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[Colloquium] An Introduction to Complex Adaptive Systems of Systems Engineering

May 3, 2011

Watch Colloquium: 

M4V file (681 MB)

  • Date: Tuesday, May 3, 2011 
  • Time: 11:00 am — 11:50 am 
  • Place: Mechanical Engineering 218

Walt Beyeler, Tom Moore, and Patrick Finley 
Sandia National Labs

The Complex Adaptive System-of-Systems Engineering initiative at Sandia National Laboratories apples complexity science, engineering principles, computer modeling and High Performance Computing to address problems of national and global scope. Our approach recognizes that many large-scale technical and social issues are best characterized as Complex Adaptive Systems. Network modeling which builds upon both complexity research and System-of-System concepts from aerospace and defense domains provides a framework for understanding and evaluating these sysems. We construct and execute models of these complex adaptive systems on massively parallel computers to generate and test interventions and public policy options which provide socially beneficial outcomes while managing potential risks. We work on a wide variety of issues such as pandemic disease spread, global finance and energy systems, polarization of societies into extremist groups and various public health issues. Our work not only addresses policy concerns in these domains, but also pushes research boundaries in modeling methodology, network science, and uncertainty quantification. We present recent work on two models: (1) a Resource Exchange model which we use to understand economies, ecological relationships, and interactions of nation-states, and (2) an Opinion Dynamics model of propagation and control of tobacco use and other lifestyle diseases on social networks.


Bio: Walt Beyeler is an electrical engineer who designs and codes complex adaptive systems models of critical infrastructures, disease propagation, economics and finance. He develops novel hybrid modeling methods to provide parsimonious representations of these diverse systems. Walt has articulated and formalized methodologies to generate comprehensive conceptual models of a variety of complex systems over a wide range of scales.

Tom Moore is a theoretical biologist who applies concepts from evolution, selection, and complexity science to large scale issues of public health and social interactions. He designs succinct models of organizational change, network dynamics, and multi-level selection which are useful for representing complex system origin and development. Tom draws upon biological and social-science metaphors to characterize and understand a vast range of complex system issues and public policy options.

Pat Finley is a computer scientist who develops and applies novel mathematical approaches to interpret complex system model results. He designs and executes experiments on large computational clusters to rigorously explore model parameter space and to map stable and unstable regions of state space. Pat has designed unique algorithms merging advanced graph-theortic search concepts and Gaussian process meta-models to extend decision theory for public policy through uncertainty quantification.