Coordination methods use predictions to improve resource sharing efficiency
Learning-Augmented Coordination Mechanisms
Computer Science and Game Theory
Summary
In situations where many people use shared resources, selfish choices can lead to inefficient results. The authors study smart ways to change resource costs based on predicted usage to encourage better overall outcomes. Their method balances between trusting these predictions and guarding against errors to avoid worse cases. They also analyze limits on how well such methods can perform when predictions are slightly wrong.
What this means in practice
- •For network schedulers: Improve traffic routing by adjusting costs based on predicted demand to reduce congestion while preserving worst-case performance guarantees.
- •For distributed system designers: Design protocols that adapt resource costs using demand predictions to enhance efficiency despite decentralized selfish agents.
A theory result. No direct application yet.
Authors
George Christodoulou, Vasilis Christoforidis, Alkmini Sgouritsa, Ioannis Vlachos
Abstract
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.