论文标题
匹配队列，灵活性和激励措施
Matching Queues, Flexibility and Incentives
论文作者
论文摘要
问题定义：在许多匹配的市场中，有些代理人完全灵活，而另一些代理只接受了一部分工作。传统的观点表明保留灵活的代理商，但这可能会适得其反：预计匹配的机会更高，代理可能会误导专家，减少整体比赛。我们询问平台如何设计简单的匹配策略，这些策略在战略性上采取行动时仍然有效。方法论/结果：我们将作业分配建模为匹配队列，并在代理报告其类型时在不同策略下分析平衡吞吐量。我们表明，在完整信息下，灵活性保留是最佳的，但使用私人信息的性能很差，有时比随机分配差得多。为了解决这个问题，我们提出了一项新的政策 - 屈从的灵活性保留 - 保证了整个设置的稳健性能。管理含义：我们的结果强调了在政策设计中考虑战略报告的重要性。拟议的后备政策将鲁棒性与简单性相结合，使得在乘车和负担得起的住房分配等平台中实施变得可行。
Problem definition: In many matching markets, some agents are fully flexible, while others only accept a subset of jobs. 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 matching queue and analyze equilibrium throughput performance under different policies when agents report their types. 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. Managerial implications: Our results underscore the importance of accounting for strategic reporting in policy design. The proposed fallback policy combines robustness with simplicity, making it practical to implement in platforms such as ride-hailing and affordable housing allocation.
