Matching Queues, Flexibility and Incentives

TL;DR

This paper studies how matching platforms can design simple, robust policies that account for strategic agent behavior, ensuring effective matching in markets with flexible and specialized agents.

Contribution

It introduces a new fallback policy for matching queues that performs well under private information and strategic agent behavior, improving robustness over traditional flexibility reservation.

Findings

Flexibility reservation can perform poorly with private information.

The fallback policy guarantees robust performance across different settings.

The proposed policy is simple, parameter-free, and practical for real-world platforms.

Abstract

Problem definition: In many matching markets, some agents are fully flexible, while others only accept a subset of jobs. For example, ridesharing drivers can specify on the platform the destinations they are willing to accept. Conventional wisdom suggests reserving flexible agents, but this can backfire: anticipating higher matching chances, agents may misreport as specialized, reducing overall matches. We ask how platforms can design simple matching policies that remain effective when agents act strategically. Methodology/results: We model job allocation as a bipartite matching queueing system and analyze equilibrium throughput performance under different policies when agents choose which queue to join. We show that flexibility reservation is optimal under full information but can perform poorly with private information, sometimes substantially worse than random assignment. To address this, we propose a new policy -- flexibility reservation with fallback -- that guarantees robust performance across settings, without requiring precise knowledge of system parameters or agent utility functions. Managerial implications: Our results underscore the importance of accounting for strategic reporting in the design of matching policies: the proposed fallback policy both preserves flexibility and exploits latent flexibility when explicitly flexible agents are exhausted. Its simplicity and parameter-free nature also make it practical to implement in platforms such as ridesharing and affordable housing allocation.