In the classical secretary problem, one attempts to find the maximum of an unknown and unlearnable\u00a0distribution through sequential search. In many real-world searches, however, distributions are not entirely\u00a0unknown and can be learned through experience. To investigate learning in such a repeated secretary problem\u00a0we conduct a large-scale behavioral experiment in which people search repeatedly from fixed distributions. In\u00a0contrast to prior investigations that find no evidence for learning in the classical scenario, in the repeated\u00a0setting we observe substantial learning resulting in near-optimal stopping behavior. We conduct a Bayesian\u00a0comparison of multiple behavioral models which shows that participants\u2019 behavior is best described by a class\u00a0of threshold-based models that contains the theoretically optimal strategy. In fact, fitting such a thresholdbased\u00a0model to data reveals players\u2019 estimated thresholds to be surprisingly close to the optimal thresholds\u00a0after 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\u00a0distribution through sequential search. In many real-world searches, however, distributions are not entirely\u00a0unknown and can be learned through experience. To investigate learning in such a repeated secretary problem\u00a0we conduct a large-scale behavioral experiment in which people search repeatedly from fixed […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-392618","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2017-6-26","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"393500","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/06\/secretary-working-paper.pdf","id":"393500","title":"secretary-working-paper","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Dan Goldstein","user_id":31618,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Dan Goldstein"},{"type":"user_nicename","value":"R. 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