# Surprisingly Popular Voting Recovers Rankings, Surprisingly!

arXiv: https://arxiv.org/abs/2105.09386 · HTML: https://mlsanity.com/paper/2105.09386/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 9 of 9)

- [Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing](https://mlsanity.com/paper/1406.3824/) (2014)
- [Crowdsourced Judgement Elicitation with Endogenous Proficiency](https://mlsanity.com/paper/1303.0799/) (2013)
- [Informed Truthfulness in Multi-Task Peer Prediction](https://mlsanity.com/paper/1603.03151/) (2016)
- [Dominantly Truthful Multi-task Peer Prediction with a Constant Number of Tasks](https://mlsanity.com/paper/1911.00272/) (2019)
- [The Wisdom of the Crowd and Higher-Order Beliefs](https://mlsanity.com/paper/2102.02666/) (2021)
- [Forecast Aggregation via Peer Prediction](https://mlsanity.com/paper/1910.03779/) (2019)
- [Peeking Behind the Ordinal Curtain: Improving Distortion via Cardinal Queries](https://mlsanity.com/paper/1907.08165/) (2019)
- [Communication, Distortion, and Randomness in Metric Voting](https://mlsanity.com/paper/1911.08129/) (2019)
- [Awareness of Voter Passion Greatly Improves the Distortion of Metric Social Choice](https://mlsanity.com/paper/1906.10562/) (2019)

## Built on by (most cited first, 4 of 4)

- [Multi-winner Approval Voting Goes Epistemic](https://mlsanity.com/paper/2201.06655/) (2022)
- [Truth-tracking via Approval Voting: Size Matters](https://mlsanity.com/paper/2112.04387/) (2021)
- [Surprisingly Popular Voting for Concentric Rank-Order Models](https://mlsanity.com/paper/2411.08367/) (2024)
- [SP-Rank: A Dataset for Ranked Preferences with Secondary Information](https://mlsanity.com/paper/2601.05253/) (2025)

## Abstract (arXiv)

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 votes only work when the opinion of the majority of the crowd is relatively accurate. A clever recent approach, surprisingly popular voting, 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 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.