Fair Division Minimizing Inequality

TL;DR

This paper explores fair division of indivisible goods focusing on minimizing inequality, analyzing axiomatic properties, computational complexity, proposing greedy algorithms, and evaluating their performance through experiments.

Contribution

It introduces tractable greedy online mechanisms for minimizing inequality in fair division, addressing computational intractability of the problem.

Findings

Greedy mechanisms effectively reduce inequality in allocations.

Computational intractability limits exact solutions for inequality minimization.

Experimental results demonstrate practical performance of proposed algorithms.

Abstract

Behavioural economists have shown that people are often averse to inequality and will make choices to avoid unequal outcomes. In this paper, we consider how to allocate indivisible goods fairly so as to minimize inequality. We consider how this interacts with axiomatic properties such as envy-freeness, Pareto efficiency and strategy-proofness. We also consider the computational complexity of computing allocations minimizing inequality. Unfortunately, this is computationally intractable in general so we consider several tractable greedy online mechanisms that minimize inequality. Finally, we run experiments to explore the performance of these methods.