Dynamic Allocation of Scarce Resources

Afshin is an assistant professor of Economics at University of Southern California. His research is centered on market and mechanism design, and draws upon insights from Economics and Computer Science to design or improve centralized markets with respect to objective criteria such as efficiency and equality. His work has been featured in conferences such as EC and FOCS, and in journals such as The Review of Economic Studies, Management Science, Operations Research, and PNAS. He completed his PhDs at Stanford in Economics and also in Operations Research in 2018.

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Abstract: 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.