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Think Complexity: Complexity Science and Computational Modeling 1st Edition
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More About the Author
Allen is an avid runner, gardener and cook. He ran the Boston Marathon for the first time in 2011, finishing in 3:45. Allen lives in Needham, MA with his wife, two daughters, and two cats.
Top Customer Reviews
Allen B. Downey's well-written new book can help you dive into complexity science and improve your Python skills along the way. It's not just another hello-world, learn-to-program-in-Python text.
"This book," Downey states, "is about data structures and algorithms, intermediate programming in Python, computational modeling, and the philosophy of science." Hello, NEW world.
His new work, he adds, sprang out of a blending of "boredom and fascination: boredom with the usual presentation of data structures and algorithms and fascination with complex systems. The problem with data structures is that they are often taught without a motivating context; the problem with complexity science is that it usually is not taught at all."
Complexity science is the scientific study of complex systems - which can be anything from computer networks to the human brain, global markets, ecosystems, metropolitan areas, space shuttles, ant trails, and so forth. Complexity science is practiced "at the intersection of mathematics, computer science, and natural science," Downey says.
How does "the philosophy of science" fit into Downey's book? "Think Complexity" offers "experiments and results [that] raise questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and epistemology."
Downey's new work "picks up where Think Python left off" and is intended to appeal to the "broad intellectual curiosity" of software engineers and their "drive to expand their knowledge and skills.Read more ›
It was interesting how the author organized the idea shift in scientific thinking of the complexity science. If one is familiar with the works like Malcolm Gladwell in "Blink", "Outliers" or similarly in "Freakonomics" one can clearly related to the method of using simulation-based computational model to solve problems that are non-linear with large composite, many-to-many elements. Many of the TED talks I have seen also employed this line of method in arriving at their respective conclusions.
The middle section of the book introduced various models and approaches into solving complex problems. I absolutely love the fact that the theories were broken down into small pieces of problems that can be illustrated by small Python programs. Of the examples, the sections on Dijkstra algorithm and scale-free networks were the most interesting to me. As network engineer whom have dealt with OSPF and IS-IS on regular basis, I never thought it was possible to simulate the algorithm via Python.Read more ›
So when I concede myself the luxury of buying a "real" book, I expect it to be something that I can enjoy sitting on a sofa or in bed, as a stand alone item.
This book is certainly an interesting read for the topics it examines, however it completely fails on my requirements. There is not a single page in which the author is not asking the reader to go check a wikipedia page, download a scientific paper or go examine a piece of code available on the book's companion website.
This leaves the reader two choices: either do what the author is asking, sacrificing what should have been a reading session for yet another go of clicks and scrolls or (what i did) just ignore the suggestions. This will obviously make it more difficult to follow the line of thought, especially because the author many times is posing questions which have no answer in the book itself. So if you don't do the homework you never get the answer!
Overall the continuous referencing to external resources has left the feeling in me that this piece of work could have been a stimulating and interesting one if only the author had put in it the extra effort to make it a self standing reading. He could still have provided links to external resources, but only as optional.
In the end I don't recommend it unless you are really committed to following the author's path, which may be more doable for a college course type of reader than for a casual one like myself.
Most Recent Customer Reviews
This wonderful little book covers topics that every computer scientist ought to know -- and will *want* to know -- but that are not covered in the standard CS sequence of studies. Read morePublished 16 months ago by bearieq
It is a small compact book that gently introduces complexity sciences. I have picked through this book over the last year and rather enjoy its intermittent injection of topics like... Read morePublished 22 months ago by michaelDubs
This is a book with very nice examples that can be adapted immediately to class room use. With all books with Python code, it suffers from the fast development of Python. Read morePublished on December 25, 2013 by Thomas J. E. Schwarz
I love Professor Downey's books. He departs from traditional Computer Science to work with interesting applications and to provide materials that supports the reader's... Read morePublished on August 25, 2013 by seapwc
My background is in mathematical physics and I am currently in the process of obtaining a Masters degree in applied mathematics. This book was written for me. Read morePublished on July 12, 2013 by L. Bovard
Beware of "free" Kindle App. I wanted this book but decided to get the Kindle version for $13.95. I don't have a Kindle but the Kindle App is free so I downloaded that. Read morePublished on February 10, 2013 by unhappy
I've found this book extremely interesting. First of all, it actually maps concepts from the Mathematical and Computer worlds to the real world problems. Read morePublished on June 21, 2012 by dan.morgantini
This was a difficult book to read! For the most part, it is a textbook introducing the field of Complexity Science and explaining some fundamental Computer Science topics. Read morePublished on April 30, 2012 by G. van Staden
I bought this book to basically understand how Google and Facebook model their internal data and have algorithms make sense of it, to understand how any real life observation could... Read morePublished on April 25, 2012 by Sachinrao Panemangalore