Anamaria is a PhD Candidate in the Department of Computational Social Science interested in modeling entrepreneurship through computational methods.

What is your background?

I am an economist by background, having studied International Business and Economics in my home country, Romania. At the Academy of Economic Studies in Bucharest, I received a BA, an MA and a PhD in this field and my first dissertation was also my first academic step towards the science of complexity and the methodological and epistemological differences between complexity and economics. Also in Bucharest, I started to read and learn about complexity and agent-based modeling through the guidance and work of the Center for Complexity Studies – UNESCO Center, a first of its kind in Eastern Europe.

Why did you choose CSS at GMU?

The research and curricula of the CSS Department were a natural step for me in combining my two academic passions, economics and complexity science. The inter-disciplinary nature of the classes and the friendly top-notch research environment were very attractive for me as an aspiring scholar. Also, that students and professors coming from different backgrounds are able to have academic conversations on a variety of topics by bringing new references or theoretical perspectives to the debate.

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

Due to my aforementioned interests, I have taken a variety of classes in Computational Social Science and Economics. I have particularly enjoyed the agent-based modeling and social network analysis classes, as they enabled me to have more of a “hands-on” approach to theory and to understand how the conceptual, theoretical process and the factual analysis intertwine. I have also enjoyed my classes in economics, as I learned how to think in the different frameworks developed by different schools of economic thought and which are the contributions of each of these to science today.

What is your research area/PhD topic?

My research interests have been focused on asymmetric information networks and the way they lead to the emergence of atypical organizations or topologies, such as hawala networks, the stateless tribes of Afghanistan and Pakistan, certain entrepreneurial forms (i.e. formation of first merchant colonies in Ancient Babylon, fashion markets, the housing market). Currently, I am working on applying social network analysis to aggregate contingency estimation markets and Bayesian networks and also on modeling the emergence of high-impact companies in US. My PhD dissertation is focused on the causal factors and the robust formalization of these companies, using agent-based computer simulations and social network analysis.