“Most of our notions are intuitive, unformalized, and vague. This suits us well enough, most of the time, and arguably some degree of vagueness is inevitable. Still, from time to time we want to make a notion less vague, less intuitive and more explicit, more amenable to examination and reasoning – to formalize it.”
Cosma Shalizi is an Associate Professor in the Statistics department at Carnegie Mellon University and an External Professor at the Santa Fe Institute.
Much of his earlier work involved complexity measures, like thermodynamic depth, and especially Grassberger-Crutchfield-Young “statistical complexity,” the amount of information about the past of a system needed to optimally predict its future. After receiving his doctorate, he moved from the mathematics of optimal prediction to devising algorithms to estimate such predictors from finite data, and applying those algorithms to concrete problems.
Today, his work focuses on using tools from probability, statistics and machine learning to understand large, complex, nonlinear dynamical systems; like applying data mining and machine learning to economics to form a new model of economic thinking.
Prior to his current positions, he was a post-doctoral fellow at the University of Michigan’s Center for the Study of Complex Systems and at the Santa Fe Institute. He received his Ph.D. in theoretical physics from the University of Wisconsin-Madison in 2001. A frequent collaborator of Henry Farrell, Shalizi is writing a book on the statistical analysis of complex systems models. His published papers include: “Social Media as Windows on the Social Life of the Mind,” “Quantifying Self-Organization with Optimal Predictors” and “Methods and Techniques in Complex Systems Science: An Overview,” among many others. He also writes the popular science blog Three-Toed Sloth.