New Metrics Reveal Hidden Temporal Unfairness in Multi-Agent Coordination
A new study introduces Alternation (ALT) metrics to assess temporal fairness in repeated multi-agent interactions, showing that standard fairness measures can overlook persistent monopolization of shared resources. In experiments with Q-learning agents in the Honey-Jar Game, agents achieved high traditional fairness scores but performed significantly worse than random baselines on ALT metrics, indicating poor temporal coordination. The framework enables diagnosis of whether temporal fairness arises naturally in decentralized adaptive systems.
Why it matters: This work highlights a significant limitation in widely used fairness metrics for multi-agent systems, with implications for real-world applications where resource monopolization is a concern.
Full story at: arXiv Multiagent Systems ↗