Fair Dynamic Rationing
Vahideh Manshadi, Rad Niazadeh, Scott Rodilitz

TL;DR
This paper introduces a simple, adaptive rationing policy called projected proportional allocation (PPA) that achieves near-optimal fairness in sequential, correlated demand scenarios like COVID-19 resource distribution, outperforming traditional methods.
Contribution
The paper develops a novel adaptive policy, PPA, that guarantees near-optimal fairness in complex, correlated demand settings without requiring detailed distributional knowledge.
Findings
PPA achieves matching lower bounds for fairness objectives.
PPA outperforms non-adaptive target-fill-rate policies.
Numerical results show PPA's effectiveness in COVID-19 resource allocation.
Abstract
We study the allocative challenges that governmental and nonprofit organizations face when tasked with equitable and efficient rationing of a social good among agents whose needs (demands) realize sequentially and are possibly correlated. As one example, early in the COVID-19 pandemic, the Federal Emergency Management Agency faced overwhelming, temporally scattered, a priori uncertain, and correlated demands for medical supplies from different states. In such contexts, social planners aim to maximize the minimum fill rate across sequentially arriving agents, where each agent's fill rate is determined by an irrevocable, one-time allocation. For an arbitrarily correlated sequence of demands, we establish upper bounds on the expected minimum fill rate (ex-post fairness) and the minimum expected fill rate (ex-ante fairness) achievable by any policy. Our upper bounds are parameterized by the…
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