Think complexity / Allen B. Downey ; [editors, Mike Loukides and Meghan Blanchette ; illustrator, Robert Romano].

Format:

Book

Author/Creator:

Downey, Allen.

Contributor:

Loukides, Michael Kosta.

Blanchette, Meghan.

Romano, Robert.

Language:

English

Subjects (All):

Complexity (Philosophy).

Science--Philosophy.

Science.

Physical Description:

1 online resource (158 p.)

Edition:

1st ed.

Place of Publication:

Sebastopol, Calif. : O'Reilly, 2012.

Language Note:

English

System Details:

text file

Summary:

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 an

Contents:

Table of Contents; Preface; Why I Wrote This Book; Suggestions for Teachers; Suggestions for Autodidacts; Contributor List; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Chapter 1. Complexity Science; What Is This Book About?; A New Kind of Science; Paradigm Shift?; The Axes of Scientific Models; A New Kind of Model; A New Kind of Engineering; A New Kind of Thinking; Chapter 2. Graphs; What's a Graph?; Representing Graphs; Random Graphs; Connected Graphs; Paul Erdős: Peripatetic Mathematician, Speed Freak; Iterators; Generators

Chapter 3. Analysis of AlgorithmsOrder of Growth; Analysis of Basic Python Operations; Analysis of Search Algorithms; Hashtables; Summing Lists; pyplot; List Comprehensions; Chapter 4. Small World Graphs; Analysis of Graph Algorithms; FIFO Implementation; Stanley Milgram; Watts and Strogatz; Dijkstra; What Kind of Explanation Is That?; Chapter 5. Scale-Free Networks; Zipf's Law; Cumulative Distributions; Continuous Distributions; Pareto Distributions; Barabási and Albert; Zipf, Pareto, and Power Laws; Explanatory Models; Chapter 6. Cellular Automata; Stephen Wolfram; Implementing CAs

CADrawerClassifying CAs; Randomness; Determinism; Structures; Universality; Falsifiability; What Is This a Model Of?; Chapter 7. Game of Life; Implementing Life; Life Patterns; Conway's Conjecture; Realism; Instrumentalism; Turmites; Chapter 8. Fractals; Fractal CAs; Percolation; Chapter 9. Self-Organized Criticality; Sand Piles; Spectral Density; Fast Fourier Transform; Pink Noise; Reductionism and Holism; SOC, Causation, and Prediction; Chapter 10. Agent-Based Models; Thomas Schelling; Agent-Based Models; Traffic Jams; Boids; Prisoner's Dilemma; Emergence; Free Will

Chapter 11. Case Study: SugarscapeThe Original Sugarscape; The Occupy Movement; A New Take on Sugarscape; Pygame; Taxation and the Leave Behind; The Gini Coefficient; Results with Taxation; Conclusion; Chapter 12. Case Study: Ant Trails; Introduction; Model Overview; API Design; Sparse Matrices; wx; Applications; Chapter 13. Case Study: Directed Graphs and Knots; Directed Graphs; Implementation; Detecting Knots; Knots in Wikipedia; Chapter 14. Case Study: The Volunteer's Dilemma; The Prairie Dog's Dilemma; Analysis; The Norms Game; Results; Improving the Chances

Appendix A. Call for SubmissionsAppendix B. Reading List; Index

Notes:

"Exploring complexity science with Python"--Cover.

Includes index.

Includes bibliographical references (p. 133-134) and index.

Description based on print resource

ISBN:

9781449331696

1449331696

9781449331702

144933170X

OCLC:

779828648