Research & Interests

General Research Theme

The main goal with traditional social science research is that it focuses on identifying correlations between dependent and/or independent variables – that is also it’s main detractor. With the availability of more data, and more sophisticated methods of analyzing that data (traditional statistical models, machine learning, neural networks, Bayesian AI etc.) we can do a superb job of answering some difficult questions, but in the end we are still looking at correlations and not causations.
I (and others) believe that one way to move beyond correlations is to simulate – specifically – simulate using agent-level behaviors that we *know* to exist, and then try to “emerge” patterns in our simulation that we also know to exist.  If we can do that quite broadly, then we can make the argument that our collection of agent behaviors *cause* our collection of emerged patterns.
There has been a number of successful attempts towards that end, but the field is still developing. So far my research and projects have all been an attempt to “play around” with different models of simulation for different topical domains. My proposed Phd dissertation will be an attempt to take this problem head-on.
My general research theme can be summarized as being at the intersection of the social and computer sciences, the qualitative and the quantitative, the individual and the system, the deterministic and the stochastic – which makes me (proudly) a jack-of-all-trades. But it also makes me a master of one (not none), namely, the intersection of the computational sociology perspective of social networks, and agent-level simulations.

Social Network Analysis

I consider myself an expert on social network analysis methodology, theory and applications. I began studying, working with data and taking courses in 2008, and have continued to this present day. It’s an area that I’m deeply passionate about. I utilize UCINET, Pajek, Gephi, R, Python, Java, ORA, and Nodexl for my analysis.

Agent-based Modeling

Agent-based modeling is a modeling and simulation process by which you create agents in an environment with simple rules. When the simulation is run, we can emerge interesting macro patterns. It is a way of doing science from the ground up to contrast inductive or deductive science. Most of my simulations are run in Netlogo, Python, Anylogic and I’m working on expanding to MASON, REPAST and Java in general.

 

Geographic Information Systems

This is an area of recent interest. It began when I decided to model a ride sharing service (Uber or Lyft) in the Washington, DC area, and began to like the process of modeling GIS systems. There’s some interesting work being done in this area, mainly in the autonomous vehicle area, so I plan to continue my work in this area.

Terrorism and Extremism

In 2015, I was introduced to the concept of terrorism and media propaganda. My work in this area was immediately accepted by an international community of scholars and military practitioners. It’s an area that I’d like to continue working in but from a limited perspective.

Other Areas

I’m also interested in economic networks, big data, and information theory.