This paper proposes to quantify common usages of "international order" in order to catalyze a more robust scientific research program on this important concept.
This project finds that individuals have an intuitive sense of the power to hurt and explores three plausible mechanisms that could account for this dynamic.
Building upon recent innovations in Computational Science, this projects proposes a new framework for inference in international relations where strategic considerations are rampant.
Uses computational modeling and network analysis to introduce a theory of hierarchy misalignment, where discrepancies between states' material and relational power drive conflict in multiple domains.
This project investigates how states renegotiate international bargains after unexpected events undermine the initial deal and finds that internal reforms are most likely when powerful and weak states alike desire change. If there is an imbalance, however, and only one group is mobilized for change, then the creation of a new institution becomes more likely as it bypasses the veto of the other group.
This paper introduces a new way to estimate individual preferences to investigate how participants respond to partisan cues regarding tariffs.
This project draws on psychometrics and machine learning techniques to propose a non-parametric Bayesian method to optimize complex multidimensional experimental estimation.
Using a survey experiment concurrently fielded in Korea, Japan, and the U.S., this project investigates what states are looking for in an ally.
This projects introduces a new method to estimate the temporal preferences of individuals regarding collective outcomes.