Optimal Efficiency-Envy Trade-Off via Optimal Transport

TL;DR

This paper introduces a novel optimal transport-based method for fair item allocation that balances efficiency and envy, with proven statistical guarantees and practical scalability for large problems.

Contribution

It formulates a semi-discrete optimal transport approach to trade off efficiency and envy, providing a simple geometric interpretation and statistical bounds.

Findings

Efficient algorithm for optimal trade-off between envy and efficiency.

Polynomial sample complexity for approximating the optimal solution.

Successful numerical experiments on realistic data simulations.

Abstract

We consider the problem of allocating a distribution of items to $n$ recipients where each recipient has to be allocated a fixed, prespecified fraction of all items, while ensuring that each recipient does not experience too much envy. We show that this problem can be formulated as a variant of the semi-discrete optimal transport (OT) problem, whose solution structure in this case has a concise representation and a simple geometric interpretation. Unlike existing literature that treats envy-freeness as a hard constraint, our formulation allows us to \emph{optimally} trade off efficiency and envy continuously. Additionally, we study the statistical properties of the space of our OT based allocation policies by showing a polynomial bound on the number of samples needed to approximate the optimal solution from samples. Our approach is suitable for large-scale fair allocation problems such as the blood donation matching problem, and we show numerically that it performs well on a prior realistic data simulator.