[Submitted on 29 Jul 2026]

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Abstract:資源分配涉及多個代理群體，在許多應用中出現，包括電子商務推薦系統、住房分配和課程分配，通常被表述為帶有多元限制的優化問題，以確保群體公平性。現有方法通常將這些限制作為硬性條件執行，這會過度限制可行解空間，並經常導致次優分配。
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.

Submission history

From: Keke Huang [view email]
[v1] Wed, 29 Jul 2026 05:25:02 UTC (871 KB)