JB Duck-Mayr

I am a PhD Candidate in the Department of Political Science at Washington University in St. Louis. My research centers on American political institutions, with a particular focus on judicial decision making. I engage in both formal theoretic and empirical research approaches, and a passion for accurate statistical inference in my substantive work also leads to methodology-focused projects.

In my dissertation, I examine how the law constrains judicial decision making and why judicially crafted policy can seem inconsistent. In the first chapter (forthcoming at Journal of Theoretical Politics), “Explaining Legal Inconsistency,” I use a social choice theoretic model to show sometimes collegial courts cannot avoid generating policy that appears inconsistent. My third chapter (and job market paper), “The Force of the Law,” shows the U.S. Supreme Court’s past decisions reliably impact the decisions of most, but not all, Supreme Court justices. It uses a Bayesian nonparametric model developed in the machine learning literature, Gaussian process (GP) classification, as I demonstrate linear models can be inappropriate when studying legal rules. To reach that chapter’s inferential quantity of interest, I had to develop new tools for inference in GP models, which I do in my second chapter, “Inference in Gaussian Process Models for Political Science.”

Outside my dissertation, I have work published and under review on judicial politics as well as methodology. My judicial politics projects examine agenda setting within the U.S. federal judicial hierarchy (with Thomas G. Hansford and James F. Spriggs II, forthcoming at Journal of Law and Courts) and explore how competitiveness of legislative elections affects judicial independence (with Joshua Boston and David Carlson, R&R’d at Journal of Law and Courts). My methods projects focus on improving measurement of political ideology, including a project focused on capturing “ends against the middle” behavior where extremists from both ends of the ideological spectrum oppose moderate proposals (with Jacob Montgomery, conditionally accepted at Political Analysis) as well as a flexible nonparametric Bayesian ideal point model (with Roman Garnett and Jacob Montgomery, published in Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence).