{"type":"ScholarlyArticle","title":"Sampling Winners in Ranked Choice Voting","authors":[{"name":"Matthew Iceland","slug":"matthew-iceland","given":"Matthew","family":"Iceland","affiliation":"University of Rochester"},{"name":"Anson Kahng","slug":"anson-kahng","given":"Anson","family":"Kahng","affiliation":"University of Rochester"},{"name":"Joseph Saber","slug":"joseph-saber","given":"Joseph","family":"Saber","affiliation":"University of Rochester"}],"abstract":"Ranked choice voting (RCV) is a voting rule that iteratively eliminates least-popular candidates until there is a single winner with a majority of all remaining votes. In this work, we explore three central questions about predicting the outcome of RCV on an election given a uniform sample of votes. First, in theory, how poorly can RCV sampling predict RCV outcomes? Second, can we use insights from the recently-proposed map of elections to better predict RCV outcomes? Third, is RCV the best rule to use on a sample to predict the outcome of RCV in real-world elections? We find that although RCV can do quite poorly in the worst case and it may be better to use other rules to predict RCV winners on synthetic data from the map of elections, RCV generally predicts itself well on real-world data, further contributing to its appeal as a theoretically-flawed but practicable voting process. We further supplement our work by exploring the effect of margin of victory (MoV) on sampling accuracy.","doi":"10.24963/ijcai.2024/314","url":"https://ijcai.org/proceedings/2024/314","pdf":"https://ijcai.org/proceedings/2024/0314.pdf","markdown":"https://ijcai.org/proceedings/2024/314.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/314","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Game Theory and Economic Paradigms","track":"Main Track","number":314,"pages":{"first":2834,"last":2841},"keywords":["Game Theory and Economic Paradigms: GTEP: Computational social choice"],"videos":[],"references":[{"position":1,"raw":"[Arora et al., 2012] Sanjeev Arora, Elad Hazan, and Satyen Kale. 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