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

Kevin He

Theoretical Economics, 2022, vol. 17, issue 3

Abstract: 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 over-pessimistic 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.

Keywords: Misspecified learning; gambler’s fallacy; Berk-Nash equilibrium; endogenous data censoring; fictitious variation (search for similar items in EconPapers)
JEL-codes: D83 D91 (search for similar items in EconPapers)
Date: 2022-07-14
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Citations: View citations in EconPapers (6)

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Related works:
Working Paper: Mislearning from Censored Data: The Gambler's Fallacy and Other Correlational Mistakes in Optimal-Stopping Problems (2021)
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