Fair Resource Allocation

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Equity Promotion in Online Resource Allocation
Dec 8, 2021Pan Xu, Yifan XuSequential Decision Making Fair Resource Allocation
Department of Computer Science, New Jersey Institute of Technology, Newark, USA · Key Lab of CNII, MOE, Southeast University, Nanjing, China

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Equity Promotion in Online Resource Allocation

Pan Xu, Yifan Xu

Department of Computer Science, New Jersey Institute of Technology, Newark, USA · Key Lab of CNII, MOE, Southeast University, Nanjing, China

Sequential Decision Making Fair Resource Allocation Group Fairness Constrained Optimization Linear Programming Online Resource Allocation

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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.

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Equity Promotion in Online Resource Allocation