“Think Complexity is about data structures and algorithms, intermediate programming in Python, computational modeling and the philosophy of science.
After reading the material, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.
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 worked examples, exercises, case studies, and easy-to-understand explanations.
Topics covered include:
Graphs including random and connected graphs.
Analysis of algorithms – the branch of computer science that considers the performance of algorithms.
Small world graphs.
Scale-free networks: Zipf’s law, cumulative, continuous and Pareto distributions.
Cellular automata.
Game of Life.
Fractals.
Self-organized criticality.
Case studies.”