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

TL;DR

The paper presents GIFF, a novel framework that uses standard value functions to promote fairness in multi-agent resource allocation without extra training, balancing efficiency and equity.

Contribution

It introduces a general incentives-based approach that infers fairness from value functions and formalizes it within a centralized control setting, with theoretical guarantees.

Findings

Outperforms strong baselines across diverse domains.

Discovers far-sighted, equitable policies.

Provides a theoretical lower bound on fairness improvement.

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.