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arxiv: 2410.06877 · v2 · pith:RYPKSOF7 · submitted 2024-10-09 · cs.GT

Best-of-Both-Worlds Fair Allocation of Indivisible and Mixed Goods

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classification cs.GT
keywords ex-postgoodsagentsallocationex-anteindivisiblemixedbest-of-both-worlds
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We study the problem of fairly allocating either a set of indivisible goods or a set of mixed divisible and indivisible goods (i.e., mixed goods) to agents with additive utilities, taking the best-of-both-worlds perspective of guaranteeing fairness properties both ex ante and ex post. The ex-post fairness notions considered in this paper are relaxations of envy-freeness, specifically, EFX for indivisible-goods allocation, and EFM for mixed-goods allocation. For two agents, we show that there is a polynomial-time randomized algorithm that achieves ex-ante envy-freeness and ex-post EFX / EFM simultaneously. For $n$ agents with bi-valued utilities, we show there exist randomized allocations that are (i) ex-ante proportional and ex-post EFM, and (ii) ex-ante envy-free, ex-post EFX, and ex-post fractionally Pareto optimal.

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Cited by 1 Pith paper

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

  1. Approximate Maximin Share with Subjective Divisibility: Beating the 1/2 Barrier

    cs.GT 2026-06 unverdicted novelty 6.0

    Under subjective divisibility, MMS approximation is 2/3-optimal for unary valuations, 5/9 in general, and 2/3 for up to four agents via new algorithms.