TY - GEN
T1 - Fairly Allocating Goods and (Terrible) Chores
AU - Hosseini, Hadi
AU - Mammadov, Aghaheybat
AU - Was, Tomasz
N1 - Publisher Copyright:
© 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
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U2 - 10.24963/ijcai.2023/305
DO - 10.24963/ijcai.2023/305
M3 - Conference contribution
AN - SCOPUS:85164399947
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2738
EP - 2746
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 -