TY - GEN
T1 - New Fairness Concepts for Allocating Indivisible Items
AU - Caragiannis, Ioannis
AU - Garg, Jugal
AU - Rathi, Nidhi
AU - Sharma, Eklavya
AU - Varricchio, Giovanna
N1 - Publisher Copyright:
© 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2023
Y1 - 2023
N2 - For the fundamental problem of fairly dividing a set of indivisible items among agents, envy-freeness up to any item (EFX) and maximin fairness (MMS) are arguably the most compelling fairness concepts proposed until now. Unfortunately, despite significant efforts over the past few years, whether EFX allocations always exist is still an enigmatic open problem, let alone their efficient computation. Furthermore, today we know that MMS allocations are not always guaranteed to exist. These facts weaken the usefulness of both EFX and MMS, albeit their appealing conceptual characteristics. We propose two alternative fairness concepts-called epistemic EFX (EEFX) and minimum EFX share fairness (MXS)-inspired by EFX and MMS. For both, we explore their relationships to well-studied fairness notions and, more importantly, prove that EEFX and MXS allocations always exist and can be computed efficiently for additive valuations. Our results justify that the new fairness concepts can be excellent alternatives to EFX and MMS.
AB - For the fundamental problem of fairly dividing a set of indivisible items among agents, envy-freeness up to any item (EFX) and maximin fairness (MMS) are arguably the most compelling fairness concepts proposed until now. Unfortunately, despite significant efforts over the past few years, whether EFX allocations always exist is still an enigmatic open problem, let alone their efficient computation. Furthermore, today we know that MMS allocations are not always guaranteed to exist. These facts weaken the usefulness of both EFX and MMS, albeit their appealing conceptual characteristics. We propose two alternative fairness concepts-called epistemic EFX (EEFX) and minimum EFX share fairness (MXS)-inspired by EFX and MMS. For both, we explore their relationships to well-studied fairness notions and, more importantly, prove that EEFX and MXS allocations always exist and can be computed efficiently for additive valuations. Our results justify that the new fairness concepts can be excellent alternatives to EFX and MMS.
UR - http://www.scopus.com/inward/record.url?scp=85170370416&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85170370416&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85170370416
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2554
EP - 2562
BT - Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
A2 - Elkind, Edith
PB - International Joint Conferences on Artificial Intelligence
T2 - 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
Y2 - 19 August 2023 through 25 August 2023
ER -