Matching Queues, Flexibility and Incentives

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.