2024
IJCAI
IJCAI 2024
Ordinal Maximin Guarantees for Group Fair Division
Abstract
We investigate fairness in the allocation of indivisible items among groups of agents using the notion of maximin share (MMS). While previous work has shown that no nontrivial multiplicative MMS approximation can be guaranteed in this setting for general group sizes, we demonstrate that ordinal relaxations are much more useful. For example, we show that if n agents are distributed equally across g groups, there exists a 1-out-of-k MMS allocation for k = O(g log(n/g)), while if all but a constant number of agents are in the same group, we obtain k = O(log n / log log n). We also establish the tightness of these bounds and provide non-asymptotic results for the case of two groups.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio