Tight Asymptotic Bounds for Fair Division With Externalities

TL;DR

This paper establishes tight bounds on the number of items needed to achieve approximate envy-freeness in fair division with externalities, showing EF-$k$ can be achieved with $O( ext{sqrt}(n))$ items, but not EF1.

Contribution

It provides the first tight asymptotic bounds for envy-free allocations with externalities, resolving key open questions in the field.

Findings

EF-$k$ allocations exist with $O( ext{sqrt}(n))$ items

Matching lower bounds show EF1 is impossible with externalities

Polynomial-time algorithms can find these allocations

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 $O(\sqrt{n})$ items always exists and can be found in polynomial time, and we prove a matching $\Omega(\sqrt{n})$ lower bound showing that this result is tight even for binary valuations, which rules out the existence of EF1 allocations when agents have externalities.