Tight Asymptotic Bounds for Fair Division with Externalities
Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 3359-3367.
https://doi.org/10.24963/ijcai.2026/373
Keywords
- Game Theory and Economic Paradigms: Fair division
Abstract
We study the problem of allocating a set of indivisible items among agents whose preferences include externalities. Unlike the standard fair division model, agents may derive positive or negative utility not only from items allocated directly to them, but also from items allocated to other agents. Since exact envy-freeness cannot be guaranteed, prior work has focused on its relaxations. However, two central questions remained open: does there always exist an allocation that is envy-free up to one item (EF1), and if not, what is the optimal relaxation EF-k that can always be attained?
We settle both questions by deriving tight asymptotic bounds on the number of items sufficient to eliminate envy. We show that for any instance with n agents, an allocation that is envy-free up to O(√n) items always exists and can be found in polynomial time. Additionally, via a reduction from fair division with externalities to discrepancy theory combined with recent discrepancy lower bounds, we prove a matching Ω(√n) lower bound showing that this result is tight even when the valuations are binary and satisfy the no-chores condition, which refutes a conjecture from previous work and resolves the main open question in the area, ruling out the existence of EF1 allocations when agents have externalities.
@inproceedings{ijcai2026p373,
title = {Tight Asymptotic Bounds for Fair Division with Externalities},
author = {Connor, Frank and Dupré la Tour, Max and Narayan, Vishnu V. and Schierreich, Šimon},
booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-26}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Diego Calvanese},
pages = {3359--3367},
year = {2026},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2026/373},
url = {https://doi.org/10.24963/ijcai.2026/373},
}
TY - CPAPER TI - Tight Asymptotic Bounds for Fair Division with Externalities AU - Connor, Frank AU - Dupré la Tour, Max AU - Narayan, Vishnu V. AU - Schierreich, Šimon T2 - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2026 SP - 3359 EP - 3367 DO - 10.24963/ijcai.2026/373 UR - https://doi.org/10.24963/ijcai.2026/373 ER -Copied
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