Ordinal Maximin Share Approximation for Goods

Hadi Hosseini, Andrew Searns, Erel Segal-Halevi

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In fair division of indivisible goods, `-out-of-d maximin share (MMS) is the value that an agent can guarantee by partitioning the goods into d bundles and choosing the ` least preferred bundles. Most existing works aim to guarantee to all agents a constant fraction of their 1-out-of-n 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’ ordinal rankings of bundles. We prove the existence of `-out-of-b(` + 12 )nc MMS allocations of goods for any integer ` ≥ 1, and present a polynomial-time algorithm that finds a 1-out-of-d32n e MMS allocation when ` = 1. We further develop an algorithm that provides a weaker ordinal approximation to MMS for any ` > 1.

Original languageEnglish (US)
Pages (from-to)353-391
Number of pages39
JournalJournal of Artificial Intelligence Research
Volume74
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Ordinal Maximin Share Approximation for Goods'. Together they form a unique fingerprint.

Cite this