Corpus ID: 53635442

Mislearning from Censored Data: The Gambler's Fallacy in Optimal-Stopping Problems

@article{He2018MislearningFC,
  title={Mislearning from Censored Data: The Gambler's Fallacy in Optimal-Stopping Problems},
  author={Kevin He},
  journal={arXiv: Economics},
  year={2018}
}
  • Kevin He
  • Published 2018
  • Economics, Computer Science
  • arXiv: Economics
I study endogenous learning dynamics for people expecting systematic reversals from random sequences -- the "gambler's fallacy." Biased agents face an optimal-stopping problem, such as managers conducting sequential interviews. They are uncertain about the underlying distribution (e.g. talent distribution in the labor pool) and must learn its parameters from previous agents' histories. Agents stop when early draws are deemed "good enough," so predecessors' histories contain negative streaks but… Expand
17 Citations
Research Statement
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Which Misperceptions Persist?
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Misinterpreting Others and the Fragility of Social Learning
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Limit Points of Endogenous Misspecified Learning
  • 9
  • Highly Influenced
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Belief Convergence under Misspecified Learning: A Martingale Approach
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Stability and Robustness in Misspecified Learning Models
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  • Highly Influenced
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References

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Informational herding with model misspecification
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Decision-Making Under the Gambler&Apos;S Fallacy: Evidence from Asylum Judges, Loan Officers, and Baseball Umpires
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