Research

Persuasion with Ambiguous Receiver Preferences

Economic Theory, August 2023.

I describe a Bayesian persuasion problem where Receiver has a private type representing a cutoff for choosing Sender's preferred action, and Sender has maxmin preferences over all Receiver type distributions with known mean and bounds. This problem can be represented as a zero-sum game where Sender chooses a distribution of posterior mean beliefs that is a mean-preserving contraction of the prior over states, and an adversarial Nature chooses a Receiver type distribution with the known mean; the player with the higher realization from their chosen distribution wins. I formalize the connection between maxmin persuasion and similar games used to model political spending, all-pay auctions, and competitive persuasion. In both a standard binary-state setting and a new continuous-state setting, Sender optimally linearizes the prior distribution over states to create a distribution of posterior means that is uniform on a known interval with an atom at the lower bound of its support.

Examining Political Trustworthiness Through Text-Based Measures of Ideology

Proceedings of the 33rd AAAI Conference on Artificial Intelligence, January 2019.

This work shows the value of word-level statistical data from the US Congressional Record for studying the ideological positions and dynamic behavior of senators. Using classification techniques from machine learning, we predict senators’ party with near-perfect accuracy. We also develop text-based ideology scores to embed a politician’s ideological position in a one-dimensional policy space. Using these scores, we find that speech that diverges from voting positions may result in higher vote totals. To explain this behavior, we show that politicians use speech to move closer to their party’s average position. These results not only provide empirical support for political economy models of commitment, but also add to the growing literature of machine-learning-based text analysis in social science contexts.

Strategic Opinion-Writing on Appellate Courts (Job Market Paper)

Ruling on thousands of cases each year, U.S. federal courts of appeals make some of the most impactful decisions in modern society. Using quasi-random three-judge panels on these courts from 1970--2013, I study the effect of partisanship on consensus among judges. While bipartisan panels cause a roughly 25% increase in dissenting opinions over party-unanimous panels, I document a novel pattern in dissenter identity: the most politically extreme judge is no more likely to dissent than their colleagues. This result is incompatible with classical models of judicial politics and is unique to partisanship; other judge characteristics produce smaller increases in dissents which are more concentrated on outlier judges. To explain my results, I introduce a theoretical framework where favored coalitions contain the most similar judges along both partisan and non-partisan dimensions. Using judge metadata, I find suggestive evidence for the model's result that partisanship increases disagreements by judges of panel-minority law school or gender. With state-of-the-art machine learning tools from natural language processing, I generalize beyond dissents, showing that those same features drive differences in opinion text while partisanship has minimal effects. My findings show that partisanship has a powerful and complex effect on consensus-building and illustrate the need for new tools to capture the subtle effects of disagreement in this opaque yet high-stakes environment.

Discovery through Trial Balloons

Last updated: November 2022

Last updated: September 2018

The increasing digitization of political speech has opened the door to studying a new dimension of political behavior using text analysis. This work investigates the value of word-level statistical data from the US Congressional Record--which contains the full text of all speeches made in the US Congress--for studying the ideological positions and behavior of senators. Applying machine learning techniques, we use this data to automatically classify senators according to party, obtaining accuracy in the 70-95% range depending on the specific method used. We also show that using text to predict DW-NOMINATE scores, a common proxy for ideology, does not improve upon these already-successful results. This classification deteriorates when applied to text from sessions of Congress that are four or more years removed from the training set, pointing to a need on the part of voters to dynamically update the heuristics they use to evaluate party based on political speech. Text-based predictions are less accurate than those based on voting behavior, supporting the theory that roll-call votes represent greater commitment on the part of politicians and are thus a more accurate reflection of their ideological preferences. However, the overall success of the machine learning approaches studied here demonstrates that political speeches are highly predictive of partisan affiliation. In addition to these findings, this work also introduces the computational tools and methods relevant to the use of political speech data.

"Partisan Opinions, but Common Language: Similarities in Topic Use by Appellate Judges"

(draft available upon request)

The "Party Line" as Optimal Delegation