Learning When to Stop Searching

• 成果类型: Article
• 署名作者: Goldstein, Daniel G.; McAfee, Preston; Suri, Siddharth; Wright, James R.
• 署名单位: Microsoft; Microsoft; University of Alberta
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2018.3245
• 发表日期: 2020
• 页码: 1375-1394
• 关键词: Bayesian model comparison; experiments; human behavior; learning; Secretary problem

摘要

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.