Graduation Year

2026

Date of Submission

4-2026

Document Type

Open Access Senior Thesis

Degree Name

Bachelor of Arts

Department

Mathematical Sciences

Reader 1

Evan T.R. Rosenman

Rights Information

2026 Jason J Liang

Abstract

Polling results from traditional single-choice plurality elections are readily interpretable. Simple frequentist population parameters are estimated, including each candidate’s total support and the size of the front runner's lead. If the point estimate for the size of the front runner's lead exceeds the margin of error of the lead, we can conclude that the poll shows a statistically significant front runner. However, the interpretability of these population statistics disappears when applied to ranked-choice voting elections. Because ballots rank multiple candidates and candidates are eliminated in rounds, simple population-wide parameters are not well-defined. In RCV elections, a candidate’s ability to win may depend on both their support across the ballot and the order by which other candidates are eliminated. Existing measures of sampling uncertainty for polls of RCV elections do not clearly quantify these path-dependent outcomes. This thesis proposes a Bayesian framework to interpret the results of polls for RCV elections. RCV simulation models are run on both a leading poll of the 2021 New York City Democratic Mayoral Primary (NYC 2021) and simulated polls from the election's official cast-vote-records.

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