Learning When to Stop Searching

Daniel Goldstein, Randolph McAfee, Siddharth Suri () and James R. Wright ()
Additional contact information
Siddharth Suri: Microsoft Research, New York, New York 10011;
James R. Wright: Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada

Management Science, 2020, vol. 66, issue 3, 1375-1394

Abstract: In the classical secretary problem, one attempts to find the maximum of an unknown and unlearnable distribution through sequential search. In many real-world searches, however, distributions are not entirely unknown and can be learned through experience. To investigate learning in such settings, we conduct a large-scale behavioral experiment in which people search repeatedly from fixed distributions in a “repeated secretary problem.” In contrast to prior investigations that find no evidence for learning in the classical scenario, in the repeated setting we observe substantial learning resulting in near-optimal stopping behavior. We conduct a Bayesian comparison of multiple behavioral models, which shows that participants’ behavior is best described by a class of threshold-based models that contains the theoretically optimal strategy. Fitting such a threshold-based model to data reveals players’ estimated thresholds to be close to the optimal thresholds after only a small number of games.

Keywords: Bayesian model comparison; experiments; human behavior; learning; secretary problem (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:66:y:2020:i:3:p:1375-1394

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