Improved Maximin Share Approximations for Chores by Bin Packing

We study fair division of indivisible chores among n agents with additive cost functions using the popular fairness notion of maximin share (MMS). Since MMS allocations do not always exist for more than two agents, the goal has been to improve its approximations and identify interesting special cases where MMS allocations exist. We show the existence of · 1-out-of-9n/11 MMS allocations, which improves the state-of-the-art factor of 1-out-of-3n/4. · MMS allocations for factored instances, which resolves an open question posed by Ebadian et al. (2021). · 15/13-MMS allocations for personalized bivalued instances, improving the state-of-the-art factor of 13/11. We achieve these results by leveraging the HFFD algorithm of Huang and Lu (2021). Our approach also provides polynomial-time algorithms for computing an MMS allocation for factored instances and a 15/13-MMS allocation for personalized bivalued instances.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i13.33518
Primary Topic
Optimization and Packing Problems
Type
article
Field-Weighted Citation Impact
0.00

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article

Improved Maximin Share Approximations for Chores by Bin Packing

Jugal Garg, Erel Segal-Halevi, Xin Huang
Proceedings of the AAAI Conference on Artificial Intelligence
Optimization and Packing Problems
article

Improved Maximin Share Approximations for Chores by Bin Packing

Jugal Garg, Erel Segal-Halevi, Xin Huang
article en

Abstract

We study fair division of indivisible chores among n agents with additive cost functions using the popular fairness notion of maximin share (MMS). Since MMS allocations do not always exist for more than two agents, the goal has been to improve its approximations and identify interesting special cases where MMS allocations exist. We show the existence of · 1-out-of-9n/11 MMS allocations, which improves the state-of-the-art factor of 1-out-of-3n/4. · MMS allocations for factored instances, which resolves an open question posed by Ebadian et al. (2021). · 15/13-MMS allocations for personalized bivalued instances, improving the state-of-the-art factor of 13/11. We achieve these results by leveraging the HFFD algorithm of Huang and Lu (2021). Our approach also provides polynomial-time algorithms for computing an MMS allocation for factored instances and a 15/13-MMS allocation for personalized bivalued instances.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(13)
Kyushu University (JP), University of Illinois Urbana-Champaign (US), Ariel University (IL)
National Science Foundation, Exploratory Research for Advanced Technology
Openalex Percentile: Top 82%
Optimization and Packing Problems
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Improved Maximin Share Approximations for Chores by Bin Packing — Jugal Garg, Erel Segal-Halevi, et al. · Proceedings of the AAAI Conference on Artificial Intelligence (2025) | TGRS Research Map | TGRS