Complexity Explorer Santa Few Institute

Fractals and Scaling (2017)

Lead instructor:

This course is no longer in session.

6.9 Additional Resources » Additional resources

Newman, Mark EJ. "Power laws, Pareto distributions and Zipf's law."Contemporary physics 46.5 (2005): 323-351.  A very clear review article.  Includes around a dozen examples of empirical power laws.  Discusses basic mathematical properties of power laws and a nice overivew of different processes that generate power laws.  [pdf]

Reed, William J., and Barry D. Hughes. "From gene families and genera to incomes and internet file sizes: Why power laws are so common in nature."Physical Review E 66.6 (2002): 067103.  [pdf]

Mitzenmacher, Michael. "A brief history of generative models for power law and lognormal distributions." Internet Mathematics 1.2 (2004): 226-251.  [pdf]

Fox Keller, Evelyn. "Revisiting “scale‐free” networks." BioEssays 27.10 (2005): 1060-1068. A critical look at the flurry of interest in 'scale-free' networks in the late 1990s and early 2000s.  Highly recommended.  [pdf]

Stumpf, Michael PH, and Mason A. Porter. "Critical truths about power laws." Science 335.6069 (2012): 665-666. A short piece taking stock of recent work in power laws, pointing out that the statistical evidence for the existence of some power laws is fairly weak.  [pdf]

Limpert, Eckhard, Werner A. Stahel, and Markus Abbt. "Log-normal Distributions across the Sciences." BioScience 51.5 (2001): 341-352.  [pdf]

Easley, David, and Jon Kleinberg. Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press, 2010. An excellent, accessible book on various aspects of networks. A pre-publication draft of the book is available as a pdf at https://www.cs.cornell.edu/home/kleinber/networks-book/.  A basic look at power laws and rich-get-richer models is found in Chapter 18.

Newman, Mark. Networks: An Introduction. Oxford University Press, 2010. [amazon] Chapter 14 covers preferential attachment (rich-get-richer) models of network growth. Many more mathematical details than in Easley and Kleinberg's book.

Duncan J. Watts, "Computational Social Science: Exciting Progress and Future Challenges." A talk given at Columbia University. Available on youtube at: https://www.youtube.com/watch?v=KOoeXzqfO7s. The discussion of the Music Lab starts around 50:00.

Papers and data from the Music Lab experiment can be found here: http://www.princeton.edu/~mjs3/musiclab.shtml

A short overview of highly optimized tolerance is here.  And here is a collection of papers, with pdfs, on highly optimized tolerance and related work.  The original paper is Carlson, Jean M., and John Doyle. "Highly optimized tolerance: A mechanism for power laws in designed systems." Physical Review E 60.2 (1999): 1412.  [pdf]

Caldarelli, Guido. Scale-free networks: complex webs in nature and technology.  Oxford University Press (2007).  [amazon]