{"type":"ScholarlyArticle","title":"Surprisingly Popular Voting Recovers Rankings, Surprisingly!","authors":[{"name":"Hadi Hosseini","slug":"hadi-hosseini","given":"Hadi","family":"Hosseini","affiliation":""},{"name":"Debmalya Mandal","slug":"debmalya-mandal","given":"Debmalya","family":"Mandal","affiliation":""},{"name":"Nisarg Shah","slug":"nisarg-shah","given":"Nisarg","family":"Shah","affiliation":""},{"name":"Kevin Shi","slug":"kevin-shi","given":"Kevin","family":"Shi","affiliation":""}],"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.","doi":"10.24963/ijcai.2021/35","url":"https://ijcai.org/proceedings/2021/35","pdf":"https://ijcai.org/proceedings/2021/0035.pdf","markdown":"https://ijcai.org/proceedings/2021/35.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/35","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":35,"pages":{"first":245,"last":251},"keywords":["Agent-based and Multi-agent Systems: Computational Social Choice","Agent-based and Multi-agent Systems: Voting","Humans and AI: Human Computation and Crowdsourcing"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}