Problem definition: Agents in online marketplaces (such as ridesharing and freelancing platforms) are often strategic, and heterogeneous in their compatibility with different types of jobs: fully flexible agents can fulfill any job, whereas specialized agents can only complete specific subsets of jobs. Convention wisdom suggests reserving agents that are more flexible whenever possible, however this may incentivize agents to pretend to be more specialized, leading to loss in matches. We focus on designing a practical matching policy that performs well in a strategic environment. Methodology/results: We model the allocation of jobs to agents as a matching queue, and analyze the equilibrium performance of various matching policies when agents are strategic and report their own types. We show that reserving flexibility naively can backfire, to the extent that the equilibrium throughput can be arbitrarily bad compared to a policy which simply dispatches jobs to agents at random. To balance matching efficiency with agents' strategic considerations, we propose a new policy dubbed flexibility reservation with fallback and show that it enjoys robust performance. Managerial implications: Our work highlights the importance of considering agent strategic behavior when designing matching policies in online platforms and service systems. The robust performance guarantee, along with the parameter-free nature of our proposed policy makes it easy to implement in practice. We illustrate how this policy is implemented in the driver destination product of major ridesharing platforms.

翻译：问题定义：在线市场（如网约车和自由职业平台）中的参与者通常具有策略性，且在与不同类型工作的兼容性上存在异质性：完全灵活的参与者可以完成任何工作，而专业化参与者只能完成特定工作子集。传统观点建议尽可能保留更灵活的参与者，但这可能激励参与者伪装成更专业化的类型，从而导致匹配损失。我们专注于设计一种在策略环境中表现良好的实用匹配策略。方法论/结果：我们将工作分配给参与者建模为匹配队列，并分析在参与者具有策略性且报告自身类型时，各种匹配策略的均衡性能。我们发现，天真地保留灵活性可能适得其反，其均衡吞吐量可能比简单地将工作随机分配给参与者的策略差任意程度。为了平衡匹配效率与参与者的策略性考虑，我们提出了一种名为“带回退的灵活性保留”的新策略，并证明其具有稳健性能。管理启示：我们的工作强调了在设计在线平台和服务系统的匹配策略时考虑参与者策略行为的重要性。所提出策略的稳健性能保证及其无参数特性使其易于在实践中实施。我们阐述了该策略如何在主流网约车平台的司机目的地产品中实施。