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Introduction to the Modeling and Analysis of Complex Systems
Introduction to the Modeling and Analysis of Complex Systems introduces students to mathematical/computational modeling and analysis developed in the emerging interdisciplinary field of Complex Systems Science. Complex systems are systems made of a large number of microscopic components interacting with each other in nontrivial ways. Many real-world systems can be understood as complex systems, where critically important information resides in the relationships between the parts and not necessarily within the parts themselves. This textbook offers an accessible yet technically-oriented introduction to the modeling and analysis of complex systems. The topics covered include: fundamentals of modeling, basics of dynamical systems, discrete-time models, continuous-time models, bifurcations, chaos, cellular automata, continuous field models, static networks, dynamic networks, and agent-based models. Most of these topics are discussed in two chapters, one focusing on computational modeling and the other on mathematical analysis. This unique approach provides a comprehensive view of related concepts and techniques, and allows readers and instructors to flexibly choose relevant materials based on their objectives and needs. Python sample codes are provided for each modeling example. This textbook is also available free online from the Open SUNY Textbooks website (http://textbooks.opensuny.org).
496 pages, Paperback
Published August 1, 2013
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Displaying 1 - 2 of 2 reviews
August 16, 2017
Great for getting started with practical modeling and analysis of Complex Systems
I read the book cover to cover as a self-study.
I found that having read more theoretical and general work previously helped a lot. This works requires learning three things at the same time; 1) Complex Systems, 2) the math that is used for modeling Complex Systems, and 3) Python (with some very specific packages and modules).
Juggling the three types of learning is challenging, but ultimately quite rewarding when you can get one of the examples working and tinker with it on your own.
Dr. Sayama recommends using Anaconda (specifically Spyder), while these seem like great packages, I had a hard time making them work correctly on my system. Instead went for plain vanilla Python with needed packages added with PIP, which worked fine.
As some others have noted, this work is available online. I bought this version as I find reading textbooks on paper works better for me.
I read the book cover to cover as a self-study.
I found that having read more theoretical and general work previously helped a lot. This works requires learning three things at the same time; 1) Complex Systems, 2) the math that is used for modeling Complex Systems, and 3) Python (with some very specific packages and modules).
Juggling the three types of learning is challenging, but ultimately quite rewarding when you can get one of the examples working and tinker with it on your own.
Dr. Sayama recommends using Anaconda (specifically Spyder), while these seem like great packages, I had a hard time making them work correctly on my system. Instead went for plain vanilla Python with needed packages added with PIP, which worked fine.
As some others have noted, this work is available online. I bought this version as I find reading textbooks on paper works better for me.
February 4, 2016
A colleague of mine recommended this book saying: "at first, I would have said is a very very good textbook for a dynamical systems class. After using it, still think is good, but maybe not superb". Right now, after reading half of it, I'm still in the first stage. I'll use it as basic bibliography, complemented by Strogatz' nonlinear textbook.
I skipped the sections of networks.
I skipped the sections of networks.
Displaying 1 - 2 of 2 reviews