Complex Adaptive Systems Engineering - improving our understanding of complex systems and reducing their risk

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System of Systems (CASoS) System Engineering of Systems Engineering Initiative http://www.sandia.

1 System of Systems (CASoS) System Engineering of Systems Engineering Initiative Systems Engineering - improving our understanding of complex systems and reducing their risk Theresa Brown, Steve Conrad and Walt Beyeler Sandia National Laboratories July 2012 Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department Earth Systems of Engineering Symposium Energy s National Nuclear Security Administration under contract DE-AC04-94AL85000.

the perturbations the system has experienced and modifications it has implemented.

2 What are Systems (CAS) and why do we want to reduce their risks? A CAS as one in which the structure modifies to enable success in its environment structure and behavior are products of all the perturbations the system has experienced and modifications it has implemented. certain structural characteristics emerge, hierarchical and modular, with simple rules for interaction among the elements Many persistent, large-scale engineering challenges involve multiple interacting CAS or Systems of Systems (CASoS). 2

3 Why CASoS Engineering? Complex adaptive systems are central to many persistent problems locally and globally Climate change, economic crises, energy and food supply disruptions have global effects Evaluating the dynamic interactions improves our understanding of risks and how to reduce them Climate change and the challenge of addressing the global risks provides a common set of problems on which to build a global community of practice for engineering solutions to complex adaptive systems of systems problems. 3

4 Power Law Behavior and Risk

5 The problem space is broad

6 What capabilities do we need in order to solve these problems? Modeling and analysis processes that account for the dynamics of human-technical-natural systems Causal relationships Condition dependent behavior Resource constraints Delays and the effects of delays on system viability and performance for uncertainties for risk reduction strategies Comparative analysis to identify solutions that are robust to uncertainty Decision maker confidence in the analysis and ability to implement the engineered solution Evaluation and improvement

Example Application of CASoS Engineering: Pandemic Planning Modeling and analysis processes that account for the dynamics of humantechnical-natural systems for uncertainties for risk reduction

7 Example Application of CASoS Engineering: Pandemic Planning Modeling and analysis processes that account for the dynamics of humantechnical-natural systems for uncertainties for risk reduction strategies Comparative analysis to identify solutions that are robust to uncertainty Decision maker confidence in the analysis and ability to implement the engineered solution Evaluation and improvement

8 Example Application of CASoS Engineering: Pandemic Planning Modeling and analysis processes that account for the dynamics of humantechnical-natural systems for uncertainties for risk reduction strategies Comparative analysis to identify solutions that are robust to uncertainty Decision maker confidence in the analysis and ability to implement the engineered solution Evaluation and improvement Example Teen T1 Household T1 Everyone Random Extended Family or Neighborhood T1 T1 Social Networks for Teen 1 Teen Random T1 School classes 6 per teen

9 Example Application of CASoS Engineering: Pandemic Planning Modeling and analysis processes that account for the dynamics of humantechnical-natural systems for uncertainties for risk reduction strategies Comparative analysis to identify solutions that are robust to uncertainty Decision maker confidence in the analysis and ability to implement the engineered solution Evaluation and improvement Latent Mean duration 1.25 days Transition Probabilities ps = 0.5 ph = 0.5 pm = 0 ps (1-pS) Infectious presymptomatic Mean duration 0.5 days I R 0.25 (1-pH) ph Infectious asymptomatic Mean duration 2 days I R 0.25 Infectious symptomatic Circulate Mean duration 1.5 days I R 1.0 for first 0.5 day, then reduced to for final day Infectious symptomatic Stay home Mean duration 1.5 days I R 1.0 for first 0.5 day, then reduced to for final day pm (1-pM) pm (1-pM) Dead Immune

10 Example Application of CASoS Engineering: Pandemic Planning Modeling and analysis processes that account for the dynamics of humantechnical-natural systems for uncertainties for risk reduction strategies Comparative analysis to identify solutions that are robust to uncertainty Decision maker confidence in the analysis and ability to implement the engineered solution Evaluation and improvement School closure, social distancing (adult/child), treatment, prophylaxis, quarantine, extended prophylaxis The best-performing intervention strategies include school closure early in the outbreak Child and teen social distancing is the next most important component (with school closure it reduces mean to 124 cases and the standard deviation to 14)

improvement Local Mitigation Strategies for Pandemic Influenza, RJ Glass, LM Glass, and WE Beyeler, SAND-2005-7955J (Dec, 2005).

11 Example Application of CASoS Engineering: Pandemic Planning Modeling and analysis processes that account for the dynamics of humantechnical-natural systems for uncertainties for risk reduction strategies Comparative analysis to identify solutions that are robust to uncertainty Decision maker confidence in the analysis and ability to implement the engineered solution Evaluation and improvement Local Mitigation Strategies for Pandemic Influenza, RJ Glass, LM Glass, and WE Beyeler, SAND J (Dec, 2005). Targeted Social Distancing Design for Pandemic Influenza, RJ Glass, LM Glass, WE Beyeler, and HJ Min, Emerging Infectious Diseases November, Design of Community Containment for Pandemic Influenza with Loki-Infect, RJ Glass, HJ Min WE Beyeler, and LM Glass, SAND P (Jan, 2007). Social contact networks for the spread of pandemic influenza in children and teenagers, LM Glass, RJ Glass, BMC Public Health, February, Rescinding Community Mitigation Strategies in an Influenza Pandemic, VJ Davey and RJ Glass, Emerging Infectious Diseases, March, Effective, Robust Design of Community Mitigation for Pandemic Influenza: A Systematic Examination of Proposed U.S. Guidance, VJ Davey, RJ Glass, HJ Min, WE Beyeler and LM Glass, PLoSOne, July, Pandemic Influenza and System of Systems (CASoS) Engineering, Glass, R.J., Proceedings of the 2009 International System Dynamics Conference, Albuquerque, New Mexico, July, Health Outcomes and Costs of Community Mitigation Strategies for an Influenza Pandemic in the U.S, Perlroth, Daniella J., Robert J. Glass, Victoria J. Davey, Alan M. Garber, Douglas K. Owens, Clinical Infectious Diseases, January, 2010.

at a retail location Network Congestion and Cascades in Payment Systems designing stabilizing solutions Dollars FX Euros

12 Other Applications Stochastic Mapping of Food Supply Chains Improving contaminant tracing (forward and backward) Backward Tracing of Contamination Conditional Probabilities: the probability contamination exists at a sprout company if detected at a retail location Network Congestion and Cascades in Payment Systems designing stabilizing solutions Dollars FX Euros Transportation Fuel Networks understanding risks and reducing them Changes in fuel production and flow due to capacity changes

13 Challenges Climate risks are global and will require an international community committed to reducing the risks. Develop a strong international community of practice of CASoS Engineering to find solutions that will benefit us all. Commitment and willingness to find ways to work together on common problems. Confidence in CAS modeling and analyses (a different approach to validation validate the engineered solutions) Analysis outcomes that demonstrate understanding of the potential dynamics Uncertainty explicitly represented Feasible solutions that are robust to uncertainty

Design of Community Containment for Pandemic Influenza

Design of Community Containment for Pandemic Influenza Robert J Glass*, H Jason Min, Walter E Beyeler andia National Laboratories and Laura M Glass Albuquerque Public High chool *rjglass@sandia.gov Design