English

Ordinal Maximin Share Approximation for Goods

Computer Science and Game Theory 2022-05-30 v3

Abstract

In fair division of indivisible goods, \ell-out-of-dd maximin share (MMS) is the value that an agent can guarantee by partitioning the goods into dd bundles and choosing the \ell least preferred bundles. Most existing works aim to guarantee to all agents a constant fraction of their 1-out-of-nn MMS. But this guarantee is sensitive to small perturbation in agents' cardinal valuations. We consider a more robust approximation notion, which depends only on the agents' \emph{ordinal} rankings of bundles. We prove the existence of \ell-out-of-(+12)n\lfloor(\ell+\frac{1}{2})n\rfloor MMS allocations of goods for any integer 1\ell\geq 1, and present a polynomial-time algorithm that finds a 11-out-of-3n2\lceil\frac{3n}{2}\rceil MMS allocation when =1\ell = 1. We further develop an algorithm that provides a weaker ordinal approximation to MMS for any >1\ell > 1.

Keywords

Cite

@article{arxiv.2109.01925,
  title  = {Ordinal Maximin Share Approximation for Goods},
  author = {Hadi Hosseini and Andrew Searns and Erel Segal-Halevi},
  journal= {arXiv preprint arXiv:2109.01925},
  year   = {2022}
}

Comments

Accepted to JAIR

R2 v1 2026-06-24T05:41:07.088Z