David Masad is a Ph.D. candidate in the Department of Computational Social Science. His research interests include agent-based and network models of international relations, with a focus on conflict.

What is your background?

I went to college at the University of Chicago, where I majored in economics and minored in near eastern studies and political science. I was already interested in ways of formally modeling social systems, and economics seemed to offer a way of doing that. After college, I moved to the DC area for a job as an analyst with a Department of Defense contractor. I gained some great experience applying my very theoretical college training to real-world questions and data, but also started to see more of the problems with conventional economics tools and methods.

Why did you choose CSS at GMU?

One of my good friends in college was doing research building detailed computer simulations of individual-level behavior, which is how I found out about agent-based modeling (ABM). Around the same time, I started reading a blog by a then-graduate student at NYU named Drew Conway, who was doing exciting things at the intersection of political science and what was about to be called data science. That's how I discovered that computation offered a whole set of tools for doing social science that I hadn't really been exposed to before.

My college friend, Sarah Wise, moved to the DC area at the same time as I did to start the PhD program at GMU CSS, and I got to hear about her coursework and research. Pretty soon I started taking part-time classes as part of the CSS certificate program.

So when I decided I wanted to pursue CSS at the PhD level, GMU was obviously on the list of options. There were, and still are, very few computational social science programs, and even fewer where complexity and agent-based modeling are taught alongside data science. While traditional political science departments are becoming increasingly open to computational methodologies, most of them don't have faculty who can provide training and mentorship in those areas. Similarly, most computer science programs are not doing much training or research on social science questions and topics.

The CSS department at GMU offered a unique opportunity to study both the computational methods and the social science issues I was interested in. The program allowed me to explore different areas of interest, and to work with students and faculty with a wide range of technical and qualitative interests. The program's flexibility also allowed me to integrate courses from the School of Government, Policy and International Affairs and the computer science department into my plan of study. Finally, the location, and the department itself, offered opportunities to communicate with the broad Washington, DC policy community.

What courses have you done and what have you enjoyed about them?

The first course I took in the department was CSS 692: Social Network Analysis. It gave me a formal foundation in both the social science and mathematics of network analysis, which would become one of my most important research tools. It also helped jump-start my programming skills, going from simple homework assignments to executing a final research project that required multiple programming languages to accomplish.

Another valuable course was CSS 610, now called Agent-Based Modeling and Simulation. The lectures focused on the nitty-gritty of implementing and analyzing agent-based models, which gave me foundational skills I routinely apply in my research. The student-led presentations, in the mean time, provided a broad survey of ABM applications across multiple disciplines, from sociology to video games.

What is your research area/PhD topic?

My dissertation is on comparative modeling of international conflict; it involves building multiple theory-grounded models of how states make decisions in the run-up to wars and studying how those models compare to one another and to empirical data. In many ways, it is the cumulation of everything I've learned in the CSS program: it combines techniques ranging from "zero-intelligence" ABMs to empirical network analysis, and is intended to build on from where previous, economics-derived models left off.