Improving the Price of Anarchy via Predictions in Parallel-Link Networks

We study non-atomic congestion games on parallel-link networks with polynomial latencies. We investigate the power of machine-learned predictions in the design of coordination mechanisms aimed at minimizing the impact of selfishness. Our main results demonstrate that enhancing coordination mechanisms with simple advice on the input rate can optimize the social cost whenever the advice is accurate (consistency ), while only incurring minimal losses even when the predictions are arbitrarily inaccurate (bounded robustness ). Moreover, we provide a full characterization of consistent mechanisms, which holds for all monotone cost functions, and show that our proposed mechanism is optimal with respect to robustness. We further explore the notion of error-tolerance within this context, i.e., we provide an approximation guarantee that degrades smoothly as a function of the prediction error, up to a predetermined threshold, while achieving a bounded robustness.