Chapter 1: What is complexity?

Chapter 2: Dynamics, Chaos, and Prediction

1. How did Galileo's experiments contradict Aristotle's ideas about motion?
2. In what way did Newton extend Galileo's ideas about motion?
3. How did chaos dash Laplace's dream of complete prediction?
4. Who is credited with the first study of chaos? What problem did he study?
5. Explain the difference between linear and nonlinear systems? Why is this important?
6. What are the possible final states for the logistic map? Be able to explain what they are.
7. What is Feigenbaum's constant? What does it have to do with phase transitions?

Chapter 3: Information

1. Be able to explain Maxwell's thought experiment. Why was it important?
2. Define entropy. How are enropy and information connected?
3. What is Boltzmann's big idea in statistical mechanics?
4. What is Boltzmann's restatement of the second law of thermodynamics?
5. How did Shannon extend these ideas?

Chapter 15: The Science of Networks

1. What is network thinking?
2. What is the small-world property?
3. What are the four "notable" properties of scale-free networks?
4. Do you think Google knew that the degree distribution for web pages followed a power law before instituting their page rank scheme? Why or why not?

Chapter 16: Applying Network Science to Real-World Networks

1. Why would an evolutionary process favor brain networks with a small-world property?
2. Why is a power-law degree distribution essential for gene regulatory networks?
3. What vaccination technique is proposed for an at-risk population? (assuming that hubs are unknown)
4. What reasons does the author give for the widespread presence of scale-free networks in natural systems?
5. Outline the three reasons some scientists are not jumping on the scale-free network bandwagon.
6. Think of an example in which a static network property predicts a dynamic network feature.

Chapter 17: The Mystery of Scaling

1. What is the scaling mystery in biology?
2. What is an organism's metabloic rate?
3. Explain Kleiber's 3/4ths power law.
4. What does the metabolic scaling theory attempt explain?
5. What is the connection between power laws and fractals?
6. What is the "space-filling" assumption for the circulatory system?
7. What does it mean for our circulatory system to be a fractal network that approximates a fourth dimension?
8. Why do dogs provide a counterexample to the quarter-power scaling laws?
9. What is Zipf's law? (note rank plot)
10. What were Mandelbrot and Simon's explanation for Zipf's law?

Chapter 18: Evolution, Complexified

1. What is the definition of a gene and how do they operate?
2. What is DNA methylation and why is it important?
3. What assumption did (do?) biotech companies make about how genes operate? Was it helpful?
4. What does the rock group "Devo" have to say about evolution?
5. What are genetic switches? How would Evo-devoists use them to explain complexity?
6. How do the experiments with beaks support the Evo-devoists position?
7. Describe Kaufman's random boolean networks?
8. What is Kaufman's proposed 4th law? Why is it important?

Chapter 19: The Past and Future of the Science of Complexity

1. What are Horgan's two main criticisms of Complexity?
2. Give an example of a general principle that is not useful.
3. At what is Norbert looking (page 296)?
4. Our author summarizes several attempts by prominent scientists to develop complexity theory (e.g. cybernetics and system theory). Why did they fail?
5. What current academic fields are an outgrowth of attempts to develop complexity theory?
6. What are the most significant contributions (so far) of complex systems research?
7. What branching does the author see in the future of complexity research?
8. What does Strogatz think is needed to move forward in a dramatic way with complexity research?