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1. Can Voters Detect Malicious Manipulation of Ballot Marking Devices?

   https://doi.org/10.1109/SP40000.2020.00118  

   Bernhard, Matthew; McDonald, Allison; Meng, Henry; Hwa, Jensen; Bajaj, Nakul; Chang, Kevin; Halderman, J. Alex (May 2020, 2020 IEEE Symposium on Security and Privacy (SP))

   null (Ed.)

   Ballot marking devices (BMDs) allow voters to select candidates on a computer kiosk, which prints a paper ballot that the voter can review before inserting it into a scanner to be tabulated. Unlike paperless voting machines, BMDs provide voters an opportunity to verify an auditable physical record of their choices, and a growing number of U.S. jurisdictions are adopting them for all voters. However, the security of BMDs depends on how reliably voters notice and correct any adversarially induced errors on their printed ballots. In order to measure voters' error detection abilities, we conducted a large study (N = 241) in a realistic polling place setting using real voting machines that we modified to introduce an error into each printout. Without intervention, only 40% of participants reviewed their printed ballots at all, and only 6.6% told a poll worker something was wrong. We also find that carefully designed interventions can improve verification performance. Verbally instructing voters to review the printouts and providing a written slate of candidates for whom to vote both significantly increased review and reporting rates-although the improvements may not be large enough to provide strong security in close elections, especially when BMDs are used by all voters. Based on these findings, we make several evidence-based recommendations to help better defend BMD-based elections. 

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2. What We Know and Don't Know About Eligible and Registered Voters in the United States: Population, Age, and Race in Census Surveys and L2 Voter Roll Data

   https://doi.org/10.1111/ssqu.70080  

   Fowler, Christopher_S; Southern, Billy (September 2025, Social Science Quarterly)

   ABSTRACT Accurate information on who is eligible to vote—and who is registered—is essential to voting rights enforcement, election reporting, and scholarship on political participation. Yet, the data used to measure eligibility and registration vary in important ways based on how the data were collected and for what purpose. This article compares three federal data sources on citizenship and voter registration, alongside state voter roll data aggregated by a private vendor (L2). We assess their strengths and limitations, helping researchers make informed choices. We review existing research on how individual preferences, policies, and politics shape registration rates across geography and demographic groups. We also explain why some uncertainty in counts of eligible and registered voters is unavoidable. Our side‐by‐side comparison reveals striking inconsistencies. The American Community Survey overstates the citizen voting‐age population. The Current Population Survey and L2 voter file counts of registered voters show a very weak correlation across states. Counts for small geographic areas vary widely. Because we are largely unable to disentangle the source of differences we observe, we recommend using multiple data sources. We also call for more attention to how voter rolls are maintained at the local level—key to reducing uncertainty. 

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3. Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences

   Schoenebeck, Grant; Tao, Biaoshuai (January 2021, Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021))

   We consider two-alternative elections where voters' preferences depend on a state variable that is not directly observable. Each voter receives a private signal that is correlated to the state variable. As a special case, our model captures the common scenario where voters can be categorized into three types: those who always prefer one alternative, those who always prefer the other, and those contingent voters whose preferences depends on the state. In this setting, even if every voter is a contingent voter, agents voting according to their private information need not result in the adoption of the universally preferred alternative, because the signals can be systematically biased.We present a mechanism that elicits and aggregates the private signals from the voters, and outputs the alternative that is favored by the majority. In particular, voters truthfully reporting their signals forms a strong Bayes Nash equilibrium (where no coalition of voters can deviate and receive a better outcome). 

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4. The Swing Voter Paradox: Electoral Politics in a Nationalized Era

   Shiro Kuriwaki (January 2021, Ph.D. Dissertation, Department of Government, Harvard University)

   With each successive election since at least 1994, congressional elections in the United States have transitioned toward nationalized two-party government. Fewer voters split their tickets for different parties between President and Congress. Regional blocs and incumbency voting --- a key feature of U.S. elections in the latter 20th century --- appear to have given way to strong party discipline among candidates and nationalized partisanship among voters. Observers of modern American politics are therefore tempted to write off the importance of the swing voter, defined here as voters who are indifferent between the two parties and thus likely to split their ticket or switch their party support. By assembling data from historical elections (1950 -- 2020), surveys (2008 -- 2018), and cast vote record data (2010 -- 2018), and through developing statistical methods to analyze such data, I argue that although they comprise a smaller portion of the electorate, each swing voter is disproportionately decisive in modern American politics, a phenomenon I call the swing voter paradox. Historical comparisons across Congressional, state executive, and state legislative elections confirm the decline in aggregate measures of ticket splitting suggested in past work. But the same indicator has not declined nearly as much in county legislative or county sheriff elections (Chapter 1). Ticket splitters and party switchers tend to be voters with low news interest and ideological moderate. Consistent with a spatial voting model with valence, voters also become ticket splitters when incumbents run (Chapter 2). I then provide one of the first direct measures of ticket splitting instate and local office using cast vote records. I find that ticket splitting is more prevalent in state and local elections (Chapter 3). This is surprising given the conventional wisdom that party labels serve as heuristics and down-ballot elections are low information environments. A major barrier for existing studies of the swing voter lies in the measurement from incomplete electoral data. Traditional methods struggle to extract information about subgroups from large surveys or cast vote records, because of small subgroup samples, multi-dimensional data, and systematic missingness. I therefore develop a procedure for reweighting surveys to small areas through expanding poststratification targets (Chapter 4), and a clustering algorithm for survey or ballot data with multiple offices to extract interpretable voting blocs (Chapter 5). I provide open-source software to implement both methods. These findings challenge a common characterization of modern American politics as one dominated by rigidly polarized parties and partisans. The picture that emerges instead is one where swing voters are rare but can dramatically decide the party in power, and where no single demographic group is a swing voter. Instead of entrenching elections into red states and blue states, nationalization may heighten the role of the persuadable voter. 

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5. Classification of Hispanic Voters in Hispanic Groups Using Name and Zip Code Data in Palm Beach, Florida

   Soto-Ortiz, Kamila; Berezovski, Mihhail (July 2023, Beyond: Undergraduate Research Journal)

   When it comes to registering to vote, Hispanic voters can only register as “Hispanic” in the “Race/ Ethnicity” category, causing difficulties when analyzing voting trends amongst the Hispanic community. Upon the recent idea that not all Hispanic Groups vote the same, the goal is to create a model that can possibly identify a voter’s Hispanic Group with the information provided on the public Florida voter file. This is accomplished using name and zip code data for all voters in Palm Beach, Florida. This paper will explore the model implemented, its findings and limitations. Palm Beach, Florida, is met with low confidence in classification, leaving the final sample of highly confident active Hispanic voters with 15% of its original sample. Further analysis on other counties will be needed to gauge how impactful this limitation might be on the rest of the state. 

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