Job Market Paper
Abstract: When people choose what messages to send to others, they often consider how others will interpret the messages. In many environments, particularly in politics, people are motivated to hold particular beliefs and distort how they process information in directions that favor their motivated beliefs. This paper uses two experiments to study how message senders are affected by receivers' motivated beliefs. Experiment 1, conducted using an online sample of social media users, analyzes the effect of incentivizing senders to be perceived as truthful. These incentives cause senders to send less truthful messages. When incentivized, senders send more false information when it aligns with receivers' politically-motivated beliefs, controlling for receivers' current beliefs. However, receivers do not anticipate the adverse effects of senders' incentives. Experiment 2 further isolates the role that information processing plays by analyzing an environment in which receivers assess the truthfulness of messages from a computer and senders choose one of the computer's messages to determine their earnings. Senders predict that receivers distort information processing in the direction of their politics, demand information about receivers' political preferences, and condition on the receivers' politics to strategically choose less truthful computer messages.
Summary: I design a new experimental tool for portably identifying motivated reasoning from Bayesian updating, and use this tool to show that politics affects inference about factual questions on a broad range of topics: immigration, income mobility, crime, racial discrimination, gender, climate change, and gun laws. Politically-motivated reasoning is more severe for partisans and leads people to over-trust news more when it reinforces the biases in their beliefs.
(Study materials here)
Summary: I experimentally show that people systematically overinfer from weak signals despite underinferring from strong signals. Overinference is especially prominent for very weak signals, which have often been neglected in the literature. Greater task experience and cognitive sophistication are each associated with less overweighting of weak signals and less underweighting of strong signals. Results are consistent with a theory in which agents are cognitively imprecise about the strength of signals.
Summary: I experimentally test whether people motivatedly reason towards believing the world is a good place for others, and find a well-powered null effect. Via a survey, I show that respondents anticipate that these types of "positive" beliefs make people happier but are not necessarily beliefs that they are motivated to hold.
Polarization and Public Health: Partisan Differences in Social Distancing during the Coronavirus Pandemic (with Hunt Allcott, Levi Boxell, Jacob Conway, Matthew Gentzkow, and David Yang). Journal of Public Economics, 2020.
Summary: Using GPS data, we show that areas with more Republicans have engaged in less social distancing during COVID-19 than areas with more Democrats, and using a new survey, we show that Republican individuals self-report less social distancing and believe that the risk of COVID-19 is lower.
Gender Differences in Motivated Reasoning. Journal of Economic Behavior and Organization, 2021.
Summary: I show experimentally that there are statistically significant gender gaps in motivated reasoning about outperforming others, but that motivated reasoning about political issues is similar across genders. Motivated reasoning can help explain why there are gender gaps in overconfidence.
Works in Progress
N Angry Men: The Importance of Endogenizing Jury Size when Comparing Voting Rules
Summary: I present a model that compares the accuracy and efficiency of jury voting mechanisms when both jury size and voting rule can vary. The main result is that an (n+2)-person jury that allows for one dissenter is both more accurate and less likely to hang than an n-person jury under unanimity. (Slides available upon request.)
Using Negative Feedback to Debias Motivated Reasoning
Summary: I test the effect of an intervention that gives people feedback when they incorrectly assess the truthfulness of news sources. The intervention improves people's assessments of news on subsequent topics and the effect is driven by decreasing trust in false information. In addition, treated subjects are significantly less overconfident.
Dynamic Information Acquisition in the Lab (with Alessandro Lizzeri and Leeat Yariv)
And Now for Something Completely Different
Tribone Tilings of Triangular Regions that Cover All but Three Holes. Discrete & Computational Geometry, 2015.
Summary: In my undergrad math thesis, I showed that there are a lot of ways to tile triangular regions with tribones when you leave three spaces uncovered.