Yankee Swap: a Fast and Simple Fair Allocation Mechanism for Matroid Rank Valuations

TL;DR

This paper introduces a simple, scalable algorithm for fair allocation of indivisible goods under matroid rank valuations, improving efficiency and understandability over existing methods.

Contribution

A new, easy-to-understand Yankee Swap-based algorithm that computes Lorenz dominating allocations more efficiently than prior complex algorithms.

Findings

Algorithm is faster than existing methods.

Approach is simple and scalable.

Produces fair and efficient allocations.

Abstract

We study fair allocation of indivisible goods when agents have matroid rank valuations. Our main contribution is a simple algorithm based on the colloquial Yankee Swap procedure that computes provably fair and efficient Lorenz dominating allocations. While there exist polynomial time algorithms to compute such allocations, our proposed method improves on them in two ways. (a) Our approach is easy to understand and does not use complex matroid optimization algorithms as subroutines. (b) Our approach is scalable; it is provably faster than all known algorithms to compute Lorenz dominating allocations. These two properties are key to the adoption of algorithms in any real fair allocation setting; our contribution brings us one step closer to this goal.