Matching Queues, Flexibility, and Incentives

Problem definition: In many matching markets, some agents are fully flexible, whereas others only accept a subset of jobs. For example, ride-sharing drivers can specify on the platform the destinations that 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 ride-sharing and affordable housing allocation.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0774.