Nature, our societies and institutions, our bodies. These are all complex systems. How do we interpret and synthesize outcomes of these systems? How do we quantify the uncertainty in these interpretations and outcomes? These are challenging problems for scientists and engineers, accustomed to a protocol of hypothesis and proof by overwhelming evidence. These problems are even more
challenging for mathematicians who's standard is the theorem. Traditional science and engineering has few tools to analyze and manipulate compelling evidence and outcomes of complex systems. Yet climate scientists were asked to determine whether the Earth was indeed warming and whether the anthropogenic component was significant. Pharmaceutical companies and medical doctors dispense drugs that can heal, yet our understanding of the human body iand its variability is far from complete. Perhaps we need to develop a different way to quantify and/or understand complex systems? In this first of a series of workshops the Uncertainty Quantification Group is bringing together researchers from a variety of disciplines for the more modest first step of exchanging notions about prediction, and, in particular cross-pollinating ideas and techniques within uncertainty quantification, risk analysis, multiscale modeling, machine learning, and statistical mechanics. The Uncertainty Quantification Group, under the sponsorship of the National Science Foundation, is inviting scientists in
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