Counting votes at scale : what computational models teach us about election polls, voter fraud, and electoral change

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The last decade of American politics has been marked by an increasingly polarized electorate and a subsequent ideological schism in political discourse. In this climate, it is imperative for research to focus on quantitative methods, and to provide inherently objective views on topics under public scrutiny. In this study, we explore various ways in which modern computational models can help in examining political topics and presenting impartial conclusions. We begin by investigating the accuracy of election polls through an empirical analysis of polls conducted during the final three weeks of the campaigns. We establish that the average survey error is approximately twice as large as that implied by most reported margins of error. Using a Bayesian hierarchical model, we find that average absolute election-level bias is about 2 percentage points, indicating that polls for a given election often share a common component of error. We discuss how these results can help to explain polling failures in the 2016 U.S. presidential election. Next, we focus on voter fraud and show how a probabilistic birthdate model can be used to estimate the number of double votes in U.S. presidential elections. Refuting commonly asserted allegations of rampant double voting, we estimate that fewer than 0.03% of votes cast in 2012 were double votes, and we show that almost all of these cases can be explained by a plausible rate of error in marking registration records. Finally, we leverage a variant of model-based post-stratification (MP) to measure the effects of sentiment and turnout on electoral change between two elections. We use this model to explain the Republican surge in the House in 2010 and 2014, and to critique the narrative that young people have tilted Republican in these two elections. The findings of our study reveal the presence of polling error, the negligibility of voter fraud, and the advantage of using an MP-based approach to investigate electoral change, and reinforce the necessity of applying computational models to difficult topics in political science.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2018; ©2018
Publication date 2018; 2018
Issuance monographic
Language English


Author Shirani-Mehr, Houshmand
Degree supervisor Goel, Sharad, 1977-
Thesis advisor Goel, Sharad, 1977-
Thesis advisor Saberi, Amin
Thesis advisor Ugander, Johan
Degree committee member Saberi, Amin
Degree committee member Ugander, Johan
Associated with Stanford University, Department of Management Science and Engineering.


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Houshmand Shirani-Mehr.
Note Submitted to the Department of Management Science and Engineering.
Thesis Thesis Ph.D. Stanford University 2018.
Location electronic resource

Access conditions

© 2018 by Houshmand Shirani-Mehr
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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