匹配队列、灵活性与激励
Matching Queues, Flexibility and Incentives
AI总结：
本文提出了一种在匹配市场中保留灵活性并应对战略报告的后备政策，以提高匹配效率。
摘要：
问题定义：在许多匹配市场中，一些代理人完全灵活，而其他代理人只接受一部分工作。例如，拼车司机可以在平台上指定他们愿意接受的目的地。传统观念认为应保留灵活的代理人，但这种做法可能会适得其反： anticipating更高的匹配机会，代理人可能会错误报告为专门化，从而减少总体匹配。我们询问平台如何设计简单的匹配政策，以在代理人采取战略行为时仍能保持有效。 方法学/结果：我们将工作分配建模为一个二分图匹配排队系统，并分析在代理人选择加入哪个队列时，不同政策下的均衡吞吐量性能。我们显示，在完全信息下，保留灵活性是最佳的，但在私人信息下表现可能较差，有时比随机分配差很多。为了解决这个问题，我们提出了一种新的政策——保留灵活性并有后备选项——它在各种情况下都能保证稳健的性能，而不需要精确了解系统参数或代理人效用函数。 管理意义：我们的结果强调了在设计匹配政策时考虑战略报告的重要性：所提出的后备政策既保留了灵活性，又在显式灵活的代理人耗尽时利用潜在的灵活性。其简单性和无参数的性质也使其在拼车和经济适用房分配等平台中易于实施。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
