{"title":"招聘作为探索","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":"{\"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}","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}
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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