arXiv:2610.36508 · v1
Learning-Augmented Coordination Mechanisms
摘要
The inefficiency of decentralized resource allocation is classically measured by the Price of Anarchy (PoA) and the Price of Stability (PoS). The most widely studied and foundational class of these models are atomic and non-atomic congestion games, for which a robust theory quantifying the inefficiency of equilibria is well-established. To mitigate the effects of selfish behavior, we employ Coordination Mechanisms, which modify resource costs to incentivize socially improved equilibria. However, standard Coordination Mechanisms assume unknown, adversarial demand, restricting positive results mainly to specialized networks such as parallel links. We address this limitation by studying learning-augmented coordination mechanisms, where the mechanism is endowed with a potentially inaccurate prediction of the demand. We study general atomic and non-atomic congestion games with polynomial latency functions of degree $d$. Our main positive result is a learning-augmented coordination mechanism tuned by a confidence parameter $β$ which achieves a "best of both worlds" type of result: approximately optimal performance for an accurate prediction without sacrificing the worst-case guarantee of a good equilibrium (PoS). We further establish bounds on PoA-Robustness (worst equilibrium performance under cost modification). Our construction relies on transforming approximate equilibria of the original game into exact equilibria of the modified game using an approximate potential function. We complement our positive results with impossibility results. We first show that requiring all equilibria to have optimal social cost under an accurate prediction precludes bounded robustness. We also show a strong error tolerance limit in non-atomic games: even infinitesimal prediction errors can cause the performance of a mechanism with optimal consistency to revert to the original PoA.
作者备注：This paper has been accepted to the 40th Conference on Neural Information Processing Systems (NeurIPS 2026). Abstract abridged due to arXiv length limit