Rawlsian equity: a new notion of fairness for the assignment problem

2026-07-31

AI Summary

This study addresses the fair allocation of indivisible goods in the absence of item-specific entitlements, proposing a Rawlsian fairness criterion grounded in Rawls’s theory of justice: if one agent prefers an item more than another does, the latter must receive a strictly better item. The authors transform this allocation problem into a stable matching problem by constructing an auxiliary market and solve it using the agent-proposing deferred acceptance algorithm. Their key contribution lies in formally introducing Rawlsian fairness into resource allocation for the first time, establishing the equivalence between the set of Rawlsian fair allocations and the set of stable matchings in the auxiliary market, which enables efficient computation of agent-optimal outcomes. However, the mechanism is not strategy-proof in general.

Abstract

We introduce Rawlsian equity, a notion of fairness for allocating indivisible objects among agents without object-specific entitlements. Rawlsian equity requires that an object not be assigned to an agent who ranks it more highly than another agent unless the latter receives a more preferred object. We show that the set of Rawlsian equitable allocations coincides with the set of stable allocations in an auxiliary market where object priorities depend on agents' preference reports. The agent-proposing deferred acceptance algorithm computes the agent-optimal element of this set, but no Rawlsian equitable rule is strategy-proof in general.

Problem

• Rawlsian equity
• assignment problem
• fairness
• indivisible objects
• stable allocations

Innovation

• Rawlsian equity
• indivisible object allocation
• stable matching
• deferred acceptance algorithm
• strategy-proofness