Ordinal Maximin Share Approximation for Goods (Extended Abstract)

  • Hadi Hosseini
  • , Andrew Searns
  • , Erel Segal-Halevi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In fair division of indivisible goods, ℓ-out-of-d maximin share (MMS) is the value that an agent can guarantee by partitioning the goods into d bundles and choosing the ℓ 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 ℓ-out-of-(Equation presented) MMS allocations of goods for any integer ℓ ≥ 1, and present a polynomial-time algorithm that finds a 1-out-of-(Equation presented) MMS allocation when ℓ = 1. We further develop an algorithm that provides a weaker ordinal approximation to MMS for any ℓ > 1.

Original languageEnglish
Title of host publicationProceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
EditorsEdith Elkind
PublisherInternational Joint Conferences on Artificial Intelligence
Pages6894-6899
Number of pages6
ISBN (Electronic)9781956792034
DOIs
StatePublished - 2023
Externally publishedYes
Event32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 - Macao, China
Duration: 19 Aug 202325 Aug 2023

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2023-August
ISSN (Print)1045-0823

Conference

Conference32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
Country/TerritoryChina
CityMacao
Period19/08/2325/08/23

Bibliographical note

Publisher Copyright:
© 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.

Funding

Hadi Hosseini acknowledges support from NSF IIS grants #2144413, #2052488 and #2107173. Erel Segal-Halevi is supported by the Israel Science Foundation (grant no. 712/20). We are grateful to Thomas Rothvoss, Ariel Procaccia, Joshua Lin, Inuyasha Yagami, Chandra Chekuri, Neal Young, and the anonymous referees of EC 2021 and JAIR for their valuable feedback.

FundersFunder number
NSF IIS2107173, 2052488, 2144413
Israel Science Foundation712/20

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