Jacob Leshno, Business Economics PhD
Thesis Chair: Alvin Roth
Dissertation Title: Essays on Matching Markets
Job Market Paper: Dynamic Matching in Overloaded Systems
In many assignment problems items arrive stochastically over time. When items are scarce agents form an overloaded waiting list and items are dynamically allocated as they arrive, for example public housing and organs for transplant. Even when all the scarce items are allocated there is an efficiency question: which policies assign the right items to the right agents? I develop a model in which impatient agents with heterogeneous preferences wait to be assigned scarce heterogeneous items that arrive stochastically over time. Social welfare is maximized by appropriately matching agents to items, but an individual impatient agent may misreport her preferences to receive an earlier mismatched item. To incentivize an agent to avoid mismatch the policy needs to provide the agent with a (stochastic) guarantee of future assignment, which we model as putting the agents in a priority buffer-queue. I first consider a standard queue-based allocation policy and derive its welfare properties. To determine the optimal policy I formulate the dynamic assignment problem as a dynamic mechanism design problem without transfers. The resulting optimal incentive compatible policy employs a new queueing policy, the uniform wait queue, to minimize the probability of mismatching agents. Finally, I derive a robustly optimal policy which uses a simple rule: giving equal priority to every agent who declines a mismatched item (a SIRO buffer-queue). This robustly optimal policy has appealing properties that make it an attractive market design solution.
