A user-friendly introduction to some of the most useful analytical tools for model building, estimation, and analysis, presenting key methods and examples.
Simulation modeling is increasingly integrated into research and policy analysis of complex sociotechnical systems in a variety of domains. Model-based analysis and policy design inform a range of applications in fields from economics to engineering to health care. This book offers a hands-on introduction to key analytical methods for dynamic modeling. Bringing together tools and methodologies from fields as diverse as computational statistics, econometrics, and operations research in a single text, the book can be used for graduate-level courses and as a reference for dynamic modelers who want to expand their methodological toolbox.
The focus is on quantitative techniques for use by dynamic modelers during model construction and analysis, and the material presented is accessible to readers with a background in college-level calculus and statistics. Each chapter describes a key method, presenting an introduction that emphasizes the basic intuition behind each method, tutorial style examples, references to key literature, and exercises. The chapter authors are all experts in the tools and methods they present. The book covers estimation of model parameters using quantitative data; understanding the links between model structure and its behavior; and decision support and optimization. An online appendix offers computer code for applications, models, and solutions to exercises.
ContributorsWenyi An, Edward G. Anderson Jr., Yaman Barlas, Nishesh Chalise, Robert Eberlein, Hamed Ghoddusi, Winfried Grassmann, Peter S. Hovmand, Mohammad S. Jalali, Nitin Joglekar, David Keith, Juxin Liu, Erling Moxnes, Rogelio Oliva, Nathaniel D. Osgood, Hazhir Rahmandad, Raymond Spiteri, John Sterman, Jeroen Struben, Burcu Tan, Karen Yee, Gönenç Yücel
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Hazhir Rahmandad is Associate Professor of Industrial and Systems Engineering at Virginia Tech and Visiting Associate Professor at MIT Sloan School of Management. Rogelio Oliva is Associate Professor and Ford Faculty Fellow in the Department of Information and Operations Management at Mays Business School at Texas A&M University. Nathaniel D. Osgood is Associate Professor of Computer Science at the University of Saskatchewan.Review:
With contributions from an array of skilled modelers, this carefully edited collection presents the state of the art in computational methods for model calibration, estimation, behavior analysis, and optimization. Much more than a survey, the book provides an intuitive introduction to each method; worked-through examples; and a glimpse of the history behind key ideas. All serious users of simulation models, whether graduate students or professionals, can learn from this timely volume.(John Morecroft, Senior Fellow, Management Science and Operations, London Business School)
Impressive in the diversity of analytical approaches it brings to bear. This collection adroitly extends the capabilities of the system dynamics field, making dynamic modeling more credible, more powerful, more compelling, and, ultimately, more relevant for policy analysis. A significant and unique contribution to the literature. Admirable!(David C. Lane FORS, Professor of Business Informatics, Henley Business School; winner of the Jay Wright Forrester Award (2007) and of the Operational Research Society's President's Medal (2014))
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