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The code for this chapter is in the Jupyter notebook chap10.ipynb in the repository for this book. Open this notebook, read the code, and run the cells. You can use this notebook to work on the following exercises.
Exercise 1: To test whether the distributions of
S are heavy-tailed, we plotted their PMFs on a log-log scale, which is what Bak, Tang and Wiesenfeld show in their paper. But as we saw in Section 6.8, this visualization can obscure the shape of the distribution. Using the same data, make a plot that shows the cumulative distributions (CDFs) of
T. What can you say about their shape? Do they follow a power law? Are they heavy-tailed?
You might find it helpful to plot the CDFs on a log-x scale and on a log-log scale.
Exercise 2: In Section 10.6 we showed that the initial configuration of the sand pile model produces fractal patterns. But after we drop a large number of random grains, the patterns look more random.
Starting with the example in Section 10.6, run the sand pile model for a while and then compute fractal dimensions for each of the 4 levels. Is the sand pile model fractal in steady state?
Exercise 3: Another version of the sand pile model, called the “single source” model, starts from a different initial condition: instead of all cells at the same level, all cells are set to 0 except the center cell, which is set to a large value. Write a function that creates a SandPile object, sets up the single source initial condition, and runs until the pile reaches equilibrium. Does the result appear to be fractal?
Exercise 4: In their 1989 paper, Bak, Chen and Creutz suggest that the Game of Life is a self-organized critical system.
To replicate their tests, start with a random configuration and run the GoL CA until it stabilizes. Then choose a random cell and flip it. Run the CA until it stabilizes again, keeping track of
T, the number of time steps it takes, and
S, the number of cells affected. Repeat for a large number of trials and plot the distributions of
S. Also, estimate the power spectrums of
S as signals in time, and see if they are consistent with pink noise.
Exercise 5: In The Fractal Geometry of Nature, Benoit Mandelbrot proposes what he calls a “heretical” explanation for the prevalence of heavy-tailed distributions in natural systems. It may not be, as Bak suggests, that many systems can generate this behavior in isolation. Instead there may be only a few, but interactions between systems might cause the behavior to propagate.
To support this argument, Mandelbrot points out:
The distribution of observed data is often “the joint effect of a fixed underlying true distribution and a highly variable filter”.
Heavy-tailed distributions are robust to filtering; that is, “a wide variety of filters leave their asymptotic behavior unchanged”.
What do you think of this argument? Would you characterize it as reductionist or holist?
Exercise 6: Read about the “Great Man” theory of history. What implication does self-organized criticality have for this theory?