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The Constrained Round Robin Algorithm for Fair and Efficient Allocation

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arxiv 1908.00161 v1 pith:MMVNKELE submitted 2019-08-01 cs.GT cs.DS

Haris Aziz , Xin Huang , Nicholas Mattei , Erel Segal-Halevi This is my paper · ORCID
classification cs.GTcs.DS
keywords algorithmallocationwelfareconstrainedefficientenvy-freenessfairnessoracle
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We consider a multi-agent resource allocation setting that models the assignment of papers to reviewers. A recurring issue in allocation problems is the compatibility of welfare/efficiency and fairness. Given an oracle to find a welfare-achieving allocation, we embed such an oracle into a flexible algorithm called the Constrained Round Robin (CRR) algorithm, that achieves the required welfare level. Our algorithm also allows the system designer to lower the welfare requirements in order to achieve a higher degree of fairness. If the welfare requirement is lowered enough, a strengthening of envy-freeness up to one item is guaranteed. Hence, our algorithm can be viewed as a computationally efficient way to interpolate between welfare and approximate envy-freeness in allocation problems.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fair Division with Social Impact

    cs.GT 2024-12 conditional novelty 7.0 of 10

    Fair allocations with standard fairness notions can lose a linear factor of social impact, but a socially aware envy-freeness definition lets an optimal social-impact allocation be fair.

  2. A polynomial-time algorithm for computing a Pareto optimal and almost proportional allocation

    cs.GT 2019-09 conditional novelty 7.0 of 10

    For mixed goods and chores, a weighted Pareto-optimal allocation that is proportional up to one item always exists and is computable in strongly polynomial time.

  3. Envy-Free but Still Unfair: Envy-Freeness Up To One Item (EF-1) in Personalized Recommendation

    cs.IR 2025-09 conditional novelty 4.0 of 10

    Envy-freeness (EF1) can hold even when personalized recommendations are highly unfair to a minority group, so it is an insufficient fairness metric.

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