Sponsored by The Macmillan Family Trust.
On April, 2014, the Vermont Complex Systems Center ran the Macmillan Symposium. Our invited speakers offered broad scientific perspective on prediction with application to the interface between physical and socio-technical systems including climate, culture, finance, medicine, and politics. We brought together a team headed by two leading scholars working in the realm of complex social systems, including Neil Johnson (Physics, University of Miami) and César A. Hidalgo (Media Lab, MIT) with an experienced group of UVM faculty and students to showcase the scientific landscape of prediction.
From the bare goal of survival up to the privileged one of flourishing, strong predictive capabilities are essential in the full spectrum of evolutionary systems. All complex life employs algorithmic inferences about the future, people choose careers in part based on prospects, and countries and corporations must soundly anticipate economic and cultural changes. Prediction runs from the mundane and individual—knowing it might rain today means we should bring an umbrella—to the potentially disastrous and widespread—an incoming category 5 hurricane leading to the evacuation of cities.
From antiquity, people of all cultures have been obsessed with finding new ways to foretell the fates of all things, producing a panoply of inventive divination ideas. We have looked for direction in the words of oracles, the alignment of the stars and our births, and the flight paths of birds. We have contended with—and discarded—the possibility of a deterministic, mechanistic universe, all paths laid out from the start. But as the physical sciences have grown, we have had much success in many areas: we have described the fundamental unpredictability of the quantum world, and we have steadily improved our ability to predict the weather and certain natural disasters, crucially quantifying and explaining our uncertainty.
The advent of global, interconnected sociotechnical systems and their quantification in “Big Data” would seem to hold much promise for our ability to greatly expand the scope of prediction science. In the 2014 MacMillan Symposium, the Vermont Complex Systems Center will bring together a team headed by Lazar and Johnson for a week-long series of research and teaching activities aimed at understanding and building a new array of divination methods.
April 28, 2014
Davis Center: Grand Maple Ballroom
1:00–2:00pm: Grand Maple Ballroom, Davis Center
Peter Dodds, UVM, Director of the Vermont Complex Systems Center: Welcome
César A. Hidalgo, MIT: “Saving Big Data from Big Mouths”
Mary Dunlop, UVM: “Synthetic Biology: From Gene Circuits to Genomes”
Peter Spector, UVM: “Taming the Fibrillating Heart: Coercing Order out of Chaos”
Maggie Eppstein, UVM: Creating Complex Systems Scholars, UVM Certificate in Complex Systems
2:00–3:00pm: Break, Livak Ballroom, Davis Center
Josh Bongard, UVM, Associate Director of the Vermont Complex Systems Center: Introduction to Poster Session
As part of the Macmillan Symposium, UVM graduate and undergraduate students with the Vermont Complex Systems Center will showcase their research with a poster presentation session.
3:00–4:00pm:Grand Maple Ballroom, Davis Center
Chris Danforth, UVM, Associate Director of Vermont Complex Systems: Welcome
Catherine Bliss, UVM: “Friend or Follower: Why Your Social Neighborhood May be Smaller Than You Think”
Benjamin Littenberg, UVM: “Diagnosis and Prediction in Health Care.”
James Bagrow, UVM: “Flight or Fight: Predicting Human Dynamics with Phones and Tweets”
Neil Johnson, University of Miami: “Calm Before the Storm: Predicting Civil Unrest Using Big but Imperfect Data”
César Hidalgo is the head of the Macro Connections group at the MIT Media Lab and the ABC Career Development Professor at MIT. An antidisciplinary academic by choice, and a poet at heart, Hidalgo’s efforts focus on improving our understanding of the world’s complexity. His tools include the construction of visualization engines that make available unwieldly volumes of data, the development of data collection methods and metrics that make visible hitherto neglected aspects of our reality, and the development of theories and narratives that can help put together the pieces that reductionist approaches have pulled apart.
Neil Johnson heads up a new inter-disciplinary research group in Complexity at the University of Miami (Physics Dept.) looking at collective behavior and emergent properties in a wide range of real-world Complex Systems: from physical, biological and medical domains through to social and financial domains. His research group continues to develop new interdisciplinary projects with other departments and schools within the University of Miami (including medicine, life sciences and social sciences) and with other institutions within U.S. and globally, e.g. Universidad de Los Andes in Bogota, Colombia, with Luis Quiroga, Ferney Rodriguez and Roberto Zarama, and the Chinese University of Hong Kong with Pak Ming Hui. Until summer 2007, Neil was Professor of Physics at Oxford University (U.K.), having first joined the faculty in 1992.
James Bagrow is an Assistant Professor in the Department of Mathematics and Statistics at UVM. His interests include: complex networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).
Catherin Bliss is a doctoral candidate in the Department of Mathematics at the University of Vermont. Her coursework and qualifying exams have spanned both pure (Real Analysis, Complex Analysis, and Abstract Algebra) and applied areas (Numerical Analysis, Differential Equations and Complex Systems). She is interested in dynamical processes on large, evolving complex networks. Many phenomena can be modeled as complex systems; systems from which emergent properties arise from nonlinear localized dynamics. With the increased availability of in situ network data, many questions of scientific interest previously addressed through analysis and simulation can now be explored through computation in a “big data” framework. She is especially interested in characterizing time-varying complex networks from incomplete or noisy data, the description and explanation of evolutionary network dynamics, and contagious processes on networks.
Dr. Spector is the director of Cardiac Electrophysiology and Cardiac Electrophysiology Laboratory at Fletcher Allen and a Professor at the University of Vermont College of Medicine. His clinical specialty includes ablation of complex abnormal heart rhythms. As professor of Medicine at UVM College of Medicine, Dr. Spector’s research interest is atrial fibrillation. He believes that research and clinical practice are mutually beneficial to patient treatment and that being a teacher and scientist improves the quality of his patient care.
Mary Dunlop joined the faculty of the University of Vermont in the School of Engineering in July of 2010. Her research interests center on control theory and synthetic biology. In her lab students research how feedback control systems are implemented in molecular biology. She is particularly interested in processes that are dynamic and use fluorescent proteins and time-lapse microscopy to image single cells over the course of many hours. Applications include problems in bioenergy and medicine.
Dr. Littenberg is the Henry and Carleen Tufo Professor of Medicine as well as Professor of Nursing, Director of General Internal Medicine, and Associate Director of the Center for Clinical and Translational Science at the University of Vermont. He founded and serves as Chief Medical Officer of Patient Engagement Systems, a company whose NIH clinical trial-validated chronic disease decision support tools for providers and patients is being utilized in primary care in New England, New York, California, and Texas. Dr. Littenberg’s research interests center on technology assessment and quality improvement.