Learning When to Stop Searching
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.Citation
D. Goldstein, R. McAfee, S. Suri, J. Wright. "Learning When to Stop Searching". Management Science, pp 1-20, August 2019.| Keywords: | Bayesian model comparison, experiments, human behavior, learning, secretary problem |
| Category: | In Journal |
| Web Links: | DOI |
| Management Science |
BibTeX
@article{Goldstein+al:19,
author = {Daniel G. Goldstein and R. Preston McAfee and Siddharth Suri and
James R. Wright},
title = {Learning When to Stop Searching},
Pages = {1-20},
journal = {Management Science},
year = 2019,
}Last Updated: February 25, 2020Submitted by Sabina P
