Fair and Efficient Allocations under Lexicographic Preferences

Hadi Hosseini, Sujoy Sikdar, Rohit Vaish, Lirong Xia

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

12 Scopus citations

Abstract

Envy-freeness up to any good (EFX) provides a strong and intuitive guarantee of fairness in the allocation of indivisible goods. But whether such allocations always exist or whether they can be efficiently computed remains an important open question. We study the existence and computation of EFX in conjunction with various other economic properties under lexicographic preferences–a well-studied preference model in artificial intelligence and economics. In sharp contrast to the known results for additive valuations, we not only prove the existence of EFX and Pareto optimal allocations, but in fact provide an algorithmic characterization of these two properties. We also characterize the mechanisms that are, in addition, strategyproof, non-bossy, and neutral. When the efficiency notion is strengthened to rank-maximality, we obtain non-existence and computational hardness results, and show that tractability can be restored when EFX is relaxed to another well-studied fairness notion called maximin share guarantee (MMS).

Original languageEnglish (US)
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
PublisherAssociation for the Advancement of Artificial Intelligence
Pages5472-5480
Number of pages9
ISBN (Electronic)9781713835974
StatePublished - 2021
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
Duration: Feb 2 2021Feb 9 2021

Publication series

Name35th AAAI Conference on Artificial Intelligence, AAAI 2021
Volume6B

Conference

Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period2/2/212/9/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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