Equity Promotion in Online Resource Allocation

TL;DR

This paper introduces LP-based sampling algorithms for equitable online resource allocation, ensuring fair distribution among demographic groups, validated through theoretical analysis and COVID-19 vaccination data experiments.

Contribution

It proposes novel LP-based sampling strategies for promoting equity in online resource allocation, with theoretical guarantees and empirical validation.

Findings

Strategies effectively promote equity in resource distribution.

Algorithms perform well with disproportionate group representation.

Empirical results confirm theoretical advantages.

Abstract

We consider online resource allocation under a typical non-profit setting, where limited or even scarce resources are administered by a not-for-profit organization like a government. We focus on the internal-equity by assuming that arriving requesters are homogeneous in terms of their external factors like demands but heterogeneous for their internal attributes like demographics. Specifically, we associate each arriving requester with one or several groups based on their demographics (i.e., race, gender, and age), and we aim to design an equitable distributing strategy such that every group of requesters can receive a fair share of resources proportional to a preset target ratio. We present two LP-based sampling algorithms and investigate them both theoretically (in terms of competitive-ratio analysis) and experimentally based on real COVID-19 vaccination data maintained by the Minnesota Department of Health. Both theoretical and numerical results show that our LP-based sampling strategies can effectively promote equity, especially when the arrival population is disproportionately represented, as observed in the early stage of the COVID-19 vaccine rollout.