Surprisingly Popular Voting Recovers Rankings, Surprisingly!

Abstract

Classical democratic approaches for aggregating individual votes only work when the opinion of the majority of the crowd is relatively accurate. A clever recent approach, surprisingly popular algorithm, elicits additional information from the individuals, namely their 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 given only partial votes and predictions, and design robust aggregation rules to recover true rankings. In particular, we introduce six elicitation formats with varying information requirements to facilitate ranked versions of surprisingly popular algorithm. Through a crowdsourcing experiment on MTurk, we demonstrate that even a little prediction information helps surprisingly popular voting outperform classical approaches across a diverse set of domains with varying difficulty levels.