Surprisingly Popular Voting Recovers Rankings, Surprisingly!

TL;DR

This paper extends surprisingly popular voting to ranked voting scenarios, demonstrating that incorporating partial predictions improves the accuracy of recovering true rankings over classical methods.

Contribution

It introduces practical techniques for adapting surprisingly popular voting to ranked preferences and designs robust aggregation rules, enhancing ranking accuracy.

Findings

Partial prediction information improves ranking recovery.

Extended methods outperform classical voting approaches.

Robust aggregation rules increase practical applicability.

Abstract

The wisdom of the crowd has long become the de facto approach for eliciting information from individuals or experts in order to predict the ground truth. However, classical democratic approaches for aggregating individual \emph{votes} only work when the opinion of the majority of the crowd is relatively accurate. A clever recent approach, \emph{surprisingly popular voting}, elicits additional information from the individuals, namely their \emph{prediction} of other individuals' votes, and provably recovers the ground truth even when experts are in minority. This approach works well when the goal is to pick the correct option from a small list, but when the goal is to recover a true ranking of the alternatives, a direct application of the approach requires eliciting too much information. We explore practical techniques for extending the surprisingly popular algorithm to ranked voting by partial votes and predictions and designing robust aggregation rules. We experimentally demonstrate that even a little prediction information helps surprisingly popular voting outperform classical approaches.