On Statistical Discrimination as a Failure of Social Learning: A Multiarmed Bandit Approach

We analyze statistical discrimination in hiring markets using a multiarmed bandit model. Myopic firms face workers arriving with heterogeneous observable characteristics. The association between the worker’s skill and characteristics is unknown ex ante; thus, firms need to learn it. Laissez-faire causes perpetual underestimation: minority workers are rarely hired, and therefore, the underestimation tends to persist. Even a marginal imbalance in the population ratio frequently results in perpetual underestimation. We demonstrate that a subsidy rule that is implemented as temporary affirmative action effectively alleviates discrimination stemming from insufficient data. This paper was accepted by Nicolas Stier-Moses, Special Issue on the Human-Algorithm Connection. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant 430-2020-00088] and JST ERATO [Grant JPMJER2301], Japan. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00893 .

• 저널Management Science · 72(1) · 442–455
• 토픽Advanced Bandit Algorithms Research · Management Science and Operations Research
• DOI10.1287/mnsc.2022.00893
• 저자Junpei Komiyama, Shunya Noda