关于作为社会学习失败的统计歧视:多武装强盗办法 (On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach)

We analyze statistical discrimination in hiring markets using a multi-armed 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, underestimation towards them tends to persist. Even a slight population-ratio imbalance frequently produces perpetual underestimation. We propose two policy solutions: a novel subsidy rule (the hybrid mechanism) and the Rooney Rule. Our results indicate that temporary affirmative actions effectively mitigate discrimination caused by insufficient data.

翻译：我们使用多武装强盗模式分析雇用市场中的统计歧视。近亲公司面对的工人具有各种可观察到的特点。工人的技能和特点之间的联系在事前并不为人所知; 因此,公司需要了解这一点。 Laissez-faire造成长期低估:少数群体工人很少被雇用,因此,低估他们往往持续存在。即使是轻微的人口-拉皮欧不平衡也经常导致长期低估。我们提出了两种政策解决方案:一种新的补贴规则(混合机制)和Rooney规则。我们的结果表明,临时扶持行动有效地减轻了数据不足造成的歧视。