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Think Complexity: Complexity Science and Computational Modeling

Overview

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 explanations.

You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced ...

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Think Complexity: Complexity Science and Computational Modeling

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Overview

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 explanations.

You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

  • Work with NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables
  • Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines
  • Get starter code and solutions to help you re-implement and extend original experiments in complexity
  • Explore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topics
  • Examine case studies of complex systems submitted by students and readers
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Product Details

  • ISBN-13: 9781449314637
  • Publisher: O'Reilly Media, Incorporated
  • Publication date: 3/14/2012
  • Edition number: 1
  • Pages: 142
  • Sales rank: 934,075
  • Product dimensions: 6.90 (w) x 9.10 (h) x 0.50 (d)

Meet the Author

Allen Downey is an Associate Professor of Computer Science at the Olin College of Engineering. He has taught computer science at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Master’s and Bachelor’s degrees from MIT.

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Table of Contents

Preface

Chapter 1: Complexity Science

Chapter 2: Graphs

Chapter 3: Analysis of Algorithms

Chapter 4: Small World Graphs

Chapter 5: Scale-Free Networks

Chapter 6: Cellular Automata

Chapter 7: Game of Life

Chapter 8: Fractals

Chapter 9: Self-Organized Criticality

Chapter 10: Agent-Based Models

Chapter 11: Case Study: Sugarscape

Chapter 12: Case Study: Ant Trails

Chapter 13: Case Study: Directed Graphs and Knots

Chapter 14: Case Study: The Volunteer’s Dilemma

Call for Submissions

Reading List

Colophon

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  • Posted April 3, 2012

    This one is not an easy one. Allen guides you through the variou

    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 process and refers not only to computer science but to philosophy and mathematics as well. Even if you won’t be able to solve all the presented puzzles it is still worth getting through the book.

    Few remarks regarding what I really liked in the book. First of all, Allen provides you with lots of references. So, if you are interested in particular topic, you have plenty of sources to start with. Secondly, Allen provides you with references to Wikipedia very often. This is not regarded usually as a good source among “university like people”, however I like this kind of approach a lot.

    Was this review helpful? Yes  No   Report this review
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