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Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment
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.
Distributionally Robust Optimal Allocation with Costly Verification
However, this result relies on the unrealistic assumptions that the agents' types follow known independent probability distributions.
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