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    Read more about Think Complexity: Exploring Complexity Science with Python - 2e

    Think Complexity: Exploring Complexity Science with Python - 2e

    (1 review)

    Allen B. Downey, Franklin W. Olin College of Engineering

    Copyright Year:

    ISBN 13: 9781449314637

    Publisher: Green Tea Press

    Language: English

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    Reviewed by Stephen Davies, Associate Professor, University of Mary Washington on 5/13/19

    It's difficult to be truly "comprehensive" in the field of Complexity, since it is so broad and so (relatively) new. An introductory text would do best to give the flavor of the field and enough interesting applications to pique the reader's... read more

    Table of Contents

    • Preface
    • 1 Complexity Science
    • 2 Graphs
    • 3 Small world graphs
    • 4 Scale-free networks
    • 5 Cellular Automatons
    • 6 Game of Life
    • 7 Physical modeling
    • 8 Self-organized criticality
    • 9 Agent-based models
    • 10 Herds, Flocks and Traffic Jams
    • 11 Evolution
    • 12 Evolution of cooperation
    • 14 Case study: The Volunteer's Dilemma

    Ancillary Material

    • Green Tea Press
    • About the Book

      Complexity Science is an interdisciplinary field—at the intersection of mathematics, computer science, and natural science—that focuses on discrete models of physical and social systems. In particular, it focuses on complex systems, which are systems with many interacting components.

      Complex systems include networks and graphs, cellular automatons, agent-based models and swarms, fractals and self-organizing systems, chaotic systems and cybernetic systems.

      This book is primarily about complexity science, but studying complexity science gives you a chance to explore topics and ideas you might not encounter otherwise, practice programming in Python, and learn about data structures and algorithms.

      This book picks up where Think Python leaves off. I assume that you have read that book or have equivalent knowledge of Python. As always, I try to emphasize fundamental ideas that apply to programming in many languages, but along the way you will learn useful features that are specific to Python.

      The models and results in this book raise a number of questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and Bayesian epistemology.

      About the Contributors


      Allen B. Downey is an American computer scientist, Professor of Computer Science at the Franklin W. Olin College of Engineering and writer of free textbooks.

      Downey received in 1989 his BS and in 1990 his MA, both in Civil Engineering from the Massachusetts Institute of Technology, and his PhD in Computer Science from the University of California at Berkeley in 1997.

      He started his career as Research Fellow in the San Diego Supercomputer Center in 1995. In 1997 he became Assistant Professor of Computer Science at Colby College, and in 2000 at Wellesley College. He was Research Fellow at Boston University in 2002 and Professor of Computer Science at the Franklin W. Olin College of Engineering since 2003. In 2009-2010 he was also Visiting Scientist at Google Inc.

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