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Envy-free Relaxations for Goods, Chores, and Mixed Items

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arxiv 2006.04428 v1 pith:35BN4BMQ submitted 2020-06-08 econ.TH cs.DMcs.GT

classification econ.THcs.DMcs.GT
keywords functionsutilityagentsallocationenvy-freenessitemsallocationsidentical
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abstract

In fair division problems, we are given a set $S$ of $m$ items and a set $N$ of $n$ agents with individual preferences, and the goal is to find an allocation of items among agents so that each agent finds the allocation fair. There are several established fairness concepts and envy-freeness is one of the most extensively studied ones. However envy-free allocations do not always exist when items are indivisible and this has motivated relaxations of envy-freeness: envy-freeness up to one item (EF1) and envy-freeness up to any item (EFX) are two well-studied relaxations. We consider the problem of finding EF1 and EFX allocations for utility functions that are not necessarily monotone, and propose four possible extensions of different strength to this setting. In particular, we present a polynomial-time algorithm for finding an EF1 allocation for two agents with arbitrary utility functions. An example is given showing that EFX allocations need not exist for two agents with non-monotone, non-additive, identical utility functions. However, when all agents have monotone (not necessarily additive) identical utility functions, we prove that an EFX allocation of chores always exists. As a step toward understanding the general case, we discuss two subclasses of utility functions: Boolean utilities that are $\{0,+1\}$-valued functions, and negative Boolean utilities that are $\{0,-1\}$-valued functions. For the latter, we give a polynomial time algorithm that finds an EFX allocation when the utility functions are identical.

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  1. A Better-than-$e^{1/e}$ Approximation Algorithm for Nash Social Welfare under Additive Valuations

    cs.GT 2026-07 conditional novelty 7.0 of 10

    An efficient randomized algorithm approximates max Nash social welfare under additive valuations by e^{1/e} - c for some c > 0 — the first improvement over the 2018 bound of Barman et al.

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