On Fair Division under Heterogeneous Matroid Constraints

TL;DR

This paper investigates fair division of indivisible goods under matroid constraints, providing algorithms and results for EF1 allocations among heterogeneous agents with various valuation and feasibility types.

Contribution

It introduces new polynomial-time algorithms for EF1 allocations in heterogeneous matroid and valuation settings, advancing understanding of fair division under complex constraints.

Findings

Polynomial-time algorithms for EF1 allocations with heterogeneous partition matroids and binary valuations.

EF1 allocations exist for 2 agents with heterogeneous partition matroids and additive valuations.

EF1 allocations are achievable for up to 3 agents with binary valuations and identical base-orderable matroids.

Abstract

We study fair allocation of indivisible goods among additive agents with feasibility constraints. In these settings, every agent is restricted to get a bundle among a specified set of feasible bundles. Such scenarios have been of great interest to the AI community due to their applicability to real-world problems. Following some impossibility results, we restrict attention to matroid feasibility constraints that capture natural scenarios, such as the allocation of shifts to medical doctors, and the allocation of conference papers to referees. We focus on the common fairness notion of envy-freeness up to one good (EF1). Previous algorithms for finding EF1 allocations are either restricted to agents with identical feasibility constraints, or allow free disposal of items. An open problem is the existence of EF1 complete allocations among heterogeneous agents, where the heterogeneity is both in the agents' feasibility constraints and in their valuations. In this work, we make progress on this problem by providing positive and negative results for different matroid and valuation types. Among other results, we devise polynomial-time algorithms for finding EF1 allocations in the following settings: (i) $n$ agents with heterogeneous partition matroids and heterogeneous binary valuations, (ii) 2 agents with heterogeneous partition matroids and heterogeneous additive valuations, and (iii) at most 3 agents with heterogeneous binary valuations and identical base-orderable matroid constraints.