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Weighted Envy-Freeness for Submodular Valuations

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Listed:
  • Luisa Montanari
  • Ulrike Schmidt-Kraepelin
  • Warut Suksompong
  • Nicholas Teh

Abstract

We investigate the fair allocation of indivisible goods to agents with possibly different entitlements represented by weights. Previous work has shown that guarantees for additive valuations with existing envy-based notions cannot be extended to the case where agents have matroid-rank (i.e., binary submodular) valuations. We propose two families of envy-based notions for matroid-rank and general submodular valuations, one based on the idea of transferability and the other on marginal values. We show that our notions can be satisfied via generalizations of rules such as picking sequences and maximum weighted Nash welfare. In addition, we introduce welfare measures based on harmonic numbers, and show that variants of maximum weighted harmonic welfare offer stronger fairness guarantees than maximum weighted Nash welfare under matroid-rank valuations.

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  • Luisa Montanari & Ulrike Schmidt-Kraepelin & Warut Suksompong & Nicholas Teh, 2022. "Weighted Envy-Freeness for Submodular Valuations," Papers 2209.06437, arXiv.org.
    • Handle: RePEc:arx:papers:2209.06437
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    File URL: http://arxiv.org/pdf/2209.06437
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    Cited by:

    1. Warut Suksompong & Nicholas Teh, 2023. "Weighted Fair Division with Matroid-Rank Valuations: Monotonicity and Strategyproofness," Papers 2303.14454, arXiv.org, revised Sep 2023.
    2. Suksompong, Warut & Teh, Nicholas, 2023. "Weighted fair division with matroid-rank valuations: Monotonicity and strategyproofness," Mathematical Social Sciences, Elsevier, vol. 126(C), pages 48-59.

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