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On Fair Allocation of Indivisible Goods to Submodular Agents
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abstract
We consider the problem of fair allocation of indivisible goods to agents with submodular valuation functions, where agents may have either equal entitlements or arbitrary (possibly unequal) entitlements. We focus on share-based fairness notions, specifically, the maximin share (MMS) for equal entitlements and the anyprice share (APS) for arbitrary entitlements, and design allocation algorithms that give each agent a bundle of value at least some constant fraction of her share value. For the equal entitlement case (and submodular valuations), Ghodsi, Hajiaghayi, Seddighin, Seddighin, and Yami [EC 2018] designed a polynomial-time algorithm for $\frac{1}{3}$-maximin-fair allocation. We improve this result in two different ways. We consider the general case of arbitrary entitlements, and present a polynomial time algorithm that guarantees submodular agents $\frac{1}{3}$ of their APS. For the equal entitlement case, we improve the approximation ratio and obtain $\frac{10}{27}$-maximin-fair allocations. Our algorithms are based on designing strategies for a certain bidding game that was previously introduced by Babaioff, Ezra and Feige [EC 2021].
Forward citations
Cited by 3 Pith papers
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From multi-allocations to allocations, with subadditive valuations
A d-multi-allocation with subadditive valuations can be converted to an allocation losing only a factor of about d, yielding an Omega(1/log log n)-MMS guarantee.
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Online Fair Division with Additional Information
With normalization information, EF1 for two agents and PROP1 for all n are achievable; with frequency predictions, any offline share-based guarantee can be matched online.
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Fair Allocation of Divisible Goods under Non-Linear Valuations
For non-linear valuations over divisible goods, a 1/(2n−1)-MMS allocation always exists (with 1/n being impossible), the 1/n bound is tight for up to three agents, and finding an envy-free efficient allocation is NP-h...
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