Improved Maximin Share Approximations for Chores by Bin Packing

TL;DR

This paper advances fair division of chores by improving approximation guarantees for maximin share allocations, especially for special cases like factored and personalized bivalued instances, using novel algorithmic techniques.

Contribution

It introduces improved approximation ratios for MMS allocations in chores division, including for factored and personalized bivalued instances, and provides polynomial-time algorithms for these cases.

Findings

Established 1-out-of-9/11 n MMS allocations, improving previous bounds.

Proved existence of MMS allocations for factored instances, answering an open question.

Achieved 15/13-MMS allocations for personalized bivalued instances.

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 exists. We show the existence of 1) 1-out-of-$\lfloor \frac{9}{11}n\rfloor$ MMS allocations, which improves the state-of-the-art factor of 1-out-of-$\lfloor \frac{3}{4}n\rfloor$. 2) MMS allocations for factored instances, which resolves an open question posed by Ebadian et al. (2021). 3) $15/13$-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.