Mechanism Design with Predictions

TL;DR

This paper explores the use of imperfect predictions to enhance mechanism design, achieving better performance with accurate predictions while maintaining worst-case guarantees, and analyzing the tradeoffs among consistency, robustness, and error tolerance.

Contribution

It systematically studies mechanism design with predictions, introducing the concepts of consistency, robustness, and error tolerance, and analyzing their inherent tradeoffs.

Findings

Mechanisms can outperform traditional designs with accurate predictions.

Worst-case guarantees are maintained even with imprecise predictions.

A fundamental tradeoff exists among consistency, robustness, and error tolerance.

Abstract

Improving algorithms via predictions is a very active research topic in recent years. This paper initiates the systematic study of mechanism design in this model. In a number of well-studied mechanism design settings, we make use of imperfect predictions to design mechanisms that perform much better than traditional mechanisms if the predictions are accurate (consistency), while always retaining worst-case guarantees even with very imprecise predictions (robustness). Furthermore, we refer to the largest prediction error sufficient to give a good performance as the error tolerance of a mechanism, and observe that an intrinsic tradeoff among consistency, robustness and error tolerance is common for mechanism design with predictions.