Think Complexity

Description

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

CONTENTS:

Preface; Why I Wrote This Book; Suggestions for Teachers; Suggestions for Autodidacts; Contributor List; Conventions Used in This Book; Using Code Examples; Safari(R) Books Online; How to Contact Us; Chapter 1: Complexity Science; 1.1 What Is This Book About?; 1.2 A New Kind of Science; 1.3 Paradigm Shift?; 1.4 The Axes of Scientific Models; 1.5 A New Kind of Model; 1.6 A New Kind of Engineering; 1.7 A New Kind of Thinking; Chapter 2: Graphs; 2.1 What’s a Graph?; 2.2 Representing Graphs; 2.3 Random Graphs; 2.4 Connected Graphs; 2.5 Paul Erdos: Peripatetic Mathematician, Speed Freak; 2.6 Iterators; 2.7 Generators; Chapter 3: Analysis of Algorithms; 3.1 Order of Growth; 3.2 Analysis of Basic Python Operations; 3.3 Analysis of Search Algorithms; 3.4 Hashtables; 3.5 Summing Lists; 3.6 pyplot; 3.7 List Comprehensions; Chapter 4: Small World Graphs; 4.1 Analysis of Graph Algorithms; 4.2 FIFO Implementation; 4.3 Stanley Milgram; 4.4 Watts and Strogatz; 4.5 Dijkstra; 4.6 What Kind of Explanation Is That?; Chapter 5: Scale-Free Networks; 5.1 Zipf’s Law; 5.2 Cumulative Distributions; 5.3 Continuous Distributions; 5.4 Pareto Distributions; 5.5 Barabási and Albert; 5.6 Zipf, Pareto, and Power Laws; 5.7 Explanatory Models; Chapter 6: Cellular Automata; 6.1 Stephen Wolfram; 6.2 Implementing CAs; 6.3 CADrawer; 6.4 Classifying CAs; 6.5 Randomness; 6.6 Determinism; 6.7 Structures; 6.8 Universality; 6.9 Falsifiability; 6.10 What Is This a Model Of?; Chapter 7: Game of Life; 7.1 Implementing Life; 7.2 Life Patterns; 7.3 Conway’s Conjecture; 7.4 Realism; 7.5 Instrumentalism; 7.6 Turmites; Chapter 8: Fractals; 8.1 Fractal CAs; 8.2 Percolation; Chapter 9: Self-Organized Criticality; 9.1 Sand Piles; 9.2 Spectral Density; 9.3 Fast Fourier Transform; 9.4 Pink Noise; 9.5 Reductionism and Holism; 9.6 SOC, Causation, and Prediction; Chapter 10: Agent-Based Models; 10.1 Thomas Schelling; 10.2 Agent-Based Models; 10.3 Traffic Jams; 10.4 Boids; 10.5 Prisoner’s Dilemma; 10.6 Emergence; 10.7 Free Will; Chapter 11: Case Study: Sugarscape; 11.1 The Original Sugarscape; 11.2 The Occupy Movement; 11.3 A New Take on Sugarscape; 11.4 Taxation and the Leave Behind; 11.5 The Gini Coefficient; 11.6 Results with Taxation; 11.7 Conclusion; Chapter 12: Case Study: Ant Trails; 12.1 Introduction; 12.2 Model Overview; 12.3 API Design; 12.4 Sparse Matrices; 12.5 wx; 12.6 Applications; Chapter 13: Case Study: Directed Graphs and Knots; 13.1 Directed Graphs; 13.2 Implementation; 13.3 Detecting Knots; 13.4 Knots in Wikipedia; Chapter 14: Case Study: The Volunteer’s Dilemma; 14.1 The Prairie Dog’s Dilemma; 14.2 Analysis; 14.3 The Norms Game; 14.4 Results; 14.5 Improving the Chances; Call for Submissions; Reading List; Colophon;