Abstract for: Doing more with Models: Illustration of a SD Approach for Dealing with Deeply Uncertain Issues

Many grand challenges are both dynamically complex and deeply uncertain. Combining System Dynamics with Exploratory Modeling and Analysis allows one to generate, explore, identify and analyze all sorts of plausible scenarios related to such issues, and design and test adaptive policies over many scenarios. This paper explains and illustrates different uses of the resulting computational System Dynamics approach by means of an applied case, the outbreak of a new flu strand like the 2009 A(H1N1)n flu. First, we illustrate the use of this approach for generating and exploring different types of plausible pandemic shocks. Second, we illustrate the use of machine learning techniques to analyze contributions and effects of uncertainties, and discover and select scenarios. Finally, we illustrate the use of this approach for supporting the design of robust adaptive policies in order to be prepared for any new flu outbreak, especially those that really require action.