Fairly Allocating Goods and (Terrible) Chores

Hadi Hosseini, Aghaheybat Mammadov, Tomasz Was

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

We study the fair allocation of mixtures of indivisible goods and chores under lexicographic preferences-a subdomain of additive preferences. A prominent fairness notion for allocating indivisible items is envy-freeness up to any item (EFX). Yet, its existence and computation has remained a notable open problem. By identifying a class of instances with terrible chores, we show that determining the existence of an EFX allocation is NP-complete. This result immediately implies the intractability of EFX under additive preferences. Nonetheless, we propose a natural subclass of lexicographic preferences for which an EFX and Pareto optimal (PO) allocation is guaranteed to exist and can be computed efficiently. Focusing on two weaker fairness notions, we investigate finding EF1 and PO allocations for special instances with terrible chores and show that MMS and PO allocations can be computed efficiently for any mixed instance with lexicographic preferences.

Original languageEnglish (US)
Title of host publicationProceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
EditorsEdith Elkind
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2738-2746
Number of pages9
ISBN (Electronic)9781956792034
DOIs
StatePublished - 2023
Event32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 - Macao, China
Duration: Aug 19 2023Aug 25 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
Period8/19/238/25/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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