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Think Complexity: Complexity Science and Computational Modeling
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Think Complexity: Complexity Science and Computational Modeling

3.83 of 5 stars 3.83  ·  rating details  ·  76 ratings  ·  8 reviews
Expand your Python skills by working with data structures and algorithms in a refreshing context—through an eye-opening exploration of complexity science. Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of exercises, case studies, and easy-to-understand explanation ...more
Paperback, 160 pages
Published March 9th 2012 by O'Reilly Media (first published January 1st 2012)
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A great read for anyone interested in Python or, more importantly, in how one might use Python or some other programming language to model such things as groups of agents exhibiting intelligent-seeming behaviour (my favourite), or fractals, or to use readily-available Python packages to easily construct competent graphs. Some not insignificant mathematical ability is required to fully appreciate some of what is discussed: manipulation of equations, logarithms and so on. However, I felt I still g ...more
In operations research, among modelers it is a truism that models are for insights, not numbers. And the ability to provide insight is even more important than the ability to provide proofs that the model is correct or that the methodology is efficient or accurate. Think Complexity is an introduction to computational modeling for the purposes of finding this insight in areas that defy proof techniques.

Complexity science is an area that is easy to hype. I admit to being highly skeptical when a st
M Sheik Uduman Ali
It is almost 2 months for me to go through this book. Allen B Downey chooses 5 complex structures that we usually rely on third party libraries. These are: Graph, Scale-free Networks, Cellular Automata and fractals.

It is quite interesting to see complex algorithm explained using Python.

Allen started explaining what is Complex system with the example of how can you explain 'why planets are elliptical'. Interestingly he explained how people choose hard way with differential equation and another si
m ko
This one is not an easy one. Allen guides you through the various, complex, algorithms and data structures. This book is not for a beginners – you have to know Python already to solve exercises presented by author. The complexity of the book itself is also rather for slightly advanced developers. If you just start your journey with Python development it may be hard to follow.

What I liked, however, is the way Allen presents the material. He tries to show you different aspects of the development p
Neal Aggarwal
Fabulous book. The example code really gets driven home if you key it all in and struggle to understand the math. There is quite a bit of math here folks, remember that. All can be researched on-line though and the book extensively refers to Wikipedia which is fine for the likes of autodidacts like me.
Good book! A programmatic tour of complexity.
Ray Pace
I found it interesting for a quick thought about some complex problems addessed ina simple model.
Sergey Leschenko
Nice idea, but I don't like the implementation.
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