Fair division of mixed divisible and indivisible goods

Xiaohui Bei, Zihao Li, Jinyan Liu, Shengxin Liu*, Xinhang Lu

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

We study the problem of fair division when the resources contain both divisible and indivisible goods. Classic fairness notions such as envy-freeness (EF) and envy-freeness up to one good (EF1) cannot be directly applied to the mixed goods setting. In this work, we propose a new fairness notion envy-freeness for mixed goods (EFM), which is a direct generalization of both EF and EF1 to the mixed goods setting. We prove that an EFM allocation always exists for any number of agents. We also propose efficient algorithms to compute an EFM allocation for two agents and for n agents with piecewise linear valuations over the divisible goods. Finally, we relax the envy-free requirement, instead asking for ε-envy-freeness for mixed goods (ε-EFM), and present an algorithm that finds an ε-EFM allocation in time polynomial in the number of agents, the number of indivisible goods, and 1/ε.

Original languageEnglish
Title of host publicationAAAI 2020 - 34th AAAI Conference on Artificial Intelligence
PublisherAAAI press
Pages1814-1821
Number of pages8
ISBN (Electronic)9781577358350
Publication statusPublished - 2020
Externally publishedYes
Event34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States
Duration: 7 Feb 202012 Feb 2020

Publication series

NameAAAI 2020 - 34th AAAI Conference on Artificial Intelligence

Conference

Conference34th AAAI Conference on Artificial Intelligence, AAAI 2020
Country/TerritoryUnited States
CityNew York
Period7/02/2012/02/20

Fingerprint

Dive into the research topics of 'Fair division of mixed divisible and indivisible goods'. Together they form a unique fingerprint.

Cite this

Bei, X., Li, Z., Liu, J., Liu, S., & Lu, X. (2020). Fair division of mixed divisible and indivisible goods. In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence (pp. 1814-1821). (AAAI 2020 - 34th AAAI Conference on Artificial Intelligence). AAAI press.