Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment


Event details

Date 04.10.2023
Hour 16:0017:00
Speaker Professor Çağıl Koçyiğit
Location Online
Category Conferences - Seminars
Event Language English
Abstract: We study the problem of allocating scarce societal resources of different types (e.g., permanent housing, deceased donor kidneys for transplantation, ventilators) to heterogeneous allocatees on a waitlist (e.g., people experiencing homelessness, individuals suffering from end-stage renal disease, Covid-19 patients) based on their observed covariates. We leverage administrative data collected in deployment to design an online policy that maximizes expected outcomes while satisfying budget constraints, in the long run. Our proposed policy waitlists each individual for the resource maximizing the difference between their estimated mean treatment outcome and the estimated resource dual-price or, roughly, the opportunity cost of using the resource. Resources are then allocated as they arrive, in a first-come first-serve fashion. We demonstrate that our data-driven policy almost surely asymptotically achieves the expected outcome of the optimal out-of-sample policy under mild technical assumptions. We extend our framework to incorporate various fairness constraints. We evaluate the performance of our approach on the problem of designing policies for allocating scarce housing resources to people experiencing homelessness in Los Angeles based on data from the homeless management information system. In particular, we show that using our policies improves rates of exit from homelessness by 1.9% and that policies that are fair in either allocation or outcomes by race come at a very low price of fairness.
Joint work with Bill Tang, Eric Rice, and Phebe Vayanos

Bio sketch: Çağıl Koçyiğit is an assistant professor at the Luxembourg Centre for Logistics and Supply Chain Management, University of Luxembourg. Her research focuses on optimization under uncertainty with applications in economics, resource allocation, and mechanism design. Prior to joining the University of Luxembourg, she received a Ph.D. in Management of Technology from EPFL in 2020, where she worked with Daniel Kuhn.


Practical information

  • Informed public
  • Free


  • Professor Daniel Kuhn