Fair-GNE : Generalized Nash Equilibrium-Seeking Fairness in Multiagent Healthcare Automation
This addresses fairness enforcement in multi-agent healthcare systems where agents share resources, though it appears to be an incremental improvement over existing MARL fairness methods.
The paper tackles the problem of ensuring fair workload allocation among multiple agents in healthcare automation by proposing Fair-GNE, a generalized Nash equilibrium-seeking framework that enforces fairness through adaptive constraints rather than post-hoc reward shaping. The approach achieves significantly better workload balance (0.89 vs. 0.33 JFI, p<0.01) while maintaining 86% task success in a resuscitation simulator.
Enforcing a fair workload allocation among multiple agents tasked to achieve an objective in learning enabled demand side healthcare worker settings is crucial for consistent and reliable performance at runtime. Existing multi-agent reinforcement learning (MARL) approaches steer fairness by shaping reward through post hoc orchestrations, leaving no certifiable self-enforceable fairness that is immutable by individual agents at runtime. Contextualized within a setting where each agent shares resources with others, we address this shortcoming with a learning enabled optimization scheme among self-interested decision makers whose individual actions affect those of other agents. This extends the problem to a generalized Nash equilibrium (GNE) game-theoretic framework where we steer group policy to a safe and locally efficient equilibrium, so that no agent can improve its utility function by unilaterally changing its decisions. Fair-GNE models MARL as a constrained generalized Nash equilibrium-seeking (GNE) game, prescribing an ideal equitable collective equilibrium within the problem's natural fabric. Our hypothesis is rigorously evaluated in our custom-designed high-fidelity resuscitation simulator. Across all our numerical experiments, Fair-GNE achieves significant improvement in workload balance over fixed-penalty baselines (0.89 vs.\ 0.33 JFI, $p < 0.01$) while maintaining 86\% task success, demonstrating statistically significant fairness gains through adaptive constraint enforcement. Our results communicate our formulations, evaluation metrics, and equilibrium-seeking innovations in large multi-agent learning-based healthcare systems with clarity and principled fairness enforcement.