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.

翻译：问题定义：在许多匹配市场中，部分参与者具备完全灵活性，而其他参与者仅接受特定子集的工作任务。例如，网约车司机可在平台上指定其愿意接受的目的地。传统观点建议保留灵活参与者，但这可能适得其反：参与者预期更高的匹配机会时，可能会谎报为专长型参与者，从而减少整体匹配数量。我们探讨平台如何设计简单的匹配策略，使其在参与者采取策略性行为时仍保持有效性。方法论/结果：我们将工作分配建模为二分匹配排队系统，分析参与者在选择加入队列时不同策略下的均衡吞吐性能。研究表明，在完全信息条件下灵活性保留策略是最优的，但在私有信息条件下可能表现不佳，有时甚至显著劣于随机分配策略。为解决此问题，我们提出一种新策略——带后备机制的灵活性保留策略——该策略无需精确掌握系统参数或参与者效用函数，即可保证在各种场景下的稳健性能。管理启示：我们的研究结果强调了在匹配策略设计中考虑策略性报告的重要性：所提出的后备机制既能保留灵活性，又能在显性灵活参与者耗尽时挖掘潜在灵活性。其简洁性和无参数特性也使其易于在网约车和保障性住房分配等平台中实施。