Almost Envy-free Repeated Matching in Two-sided Markets

TL;DR

This paper introduces a polynomial-time algorithm for repeated matchings in two-sided markets that guarantees envy-freeness up to one match while maximizing efficiency, even with dynamic valuations, under symmetric binary preferences.

Contribution

It presents the first analysis of envy-freeness in repeated matchings, providing an efficient algorithm for symmetric binary valuations that balances fairness and efficiency.

Findings

Algorithm guarantees EF1 and maximum weight matching

Fairness and efficiency are achievable for symmetric binary valuations

Impossibility results for asymmetric valuations

Abstract

A two-sided market consists of two sets of agents, each of whom have preferences over the other (Airbnb, Upwork, Lyft, Uber, etc.). We propose and analyze a repeated matching problem, where some set of matches occur on each time step, and our goal is to ensure fairness with respect to the cumulative allocations over an infinite time horizon. Our main result is a polynomial-time algorithm for additive, symmetric (v_i(j) = v_j(i)), and binary (v_i(j) \in \{a,1\}) valuations that both (1) guarantees "envy-freeness up to a single match" (EF1) and (2) selects a maximum weight matching on each time step. Thus for this class of valuations, fairness can be achieved without sacrificing economic efficiency. This result holds even for "dynamic valuations", i.e., valuations that change over time. Although symmetry is a strong assumption, we show that this result cannot be extended to asymmetric binary valuations: (1) and (2) together are impossible even when valuations do not change over time, and for dynamic valuations, even (1) alone is impossible. To our knowledge, this is the first analysis of envy-freeness in a repeated matching setting.