Dynamic Allocation of Scarce Resources

In this talk I consider tradeoffs between matching fast and slowly in different matching markets, and the role that hidden market information plays in resolving this tradeoff. In many matching problems, such as the allocation of organs and public housing, agents are matched to resources over time. A common question in such problems is whether to match agents quickly to lower waiting times, or slowly and more carefully to make more or higher-quality matches.

I study this tradeoff first in kidney exchanges where the participants’ information is easily observable or elicitable by the planner, and second, in matching markets where there is hidden information. A greedy policy which attempts to match agents upon arrival, ignores the benefit that waiting agents provide by facilitating future matchings. However, I prove that in kidney exchanges the trade-off between a “thicker” market and faster matching vanishes in large markets: the greedy policy leads to shorter waiting times and more agents matched than any other policy. I also empirically confirm this in data from the National Kidney Registry. Greedy matching achieves as many transplants as commonly used policies (1.8% more than monthly batching) and shorter waiting times (16 days faster than monthly batching). 

This conclusion can change in markets where the agents have private information about their willingness-to-wait for higher quality matches. I will discuss optimal solutions through information design, where we discover tradeoffs between distributional equality and allocative efficiency.