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Fairness and Diversity in Public Resource Allocation Problems

Abstract : In this article, we address important extensions to the problem of allocating indivisible items to a population of agents: The agents are partitioned into disjoint groups on the basis of attributes (e.g., ethnicity) and we want the overall utility of the allocation to respect some notion of diversity and/or fairness with respect to these groups. We study two specific incarnations of this general problem. First, we address a constrained optimization problem, inspired by diversity quotas in some real-world allocation problems, where the items are also partitioned into blocks and there is an upper bound on the number of items from each block that can be assigned to agents in each group. We theoretically analyze the price of diversity-a measure of the overall welfare loss due to these capacity constraints-and report experiments based on two real-world data sets (Singapore public housing and Chicago public school admissions) comparing this constrained optimization-based approach with a lottery mechanism with similar quotas. Next, instead of imposing hard constraints, we cast the problem as a variant of fair allocation of indivisible goods-we treat each group of agents as a single entity receiving a bundle of items whose valuation is the maximum total utility of matching agents in that group to items in that bundle; we present algorithms that achieve a standard relaxation of envy-freeness in conjunction with specific efficiency criteria.
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Submitted on : Thursday, October 10, 2019 - 12:19:48 PM
Last modification on : Monday, October 14, 2019 - 9:30:30 AM

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Nawal Benabbou, Mithun Chakraborty, Yair Zick. Fairness and Diversity in Public Resource Allocation Problems. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 2019. ⟨hal-02308588⟩

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