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 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.
Unlike what is commonly thought, this paper demonstrates that audience costs exist because individuals have substantive preferences over policy.
This projects introduces a new method to estimate the temporal preferences of individuals regarding collective outcomes.