Doing more with Models: Illustration of a SD Approach for exploring deeply uncertain issues, analyzing models, and designing adaptive robust policies Erik Pruyt * , Jan H. Kwakkel & Caner Hamarat Delf University of Technology July 2013 Abstract 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. Introduction In terms of applications, our research team addresses grand challenges and important issues which are characterized by high degrees of dynamic complexity and deep uncertainty. In this paper we illustrate how developments in sciences involved in model-based decision support can be combined with System Dynamics (SD) modeling and simulation (Forrester 1961; Sterman 2000). Combining them is useful for addressing the combined challenge of dynamic complexity and deep uncertainty through generation, exploration and analysis of many plausible scenarios 1 and through robust optimization of adaptive policies. The remainder of this paper is structured as follows. First we define deep uncertainty and introduce Exploratory Modeling and Analysis for dealing with deep uncertainty, as well as its combination with SD modeling, for dealing with deeply uncertain dynamically complex issues. Then we use a single case to illustrate multiple uses of this approach, more specifically (i) open exploration, (ii) advanced analysis using machine learning algorithms, (iii) open scenario discovery and selection, (iv) directed scenario discovery and selection, (v) adaptive policy design, robust optimization and regret analysis, and (vi) model testing (verification and validation). The case used to illustrate this computational SD approach is the ‘A(H1N1)n’ case –the 2009-2010 flu pandemic. This case was chosen mainly for explanatory reasons: the model is relatively simple (a small SD101 simulation model 2 ), the case is easily understandable (everyone is familiar with the 2009-2010 pandemic), and is used in the tutorial on our web site 3 which explains how to use our * Corresponding author: Erik Pruyt, Delft University of Technology, Faculty of Technology, Policy and Manage- ment, Policy Analysis Section; P.O. Box 5015, 2600 GA Delft, The Netherlands – E-mail: e.pruyt@tudelft.nl 1 In this paper we use the word ‘scenario’ for the time evolutionary behavior of a simulation run or computational experiment which is a combination of specific instantiations of uncertainties. 2 This case is available in the ‘small System Dynamics Models for BIG Issues’ case book (Pruyt 2013) available for free at http://simulation.tbm.tudelft.nl. 3 The tutorial is available at http://simulation.tbm.tudelft.nl/ema-workbench/tutorial.html and an older version of the EMA workbench is available for free at http://simulation.tbm.tudelft.nl/ema-workbench/download.html. 1