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

• 成果类型: Article; Early Access
• 署名作者: Komiyama, Junpei; Noda, Shunya
• 署名单位: New York University; University of Tokyo
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.00893
• 发表日期: 2024
• 关键词: statistical discrimination; social learning; affirmative action; multiarmed bandit; algorithmic fairness

摘要

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.