The Optimization Trilemma: Efficiency, Comfort and Fairness in Decentralized Multi-agent Coordination

• URL: http://arxiv.org/abs/2607.17311v1
• Date: Sun, 19 Jul 2026 15:51:33 GMT
• Title: The Optimization Trilemma: Efficiency, Comfort and Fairness in Decentralized Multi-agent Coordination
• Abstract summary: We study the problem of optimizing three objectives: system-wide efficiency, individuals' comfort and (iii) fairness in decentralized multi-agent coordination.<n>We design a novel model to optimize those three objectives, without any substantial increase in communication and computational overhead.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The problem of fair multi-agent coordination in decentralized settings is one of the most pressing challenges for building efficient collaborative systems. Resource allocation is based on optimized collective arrangements accounting for agents' needs. Such coordination should not only be computationally efficient but also account for fairness, i.e., equitable redistribution of costs incurred by all agents. Recent literature has proposed several algorithms that efficiently determine optimal plan combinations balancing system-wide efficiency and individual discomfort of agents in a centralized setting. However, these works do not address equitable resource optimization in fully decentralized scenarios, specifically, the optimized redistribution of discomfort among coordinating agents so that none experiences a discomfort level that could lead to loss of incentive or polarization that can disrupt planned operations. In this work, we study the problem of optimizing three objectives: (i) system-wide efficiency, (ii) individuals' comfort and (iii) fairness (i.e., balancing of incurred discomfort costs) in decentralized multi-agent coordination. We design a novel model to optimize those three orthogonal objectives, without any substantial increase in communication and computational overhead. Through experiments on two real-world datasets, we validate the model and demonstrate that it can achieve fairer optimization outcomes, while satisfying agents' preferences and system goals.

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