Hiring under Congestion and Algorithmic Monoculture: Value of Strategic Behavior

2025-02-27

AI Summary

This paper studies strategic interview decisions in hiring markets where multiple firms employ homogeneous algorithms to evaluate applicants competing for a limited pool of candidates. We model this scenario as a capacity-constrained congestion game and provide the first formal analysis of the structural properties of such algorithmically homogenized recruitment games. We rigorously characterize the existence and uniqueness of Nash equilibria, proving that equilibrium outcomes—measured by social welfare (i.e., total successful placements)—substantially outperform naive high-score-first policies, especially when the number of firms is large and individual interview capacities are small. We introduce the concept of “competition heat”—a succinct summary statistic—that dramatically reduces equilibrium computation complexity and accelerates sequential best-response convergence. Price-of-Anarchy analysis reveals negligible efficiency loss at equilibrium, demonstrating that strategic interaction mitigates congestion induced by algorithmic homogeneity and enhances aggregate employment rates.

Technology Category

• Search and Optimization: Algorithm Configuration
• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

We study the impact of strategic behavior in a setting where firms compete to hire from a shared pool of applicants, and firms use a common algorithm to evaluate them. Each applicant is associated with a scalar score that is observed by all firms, provided by the algorithm. Firms simultaneously make interview decisions, where the number of interviews is capacity-constrained. Job offers are given to those who pass the interview, and an applicant who receives multiple offers accepts one of them uniformly at random. We fully characterize the set of Nash equilibria under this model. Defining social welfare as the total number of applicants who find a job, we then compare the social welfare at a Nash equilibrium to a naive baseline where all firms interview applicants with the highest scores. We show that the Nash equilibrium greatly improves upon social welfare compared to the naive baseline, especially when the interview capacity is small and the number of firms is large. We also show that the price of anarchy is small, providing further appeal for the equilibrium solution. We then study how the firms may converge to a Nash equilibrium. We show that when firms make interview decisions sequentially and each firm takes the best response action assuming they are the last to act, this process converges to an equilibrium when interview capacities are small. However, we show that the task of computing the best response is difficult if firms have to use its own historical samples to estimate it, while this task becomes trivial if firms have information on the degree of competition for each applicant. Therefore, converging to an equilibrium can be greatly facilitated if firms have information on the level of competition for each applicant.

Problem

• Impact of strategic hiring behavior
• Comparison of social welfare in Nash equilibrium
• Convergence to equilibrium under competition information

Innovation

• Algorithmic evaluation of applicants
• Nash equilibrium improves social welfare
• Sequential decision-making converges to equilibrium