In the "Big Data" context, large amounts of data are processed using analytical techniques to uncover relationships and identify trends. This talk introduces a complementary viewpoint: using an initial set of assumptions about the state of the world derived from data analytics, a computer simulation model functioning as a "silicon petri dish" can be used to create synthetic data containing salient features of the real data set. Data analysis techniques are balanced with modeling and simulation, leading to a fuller understanding of the actors in the problem domain, their interactions with each other, and their interactions with the environment. The talk includes examples of analytical techniques and simulation models to be selected from the natural and social sciences. Audience participation was highly encouraged via interactive team-based problem-solving activities.


Stephen Scott is a Senior Information Systems Engineer at the MITRE Corporation, a non-profit Federally Funded Research and Development Center (FFRDC). He has supported a variety of federal agencies on large-scale systems engineering, software engineering, and system architecture tasks. His research interests at MITRE have focused on applying innovative design thinking and systems thinking methods used in commercial industry to government agency projects. Prior to joining the staff at MITRE, he worked as a systems and software engineer at Hughes Aircraft Company.

Dr. Scott holds a PhD in Computational Social Science and a Master of Science in Computer Science from George Mason University, where he also serves as an adjunct faculty in the College of Science. His academic research is focused on the use of Complex Adaptive Systems (CAS) approaches to the analysis of public policy, with an emphasis on natural resource management for US commercial fisheries.‚Äč

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