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 \u201crepeated secretary problem.\u201d 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\u2019 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\u2019 estimated thresholds to be close to the optimal thresholds after only a small number of games.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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