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What aspects of politics and political methodology have the largest untapped potential for work in the world of data science? We’ve gone from this being cutting edge to a standard tool in a bit over a decade. There has also been a revolution in computations, with Markov chain Monte Carlo making things possible. These days I expect all of our graduate students to become conversant with these methods. Gary King, Langche Zeng, and I have looked into neural nets in to study conflict under the hypothesis that one needed high order complex interactions to model conflict. I’ve worked with Jonathan Katz on time-series - cross-sectional models, and I also did some work with Simon Jackman on generalized additive models. My first presentation in a statistics class at Yale was on an early tree based method (Automatic Interaction Detector), and though at the time it was laughed at, with more modern thinking about cross-validation and such, this has become a rather useful tool (essentially the move from stepwise regression to LASSO).Īs a person interested in time series, I have always thought about the possibilities with out of sample validation, and cross validation has proved increasingly useful. While I’m interested in text analysis, my own interests have always been in non-linear methods.
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What data science methods have you used in the past for your work in political science? Natural language processing seems to be an obvious one, but I’m curious if there are other disciplines in data science you’re using to study politics. Fortunately Bayesian models can be very useful here.
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We still need to figure out how to get trees to tell us about social phenomena (as opposed to simply classifying things, our task is not to read zip codes) and we need to continue to put them on a firm statistical basis. Now more complicated models (trees) are in that state. 50 years ago regression was state of the art. The use of regression models is just a very simple form of machine learning. How does machine learning factor into your work? I am also actively trying to see if there are ways to combine machine learning methods with structural assumptions we have about political systems. One of my main interests is figuring out the usefulness of machine learning methods for political science, where we want to describe what the so called, “black box” looks like. Within the cross-section of political science and data science, what are some of your research interests? And I had been doing supervised machine learning for a long time before I even knew that term existed I developed an interest in non-linear models very early on, so moving to data science was not a hard sell. How did you start to incorporate data science into your political science studies? I loved the research and have never looked back. course and easily chat with someone like Riker. Rochester was also the kind of place where an undergraduate could just walk into a Ph.D.
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It was sheer luck I was at the University of Rochester, as they had just hired Bill Riker who was one of the key founders of modern political science (both in terms of game theory and top empirical work). Around that time I was excited by Isaac Asimov’s science fiction series, Foundation, and I became interested in his psycho-historian characters who could forecast mass movements in society, so I turned to political science. I started off as a math major, but I soon realized I would never be a really good mathematician. I went to the University of Rochester almost half a century ago. What did you study in school? How did you get to what you study now? Nathaniel Beck is a Professor at New York University’s Wilf Family Department of Politics, and an Affiliated Faculty member at the Center for Data Science.