{"title":"Hiring as Exploration","authors":"Lindsey Raymond, Danielle Li, Peter Bergman","doi":"10.5465/amproc.2023.13216abstract","DOIUrl":null,"url":null,"abstract":"This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance “exploitation'' (selecting from groups with proven track records) with “exploration'' (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on “supervised learning","PeriodicalId":471028,"journal":{"name":"Proceedings - Academy of Management","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings - Academy of Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5465/amproc.2023.13216abstract","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance “exploitation'' (selecting from groups with proven track records) with “exploration'' (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on “supervised learning
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