Analytical Methods for Dynamic Modelers (The MIT Press) by Hazhir Rahmandad (PDF)

Description

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

User’s Reviews

Editorial Reviews: Review 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) 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.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐This is a much awaited book which gathers techniques that have been developed around System Dynamics modeling. It delivers in technical depth and opens the field of available tools for experienced dynamic modelers.This is not a beginners’ book, and requires some knowledge of system dynamics modeling. The available supplement files are a very efficient way of understanding the development of the examples. These files sometimes require access to other proprietary software (e.g., MATLAB, MATHEMATICA).Overall a must-have for any serious system dynamic modeler.

⭐This is a wonderful book for all those who want to increase their skills in modeling and simulation. It was written by masters, is a powerful set of instruments, and will be a welcome source of knowledge, for the community of modelers at large. The book makes a good read, but it is much more than that: it is a blueprint for better models. Best value!!

⭐”Learning all the relevant analytical tools will require taking several graduate-level courses in different departments, few of which directly focus on dynamics of social systems” – I can’t say how much I believe this quotation from the introduction of “Analytical Methods for Dynamic Modelers” to be spot on. Becoming a proficient dynamic modeler who is abreast with the state of the art in his “craft” is like going on a journey that never ends. Jay Forrester, I believe, used to introduce himself as a “student of system dynamics” rather than as a master.While the journey still will never quite end, one might as of now at least not waste time with navigation and finding the way to proceed in one’s studies – thanks to this very useful collection of introductory articles spanning three losely separated domains: estimation of parameters, model analysis and decision support/optimization.For anybody who wants to go beyond tautologies like “all models are wrong” or “regulators need to have requisite variety” or “system’s thinking” in its more esoteric forms this is much needed reading (one wants to ask: “what took you guys so long?”). Very highly recommended!PS: It is interesting to see that when it comes to advanced methods Vensim still seems to be the SD-tool of choice. But to really go forward there is – as a look into the book will tell — no alternative to using advanced scientific programming tools like Mathematica, Matlab or R. Hopefully this is going to be acknowledged by those who design SD-courses.

⭐It is a very good book.

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