Utilitarian Distortion with Predictions

TL;DR

This paper investigates how learning-augmented mechanisms can improve social choice outcomes by effectively balancing reliance on predictions with worst-case guarantees in voting and matching problems.

Contribution

It introduces a framework analyzing the tradeoffs between consistency and robustness in social choice mechanisms using predictions, providing tight bounds for different information levels.

Findings

Established tight tradeoffs between consistency and robustness.

Analyzed mechanisms for single-winner voting and one-sided matching.

Provided bounds depending on the accuracy of predictions.

Abstract

We study the utilitarian distortion of social choice mechanisms under the recently proposed learning-augmented framework where some (possibly unreliable) predicted information about the preferences of the agents is given as input. In particular, we consider two fundamental social choice problems: single-winner voting and one-sided matching. In these settings, the ordinal preferences of the agents over the alternatives (either candidates or items) is known, and some prediction about their underlying cardinal values is also provided. The goal is to leverage the prediction to achieve improved distortion guarantees when it is accurate, while simultaneously still achieving reasonable worst-case bounds when it is not. This leads to the notions of consistency and robustness, and the quest to achieve the best possible tradeoffs between the two. We show tight tradeoffs between the consistency and robustness of ordinal mechanisms for single-winner voting and one-sided matching, for different levels of information provided by as prediction.