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The Leximin Approach for a Sequence of Collective Decisions

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arxiv 2305.18024 v1 pith:SZAW75KT submitted 2023-05-29 cs.GT cs.AI

classification cs.GTcs.AI
keywords fairnessleximinproportionalityconsidermechanismrelaxationsagentsdecisions
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In many situations, several agents need to make a sequence of decisions. For example, a group of workers that needs to decide where their weekly meeting should take place. In such situations, a decision-making mechanism must consider fairness notions. In this paper, we analyze the fairness of three known mechanisms: round-robin, maximum Nash welfare, and leximin. We consider both offline and online settings, and concentrate on the fairness notion of proportionality and its relaxations. Specifically, in the offline setting, we show that the three mechanisms fail to find a proportional or approximate-proportional outcome, even if such an outcome exists. We thus introduce a new fairness property that captures this requirement, and show that a variant of the leximin mechanism satisfies the new fairness property. In the online setting, we show that it is impossible to guarantee proportionality or its relaxations. We thus consider a natural restriction on the agents' preferences, and show that the leximin mechanism guarantees the best possible additive approximation to proportionality and satisfies all the relaxations of proportionality.

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

  1. Perpetually Fair Assignments Via Balanced Sequences of Permutations

    math.CO 2026-02 conditional novelty 7.0 of 10

    Balanced permutation sequences guarantee ordinal PROP1 fairness after every day where they exist, but they exist only for n≤11 (none beyond 61), and full PROP2 existence remains open.

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