"This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science: Data structures and algorithms: A data structure is a collection that contains data elements organized in a way that supports particular operations. For example,
Think Complexity
β Scribed by Allen Downey;
- Year
- 2018
- Tongue
- English
- Leaves
- 228
- Edition
- 2nd edition.
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
Preface
Who is this book for?
Changes from the first edition
Using the code
Complexity Science
The changing criteria of science
The axes of scientific models
Different models for different purposes
Complexity engineering
Complexity thinking
Graphs
What is a graph?
NetworkX
Random graphs
Generating graphs
Connected graphs
Generating ER graphs
Probability of connectivity
Analysis of graph algorithms
Exercises
Small World Graphs
Stanley Milgram
Watts and Strogatz
Ring lattice
WS graphs
Clustering
Shortest path lengths
The WS experiment
What kind of explanation is that?
Breadth-First Search
Dijkstra's algorithm
Exercises
Scale-free networks
Social network data
WS Model
Degree
Heavy-tailed distributions
BarabΓ‘si-Albert model
Generating BA graphs
Cumulative distributions
Explanatory models
Exercises
Cellular Automatons
A simple CA
Wolfram's experiment
Classifying CAs
Randomness
Determinism
Spaceships
Universality
Falsifiability
What is this a model of?
Implementing CAs
Cross-correlation
CA tables
Exercises
Game of Life
Conway's GoL
Life patterns
Conway's conjecture
Realism
Instrumentalism
Implementing Life
Exercises
Physical modeling
Diffusion
Reaction-diffusion
Percolation
Phase change
Fractals
Fractals and Percolation Models
Exercises
Self-organized criticality
Critical Systems
Sand Piles
Implementing the Sand Pile
Heavy-tailed distributions
Fractals
Pink noise
The sound of sand
Reductionism and Holism
SOC, causation, and prediction
Exercises
Agent-based models
Schelling's Model
Implementation of Schelling's model
Segregation
Sugarscape
Wealth inequality
Implementing Sugarscape
Migration and Wave Behavior
Emergence
Exercises
Herds, Flocks, and Traffic Jams
Traffic jams
Random perturbation
Boids
The Boid algorithm
Arbitration
Emergence and free will
Exercises
Evolution
Simulating evolution
Fitness landscape
Agents
Simulation
No differentiation
Evidence of evolution
Differential survival
Mutation
Speciation
Summary
Exercises
Evolution of cooperation
Prisoner's Dilemma
The problem of nice
Prisoner's dilemma tournaments
Simulating evolution of cooperation
The Tournament
The Simulation
Results
Conclusions
Exercises
Reading list
π SIMILAR VOLUMES
Expand your Python skills by working with data structures and algorithms in a refreshing contextβthrough an eye-opening exploration of complexity science. Whether youβre an intermediate-level Python programmer or a student of computational modeling, youβll delve into examples of complex systems thro
Enhances Python skills by working with data structures and algorithms and gives examples of complex systems using exercises, case studies, and simple explanations.
Complexity science uses computation to explore the physical and social sciences. In<i>Think Complexity</i>, you'll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.<br /><br />Whether you're an intermediate-level Python programmer or a student
<p>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
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 th