Exploring Relations among Fairness Notions in Discrete Fair Division

TL;DR

This paper systematically analyzes 22 fairness notions in discrete fair division, establishing a hierarchy of implications among them across various settings, and introduces an inference engine tool for automated reasoning.

Contribution

It provides a near-complete hierarchy of fairness notions, many new implications, and a novel inference engine tool for automated analysis in fair division.

Findings

Most implication relationships are characterized, with many new results.

The hierarchy clarifies the relative strength of fairness notions.

An inference engine automates the reasoning process.

Abstract

Fair allocation of indivisible items among agents is a fundamental and extensively studied problem. However, fairness does not have a single universally accepted definition, leading to a variety of competing fairness notions. Some of these notions are considered stronger or more desirable, but they are also more difficult to guarantee. In this work, we examine 22 different notions of fairness and organize them into a hierarchy. Formally, we say that a fairness notion $F_1$ implies another notion $F_2$ if every $F_1$-fair allocation is also $F_2$-fair. We give a near-complete picture of implications among fairness notions: for almost every pair of notions, we either prove an implication or give a counterexample demonstrating that the implication does not hold. Although some of these results are already known, many are new. We examine multiple settings, including the allocation of goods, chores, and mixed manna. We believe this work clarifies the relative strengths and applicability of these notions, providing a foundation for future research in fair division. Moreover, we developed an inference engine to automate part of our work. It is available as a user-friendly web application and may have broader applications beyond fair division.