Approximate-EFX Allocations with Ordinal and Limited Cardinal Information

TL;DR

This paper explores algorithms for fair division that approximate EFX fairness using limited ordinal and cardinal information, establishing tradeoffs between fairness quality and information access.

Contribution

It introduces near-optimal algorithms for approximate EFX allocations under limited information, with specific improvements for special cases like few agents or binary valuations.

Findings

Tradeoffs between EFX approximation factor and number of queries.

Algorithms achieve near-optimal fairness with limited valuation data.

Improved results for cases with few agents or binary valuations.

Abstract

We study a discrete fair division problem where $n$ agents have additive valuation functions over a set of $m$ goods. We focus on the well-known $\alpha$-EFX fairness criterion, according to which the envy of an agent for another agent is bounded multiplicatively by $\alpha$, after the removal of any good from the envied agent's bundle. The vast majority of the literature has studied $\alpha$-EFX allocations under the assumption that full knowledge of the valuation functions of the agents is available. Motivated by the established literature on the distortion in social choice, we instead consider $\alpha$-EFX algorithms that operate under limited information on these functions. In particular, we assume that the algorithm has access to the ordinal preference rankings, and is allowed to make a small number of queries to obtain further access to the underlying values of the agents for the goods. We show (near-optimal) tradeoffs between the values of $\alpha$ and the number of queries required to achieve those, with a particular focus on constant EFX approximations. We also consider two interesting special cases, namely instances with a constant number of agents, or with two possible values, and provide improved positive results.