A General Incentives-Based Framework for Fairness in Multi-agent Resource Allocation

• URL: http://arxiv.org/abs/2510.26740v1
• Date: Thu, 30 Oct 2025 17:37:51 GMT
• Title: A General Incentives-Based Framework for Fairness in Multi-agent Resource Allocation
• Authors: Ashwin Kumar, William Yeoh,
• Abstract summary: We introduce the General Incentives-based Framework for Fairness (GIFF)<n>GIFF is a novel approach for fair multi-agent resource allocation that infers fair decision-making from standard value functions.
• Score: 4.930376365020355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce the General Incentives-based Framework for Fairness (GIFF), a novel approach for fair multi-agent resource allocation that infers fair decision-making from standard value functions. In resource-constrained settings, agents optimizing for efficiency often create inequitable outcomes. Our approach leverages the action-value (Q-)function to balance efficiency and fairness without requiring additional training. Specifically, our method computes a local fairness gain for each action and introduces a counterfactual advantage correction term to discourage over-allocation to already well-off agents. This approach is formalized within a centralized control setting, where an arbitrator uses the GIFF-modified Q-values to solve an allocation problem. Empirical evaluations across diverse domains, including dynamic ridesharing, homelessness prevention, and a complex job allocation task-demonstrate that our framework consistently outperforms strong baselines and can discover far-sighted, equitable policies. The framework's effectiveness is supported by a theoretical foundation; we prove its fairness surrogate is a principled lower bound on the true fairness improvement and that its trade-off parameter offers monotonic tuning. Our findings establish GIFF as a robust and principled framework for leveraging standard reinforcement learning components to achieve more equitable outcomes in complex multi-agent systems.

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