ISBN-10:
0262029499
ISBN-13:
9780262029490
Pub. Date:
11/13/2015
Publisher:
MIT Press
Analytical Methods for Dynamic Modelers

Analytical Methods for Dynamic Modelers

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Overview

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.

Contributors
Wenyi 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

Product Details

ISBN-13: 9780262029490
Publisher: MIT Press
Publication date: 11/13/2015
Series: The MIT Press
Edition description: New Edition
Pages: 448
Sales rank: 1,079,606
Product dimensions: 6.20(w) x 9.10(h) x 1.00(d)
Age Range: 18 Years

About the Author

Hazhir Rahmandad is Albert and Jeanne Clear Career Development Professor of Management and Assistant Professor of System Dynamics at the 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.

David Keith has worked near the interface between climate science, energy technology, and public policy for twenty years. He is currently the Gordon McKay Professor of Applied Physics in the School of Engineering and Applied Sciences (SEAS) at Harvard University and Professor of Public Policy at the Harvard Kennedy School.

Hazhir Rahmandad is Albert and Jeanne Clear Career Development Professor of Management and Assistant Professor of System Dynamics at the MIT Sloan School of Management.

Nathaniel D. Osgood is Associate Professor of Computer Science at the University of Saskatchewan.

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.

Table of Contents

Overview of Online Appendices ix

Foreword xi

Preface xiii

Introduction xv

I Estimation of Model Parameters 1

1 Parameter Estimation Through Maximum Likelihood and Bootstrapping Methods Jeroen Struben John Sterman David Keith 3

2 Using the Method of Simulated Moments for System Identification Mohammad S. Jalali Hazhir Rahmandad Hamed Ghoddusi 39

3 Simultaneous Linear Estimation using Structural Equation Modeling Peter S. Hovmand Nishesh Chalise 71

4 Working with Noisy Data: Kalman Filtering and State Resetting Robert Eberlein 95

5 Combining Markov Chain Monte Carlo Approaches and Dynamic Modeling Nathaniel D. Osgood Juxin Liu 125

II Model Analysis 171

6 Pattern Recognition for Model Testing, Calibration, and Behavior Analysis Gönenç Yücel Yaman Barlas 173

7 Linking Structure to Behavior Using Eigenvalue Elasticity Analysis Rogelio Oliva 207

III Decision Support and Optimization 241

8 An Introduction to Deterministic and Stochastic Optimization Erling Moxnes 243

9 Addressing Dynamic Decision Problems Using Decision Analysis and Simulation Nathaniel D. Osgood Karen Yee Wenyi An Wirtfried Grassmann 277

10 Using Decision Trees to Value Managerial Real Options Burcu Tan Edward G. Anderson 307

11 Optimal Control for Complex Systems Edward G. Anderson Nitin R. Joglekar 337

12 Modeling Competing Actors Using Differential Games Hazhir Rahmandad Raymond J. Spiteri 373

Contributors 405

Index 407

What People are Saying About This

David C. Lane FORS

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!

From the Publisher

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)

Endorsement

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)

John Morecroft

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.

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