Repeatedly Matching Items to Agents Fairly and Efficiently

TL;DR

This paper studies the complex problem of repeatedly matching items to agents over multiple rounds, addressing fairness and efficiency challenges, and introduces new fairness notions like swap envy-freeness for mixed items.

Contribution

It introduces a novel repeated matching model with dynamic agent-item valuations and proposes new fairness concepts, analyzing their computational feasibility and limitations.

Findings

EF1 can be satisfied under certain conditions for goods

Achieving fairness and efficiency simultaneously is computationally hard

Swap envy-freeness is proposed for mixed items

Abstract

We consider a novel setting where a set of items are matched to the same set of agents repeatedly over multiple rounds. Each agent gets exactly one item per round, which brings interesting challenges to finding efficient and/or fair {\em repeated matchings}. A particular feature of our model is that the value of an agent for an item in some round depends on the number of rounds in which the item has been used by the agent in the past. We present a set of positive and negative results about the efficiency and fairness of repeated matchings. For example, when items are goods, a variation of the well-studied fairness notion of envy-freeness up to one good (EF1) can be satisfied under certain conditions. Furthermore, it is intractable to achieve fairness and (approximate) efficiency simultaneously, even though they are achievable separately. For mixed items, which can be goods for some agents and chores for others, we propose and study a new notion of fairness that we call {\em swap envy-freeness} (swapEF).