Hello! I'm a Rackham Predoctoral Fellow and a PhD candidate in political science and scientific computing at the University of Michigan. I've completed an MS in mathematics and a Graduate Certificate in complex systems, and I was a 2019-2020 Fellow of the Michigan Institute for Computational Discovery and Engineering.
I study problems in electoral democracy using computational social science. I am particularly interested in how electoral systems and party systems define the choices that electors face, and how that affects their representation. I also study how methods in computational social science can be used to answer questions about politics, and I create and analyze large historical classifications of elections and regime types.
My dissertation asks why established democracies sometimes manage to change their electoral systems, given that these attempts almost always fail. Building on the idea that incumbents shape election rules in their own interests, I introduce a method that estimates the seat counts for the country's major political parties under a variety of alternative electoral systems. The method accounts for the fact that people might vote differently under a different electoral system, by combining game theory and public opinion data into a computational formal model that can be applied to nearly any modern Single-Member Plurality election. Though incumbents do choose the system that they perceive to be in their best interest, the results suggests that incumbents are not necessarily correct about what their interests are. Applying the tool to recent failed attempts to change electoral systems in Canada and the United Kingdom, the model suggests that certain alternative systems would have been beneficial to enough parties that a change would actually have been broadly advantageous. In other cases, the method can help explain the design of unusual electoral systems, like the particularly baroque system that Alaska now uses to elect its federal representatives.
Details: This is a chapter in Why Democracies Develop and Decline, edited by Michael Coppedge, Amanda B. Edgell, Carl Knutsen, and Staffan Lindberg, forthcoming with Cambridge University Press. Allen Hicken and Fabricio Vasselai are coauthors.
Summary: We re-examine the effect of some of the most well-studied political institutions on democratic stability and quality. We focus on how three categories of institutions shape democracy: state capacity, executive regime type, and party system features. The precision and coverage of Varieties of Democracy data allows us to consider how institutional arrangements affect the probability of a democratic step-up or a democratic step-down when democracies are defined in hundreds of different ways, so we can make much more precise claims about how different institutional arrangements matter for countries that have different levels of democracy.
Details: This article is part of a symposium on Wikipedia and political science, accepted at PS: Political Science and Politics and edited by Kristin Michelitch and Brooke Ackerly
Summary: One in five political scientists with biographies on Wikipedia are women, while nearly half are American. Biases on Wikipedia can cause real harm, so I created or expanded a political science-related Wikipedia article every day for a year, focusing on writing new pages about political scientists from underrepresented groups. In the article I show that Wikipedia's coverage of political scientists remains skewed by gender and nationality, and I suggest ways for political scientists to improve Wikipedia's representation of the discipline. I also wrote about this project in The Monkey Cage.
Summary: We show that behind decades of disagreements over how to classify regime types, there is an unexpectedly strong consensus about which countries have been democratic in which years. By matching many-valued classifications of democracies and autocracies to binary classifications, and finding the cutpoint that dichotomizes each many-valued measure so that it matches each binary measure as closely as possible, we find evidence of a strong underlying agreement between all of the major classifications.
Summary: Election forensics identifies fraudulent activity from empirical distributions of election turnout and vote choice, but strategic behavior can affect these distributions in ways that might resemble fraud. Many types of election forensics interpret a multiplicity of modes in election data as indicators of frauds, but strategic behavior induces correlations among electors' behavior that can produce multimodalities. We design simulations to match equilibria derived in models of wasted vote logic and of strategic abstention, and we simulate elections in which we know that strategic behaviour is present while fraud is not. The results suggest that fraud detection models might misidentify legitimate strategic coordination as a signal of election fraud.
Summary: In the period before a large democratic election, thousands of electors, with diverse preferences and very different ways of making decisions, update their ideas about which candidates are competitive and how they should vote. Iterative computer models allow us to study this process without abstracting away the notion of time, in a framework that avoids some of the less realistic assumptions of pencil-and-paper models. But how do we know when the model has converged to an election result? I demonstrate that certain broad categories of iterative election models produce cyclic behaviours in the electors' strategic intentions, and that those cycles obey simple rules. This opens up a new modeling framework that captures some of the dynamics of elections better than existing formal models can.