International Relations

I've just had the singular opportunity of participating in the habilitation examination of Gianluca Manzo at the Sorbonne, based on his excellent manuscript on the relevance of agent-based models for justifying causal claims in the social sciences. Manzo is currently a research fellow in sociology at CNRS in Paris ( Centre National de la Recherche Scientifique ), and is a prolific contributor to analytical sociology and computational social science. The habilitation essay is an excellent piece of work and I trust it will be published as an influential monograph. Manzo has the distinction of being expert both on the philosophical and theoretical debates that are underway about social causation and an active researcher in the field of ABM simulations. Pierre Demeulenaere served as a generous and sympathetic mentor. The committee consisted of Anouk Barberousse, Ivan Ermakoff, Andreas Flache, Olivier Godechot, and myself, and reviewer comments and observations were of the highest qua...

One of the most interesting authorities on social models and simulations is Scott Page. This month he published a major book on this topic, The Model Thinker: What You Need to Know to Make Data Work for You , and it is a highly valuable contribution. The book corresponds roughly to the content of Page's very successful Coursera course on models and simulations, and it serves as an excellent introduction to many different kinds of mathematical models in the social sciences. Page's fundamental premise in the book is that we need many models, and many intellectual perspectives, to make sense of the social world. Mathematical modeling is a way of getting disciplined about the logic of our theories and hypotheses about various processes in the world, including the physical, biological, and social realms. No single approach will be adequate to understanding the complexity of the world; rather, we need multiple hypotheses and models to disentangle the many concurrent causal and syste...

The Center for the Study of Complex Systems at the University of Michigan hosted an intensive day-long training on some of the basics of machine learning for graduate students and interested faculty and staff. Jake Hofman, a Microsoft researcher who also teaches this subject at Columbia University, was the instructor, and the session was both rigorous and accessible ( link ). Participants were asked to load a copy of R , a software package designed for the computations involved in machine learning and applied statistics, and numerous data sets were used as examples throughout the day. (Here is a brief description of R; link .) Thanks, Jake, for an exceptionally stimulating workshop. So what is machine learning? Most crudely, it is a handful of methods through which researchers can sift through a large collection of events or objects, each of which has a very large number of properties, in order to arrive at a predictive sorting of the events or objects into a set of categories. The obj...