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

2025-10-30

AI Summary

In resource-constrained multi-agent systems, balancing efficiency and fairness remains challenging due to inherent trade-offs in resource allocation. Method: This paper proposes a general incentive-based fairification framework that augments the standard value function with a local fairness gain term and a counterfactual advantage correction term—requiring no additional training—thereby mitigating over-allocation to advantaged agents. A centralized arbitration mechanism and counterfactual Q-value correction enable policy optimization within a reinforcement learning framework. Contribution/Results: We theoretically establish a lower bound on fairness improvement and prove monotonic adjustability of the fairness-efficiency trade-off parameter. Empirical evaluation across dynamic ride-pooling, homelessness intervention, and complex task scheduling demonstrates significant improvements over strong baselines, achieving superior long-term utility while ensuring equitable resource distribution.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Fair Division
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Achieving fairness in multi-agent resource allocation under efficiency constraints
• Balancing efficiency and fairness using standard value functions without retraining
• Preventing over-allocation to well-off agents through counterfactual advantage correction

Innovation

• Infers fair decisions from standard value functions
• Uses fairness gain and advantage correction for balance
• Modifies Q-values for equitable multi-agent allocation