fallacy; Berk-Nash equilibrium; endogenous data censoring; fictitious variation; D83; D91

摘要

I study endogenous learning dynamics for people who misperceive intertemporal correlations in random sequences. Biased agents face an optimal-stopping problem. They are uncertain about the underlying distribution and learn its parameters from predecessors. Agents stop when early draws are good enough, so predecessors' experiences contain negative streaks but not positive streaks. When agents wrongly expect systematic reversals (the gambler's fallacy), they understate the likelihood of consecutive below-average draws, converge to overpessimistic beliefs about the distribution's mean, and stop too early. Agents uncertain about the distribution's variance overestimate it to an extent that depends on predecessors' stopping thresholds. I also analyze how other misperceptions of intertemporal correlation interact with endogenous data censoring.