| dc.description.abstract | In this thesis, I describe three approaches to political communication and decision-making. Chapter 1, ``Persuasion with Ambiguous Receiver Preferences,'' studies an informed Sender who knows only the average threshold belief needed to persuade a Receiver and wishes to safeguard against unfavorable distributions of individual preferences. Chapter 2, ``Discovery through Trial Balloons,'' examines how correlation between different projects affects information disclosure by a principal who designs a bundle of projects that an agent can then choose to approve. Chapter 3, ``Strategic Opinion-Writing on Appellate Courts,'' describes how and why the partisan composition of quasi-random panels of judges on the U.S. Federal Courts of Appeals affects consensus-building. I describe each chapter in more detail below.
The first chapter, ``Persuasion with Ambiguous Receiver Preferences,'' describes a Bayesian persuasion problem where Receiver has a private belief cutoff for 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 mean-preserving contraction of the prior over states and adversarial Nature chooses a Receiver type distribution. 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.
The second chapter, ``Discovery through Trial Balloons,'' presents a model of a principal and an agent who face symmetric uncertainty about the agent's value for two correlated projects. The principal chooses which project values to publicly discover and makes a proposal to the agent, who accepts if and only if the expected sum of values is positive. I characterize optimal discovery for various principal preferences: maximizing the probability of the grand bundle, of having at least one project approved, and of a weighted combination of projects. My results show when discovering ex-ante disfavored projects may be optimal; these conclusions rationalize the inclusion of controversial policies in omnibus bills and the presence of moonshot projects in organizations.
The third chapter, ``Strategic Opinion-Writing on Appellate Courts,'' studies consensus and decision-making by powerful judges on the U.S. Federal Courts of Appeals. Using quasi-random three-judge panels on these courts from 1970\textendash 2013 I document a novel pattern in dissenting opinions: compared to party-unanimous panels, party-mixed panels cause all judges to dissent more often, and at equal rates. This result is incompatible with classical models of judicial politics and is unique to partisanship. To explain my results, I introduce a theoretical framework where judges' favored coalitions are more homogeneous along both partisan and non-partisan dimensions. Using judge metadata, I find suggestive evidence for the model's result that polarization increases dissents 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 even when rulings are unanimous. | |
