算法同质化下企业抢人拥堵，策略分化能显著提升效率。
Strategic Hiring under Algorithmic Monoculture
- 企业用统一算法选人时，会扎堆抢高分候选人，导致拥堵。
- 均衡策略使企业分散目标，福利提升与企业数成正比，效率接近最优。
- 平台需公开拥挤信息，企业才能实现最优策略，否则无法自适应。
我们研究在算法同质化劳动市场中，企业策略行为的影响。当企业使用同一算法评估候选人并竞争同一申请人池时，采用‘非策略性’招聘方式会导致严重拥堵，因企业集体追逐相同高分候选人。我们将此竞争建模为容量受限企业的博弈，并完全刻画了纳什均衡集。结果表明，均衡策略自然促使企业差异化面试目标，显著优于非策略选择：战略差异化带来的社会福利提升（即‘非策略选择代价’）随企业数量线性增长；而去中心化导致的效率损失（即‘无政府状态代价’）趋近于1，表明均衡几乎达到社会最优。最后，我们分析了收敛性，证明简单的顺序最优响应过程可收敛至理想均衡。然而，企业仅凭自身历史数据无法推断特定候选人的拥挤程度，因此要实现福利增益，算法平台必须显式向企业披露拥挤信息。
原文摘要 · Abstract (English)
We study the impact of strategic behavior in labor markets characterized by algorithmic monoculture, where firms compete for a shared pool of applicants using a common algorithmic evaluation. In this setting, "naive" hiring strategies lead to severe congestion, as firms collectively target the same high-scoring candidates. We model this competition as a game with capacity-constrained firms and fully characterize the set of Nash equilibria. We demonstrate that equilibrium strategies, which naturally diversify firms' interview targets, significantly outperform naive selection, increasing social welfare for both firms and applicants. Specifically, the Price of Naive Selection (welfare gain from strategy) grows linearly with the number of firms, while the Price of Anarchy (efficiency loss from decentralization) approaches 1, implying that the decentralized equilibrium is nearly socially optimal. Finally, we analyze convergence, and we show that a simple sequential best-response process converges to the desired equilibrium. However, we show that firms generally cannot infer the key input needed to compute best responses, namely congestion for specific candidates, from their own historical data alone. Consequently, to realize the welfare gains of strategic differentiation, algorithmic platforms must explicitly reveal congestion information to participating firms.
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