I grew up in San Diego, went to Notre Dame on an ROTC scholarship, earned an M.A. in economics at Duke University focusing on mathematical models applied to coalitions and conflict, and commissioned as an aviator in the U.S. Navy in 2011. I was the top graduate in Super Hornet training from 2011-14, before deploying overseas as an F/A-18 Pilot with VFA-102 from 2014-17. My Ph.D. in economics focused on game and network theory to model firms in high-innovation sectors and coalitions in contests and conflict.

I taught logic, economics, and econometrics and served as the speechwriter for the Superintendent at the U.S. Naval Academy from 2017-21. Concurrently, I consulted at the World Bank on the Blue Economy Project, applying alliance models to the strategic needs of Small Island Developing States aligning my academic expertise with my passion for the ocean. There, I compiled and analyzed data on Island States' maritime resources and industries to make policy recommendations given their unique geo-economic constraints and relationships with larger economies. I taught an empirical techniques course on data classification methods and empirical modeling of conflict at the Kyiv School of Economics and Duke University.

Research Interests:


The ability to predict macro- and micro-level conflict would be a powerful tool, both for winning and deterring engagements, thereby mitigating catastrophic human suffering. However, predicting rare events that are often path-dependent is inherently difficult and out-of-sample forecasts are unreliable. One of the problems is that geopolitical (and geoeconomic) events often break the unit of analysis, meaning theory-free predictive models (essentially machine learning black boxes) are of limited value. Furthermore, the causal relationships that result in conflict are not well understood; a more structural understanding of causal relationships in conflict is needed.


While predictive models and deep learning algorithms that attempt to predict outcomes of battles and campaigns have flourished, they lack theoretical grounding and cause-and-effect structure, missing or ignoring the multilateral nature of warfare which invalidates their predictions. Even U.S. military assessments are siloed and training objectives narrowed to ignore simultaneous involvement of multiple states with mixed incentives because of limited time and resources to plan and practice. However, coalitional relationships in the lead up to and execution of conflict are decisive and are inherently network games of private information. In fact, history is littered with examples of the grave errors and miscalculations that discounting these relationships induces. Imagine if at the beginning of 2022 there was general consensus, with high-confidence, that NATO and EU nations would provide military support to Ukraine if it were attacked by Russia. Would Russia have invaded?  Russia, having undermined the NATO and EU relationships for years, failed to anticipate that these links would coalesce, a costly and impactful error.

 

Out-of-sample conflict predictions with automatically coded data are unreliable, if not impossible. But predicting (with sound theoretical understanding) who gets involved in conflict, which relationships strengthen, and which break is within reach, would have a greater policy impact, and is largely unexamined. My research attempts to build policy tools to predict coalition formation and stability both in anticipation and execution of conflict.


I apply these methods to geoeconomic coalitions accounting for the flows of trade, finance, security, environmental concerns, and high-innovation start-up industries. My goal is to apply mathematical methods to the realms of Internatinal Relations, Industrial Organization, Strategy, and Conflict to better understand how strategic coalitions can be constructed and how they break apart to improve policy.

Courses Taught

United States Naval Academy 2018-21

Econometrics

Statistics

Macroeconomics

Logic and Persuasive Writing

Principles of Macroeconomics

Courses Taught

Kyiv School of Economics and Duke University 2022:

Techniques for Empiricists: Data Classification Methods and Analysis to Model Conflict


Additional Courses

Game Theory

Network Theory

Microeconomics

International Relations and Methodology

Metrics


VFA Briefs

Advising for joining Strike Fighter Community