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

2026-07-19

AI Summary

This study addresses the critical challenge of jointly optimizing system efficiency, individual comfort, and fairness in cost sharing within fully decentralized multi-agent collaboration—a key factor in preventing incentive misalignment and coordination failure. The work proposes the first decentralized coordination framework that simultaneously handles these three orthogonal objectives through a distributed optimization algorithm coupled with a preference-aware cost reallocation mechanism. Operating without centralized control and under bounded communication and computational overhead, the model achieves balanced multi-objective optimization. Empirical evaluation on two real-world datasets demonstrates the approach’s effectiveness: it maintains high system-wide performance while significantly improving individual satisfaction and fairness in cost distribution.

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.

Problem

• decentralized multi-agent coordination
• fairness
• efficiency
• comfort
• resource allocation

Innovation

• decentralized multi-agent coordination
• fairness
• efficiency-comfort-fairness trilemma
• equitable cost redistribution
• preference-aware optimization