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.

翻译：本研究探讨了在算法单一化的劳动力市场中策略性行为的影响，该市场中企业使用共同的算法评估来竞争共享的申请人池。在此背景下，"朴素"的招聘策略会导致严重的拥堵现象，因为企业会共同瞄准同一批高分候选人。我们将这种竞争建模为具有容量约束企业的博弈，并完整刻画了纳什均衡的集合。我们证明，均衡策略能自然实现企业面试目标的多样化，显著优于朴素选择策略，从而提升企业和申请人的整体社会福利。具体而言，朴素选择代价（策略带来的福利增益）随企业数量线性增长，而无政府代价（分散化导致的效率损失）趋近于1，这意味着分散化均衡近乎社会最优。最后，我们分析了收敛性，证明简单的序贯最优响应过程能够收敛到期望均衡。然而，我们发现企业通常无法仅从自身历史数据中推断计算最优响应所需的关键输入——即特定候选人的拥堵信息。因此，为实现策略性分化带来的福利增益，算法平台必须向参与企业明确披露拥堵信息。