The Optimization Trilemma: Balancing Efficiency, Comfort, and Fairness in Decentralized Multi-agent Coordination
A new preprint introduces the 'Optimization Trilemma' in decentralized multi-agent coordination, focusing on the simultaneous optimization of system-wide efficiency, individual comfort, and fairness. The authors present a novel model that addresses all three objectives without significant increases in communication or computational overhead. Experiments on two real-world datasets demonstrate that the approach achieves fairer outcomes while meeting agent preferences and system goals.
Why it matters: This work advances decentralized AI by enabling fairer and more efficient resource allocation among agents without added complexity.
Full story at: arXiv Computers and Society ↗