Parameterized Fair Resource Allocation under Diversity Constraints

2026-07-29

AI Summary

This work addresses the limitations of traditional multi-group resource allocation methods, which impose diversity constraints as hard requirements, thereby overly restricting the solution space and compromising efficiency. To overcome this, the authors propose the PRA framework, which introduces risk-aversion principles into fair allocation for the first time, softening diversity constraints through an adjustable inequality-aversion parameter to enable a flexible trade-off between fairness and efficiency. They further extend PRA into an adaptive variant, APRA, capable of accommodating arbitrary fairness metrics and complex real-world constraints. Evaluated across three real-world applications—e-commerce recommendation, housing allocation, and course scheduling—PRA and APRA consistently outperform existing baselines, achieving state-of-the-art performance and robustness.

Abstract

Resource allocation across multiple agent groups arises in many applications including e-commerce recommendation systems, housing assignment, and course allocation, and is commonly formulated as an optimization problem with diversity constraints to ensure group fairness. Existing approaches typically enforce these constraints as hard conditions, which overly restrict the feasible solution space and often lead to suboptimal allocations. In this paper, we propose PRA, a parameterized framework for fair resource allocation under diversity constraints. Inspired by the use of risk-aversion parameters in economic models, PRA introduces a set of controllable inequality-aversion parameters to softly regulate group-level diversity, thereby enabling flexible trade-offs between fairness and allocation efficiency. With appropriately calibrated parameters, PRA yields fairness-optimal assignments that comply with the specified diversity constraints. To accommodate additional application-specific constraints, we further extend the framework to an adaptive variant, APRA. We establish that the optimality of both PRA and APRA holds regardless of the chosen fairness metric and the nature of the additional constraints, underscoring the generality and robustness of our approach. Extensive experiments on three real-world applications demonstrate that our proposed framework consistently outperforms existing baselines in both effectiveness and robustness.

Problem

• fair resource allocation
• diversity constraints
• group fairness
• optimization
• inequality-aversion

Innovation

• parameterized fairness
• diversity constraints
• inequality-aversion parameters
• soft constraints
• adaptive resource allocation