Analytical Methods for Dynamic Modelers

by Rahmandad, Oliva, Osgood, Richardson

ISBN: 9780262331432 | Copyright 2015

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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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Contents (pg. vii)
Overview of Online Appendices (pg. ix)
Foreword (pg. xi)
Preface (pg. xiii)
Introduction (pg. xv)
I Estimation of Model Parameters (pg. 1)
1 Parameter Estimation Through Maximum Likelihood and Bootstrapping Methods (pg. 3)
2 Using the Method of Simulated Moments for System Identification (pg. 39)
3 Simultaneous Linear Estimation Using Structural Equation Modeling (pg. 71)
4 Working with Noisy Data (pg. 95)
5 Combining Markov Chain Monte Carlo Approaches and Dynamic Modeling (pg. 125)
II Model Analysis (pg. 171)
6 Pattern Recognition for Model Testing, Calibration, and Behavior Analysis (pg. 173)
7 Linking Structure to Behavior Using Eigenvalue Elasticity Analysis (pg. 207)
III Decision Support and Optimization (pg. 241)
8 An Introduction to Deterministic and Stochastic Optimization (pg. 243)
9 Addressing Dynamic Decision Problems Using Decision Analysis and Simulation (pg. 277)
10 Using Decision Trees to Value Managerial Real Options (pg. 307)
11 Optimal Control for Complex Systems (pg. 337)
12 Modeling Competing Actors Using Differential Games (pg. 373)
Contributors (pg. 405)
Index (pg. 407)
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