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

arXiv: https://arxiv.org/abs/2607.17311 · HTML: https://mlsanity.com/paper/2607.17311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 19 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Learning Fairness in Multi-Agent Systems](https://mlsanity.com/paper/1910.14472/) (2019)
- [Fair Algorithms for Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2007.06699/) (2020)
- [Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2012.09421/) (2020)
- [Socio-technical Smart Grid Optimization via Decentralized Charge Control of Electric Vehicles](https://mlsanity.com/paper/1701.06811/) (2017)
- [DECAF: Learning to be Fair in Multi-agent Resource Allocation](https://mlsanity.com/paper/2502.04281/) (2025)
- [Self-regulating Supply-Demand Systems](https://mlsanity.com/paper/1701.04679/) (2017)
- [Appliance-level Flexible Scheduling for Socio-technical Smart Grid Optimisation](https://mlsanity.com/paper/1907.03470/) (2019)
- [Cooperative Flexibility Exchange: Fair and Comfort-Aware Decentralized Resource Allocation](https://mlsanity.com/paper/2510.04192/) (2025)
- [Multi-Objective Multi-Agent Bandits: From Learning Efficiency to Fairness Optimization](https://mlsanity.com/paper/2605.06864/) (2026)

## Abstract (arXiv)

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.