论文标题
关于统计歧视是社会学习的失败：多军匪徒的方法
On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach
论文作者
论文摘要
我们使用多军匪徒模型分析招聘市场中的统计歧视。近视公司面临的工人以异类可观察的特征到达。工人的技能和特征之间的关联是未知的事；因此，企业需要学习它。自由放任会导致永久低估：少数族裔工人很少被雇用，因此，低估往往会持续存在。即使人口比率的边际失衡也常常导致永久低估。我们提出了两种政策解决方案：一种新颖的补贴规则（混合机制）和鲁尼规则。我们的结果表明，暂时的平权行动有效地减轻了由于数据不足而产生的歧视。
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, the underestimation tends to persist. Even a marginal imbalance in the population ratio frequently results in 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 alleviate discrimination stemming from insufficient data.
