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Think Complexity: Exploring Complexity Science with Python
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.
- Author
- Allen B. Downey
- Publisher
- Olin College
- Source
- LibreTexts
- License
- CC BY-NC-SA
- Language
- EN
- Category
- Information Technology
Contents· 120 sections
- 01: Complexity Science
- 1.01: The changing criteria of science
- 1.02: The axes of scientific models
- 1.03: Different models for different purposes
- 1.04: Complexity engineering
- 1.05: Complexity thinking
- 02: Graphs
- 2.01: What is a graph
- 2.02: NetworkX
- 2.03: Random graphs
- 2.04: Generating graphs
- 2.05: Connected graphs
- 2.06: Generating ER graphs
- 2.07: Probability of connectivity
- 2.08: Analysis of graph algorithms
- 2.09: Exercises
- 03: Small World Graphs
- 3.01: Stanley Milgram
- 3.02: Watts and Strogatz
- 3.03: Ring lattice
- 3.04: WS graphs
- 3.05: Clustering
- 3.06: Shortest path lengths
- 3.07: The WS experiment
- 3.08: What kind of explanation is that
- 3.09: Breadth-First Search
- 3.10: Dijkstras algorithm
- 3.11: Exercises
- 04: Scale-free networks
- 4.01: Social network data
- 4.02: WS Model
- 4.03: Degree
- 4.04: Heavy-tailed distributions
- 4.05: Barabasi-Albert model
- 4.06: Generating BA graphs
- 4.07: Cumulative distributions
- 4.08: Explanatory models
- 4.09: Exercises
- 05: Cellular Automatons
- 5.01: A simple CA
- 5.02: Wolframs experiment
- 5.03: Classifying CAs
- 5.04: Randomness
- 5.05: Determinism
- 5.06: Spaceships
- 5.07: Universality
- 5.08: Falsifiability
- 5.09: What is this a model of
- 5.10: Implementing CAs
- 5.11: Cross-correlation
- 5.12: CA tables
- 5.13: Exercises
- 06: Game of Life
- 6.01: Conways GoL
- 6.02: Life patterns
- 6.03: Conways conjecture
- 6.04: Realism
- 6.05: Instrumentalism
- 6.06: Implementing Life
- 6.07: Exercises
- 07: Physical modeling
- 7.01: Diffusion
- 7.02: Reaction-diffusion
- 7.03: Percolation
- 7.04: Phase change
- 7.05: Fractals
- 7.06: Fractals and Percolation Models
- 7.07: Exercises
- 08: Self-organized criticality
- 8.01: Critical Systems
- 8.02: Sand Piles
- 8.03: Implementing the Sand Pile
- 8.04: Heavy-tailed distributions
- 8.05: Fractals
- 8.06: Pink noise
- 8.07: The sound of sand
- 8.08: Reductionism and Holism
- 8.09: SOC causation and prediction
- 8.10: Exercises
- 09: Agent-based models
- 9.01: Schellings Model
- 9.02: Implementation of Schellings model
- 9.03: Segregation
- 9.04: Sugarscape
- 9.05: Wealth inequality
- 9.06: Implementing Sugarscape
- 9.07: Migration and Wave Behavior
- 9.08: Emergence
- 9.09: Exercises
- 10: Herds Flocks and Traffic Jams
- 10.01: Traffic jams
- 10.02: Random perturbation
- 10.03: Boids
- 10.04: The Boid algorithm
- 10.05: Arbitration
- 10.06: Emergence and free will
- 10.07: Exercises
- 11: Evolution
- 11.01: Simulating evolution
- 11.02: Fitness landscape
- 11.03: Agents
- 11.04: Simulation
- 11.05: No differentiation
- 11.06: Evidence of evolution
- 11.07: Differential survival
- 11.08: Mutation
- 11.09: Speciation
- 11.10: Summary
- 11.11: Exercises
- 12: Evolution of cooperation
- 12.01: Prisoners Dilemma
- 12.02: The problem of nice
- 12.03: Prisoners dilemma tournaments
- 12.04: Simulating evolution of cooperation
- 12.05: The Tournament
- 12.06: The Simulation
- 12.07: Results
- 12.08: Conclusions
- 12.09: Exercises
- Appendix A: Reading list