  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.
