The Computational Beauty of Nature
Computer Explorations of Fractals, Chaos,
Complex Systems, and Adaptation

About the Book
  · title page
  · home*
  · cover artwork
  · jacket text
  · table of contents
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Book Contents
  · three themes
  · part synopses
  · selected excerpts
  · all figures from book
  · quotes from book
  · glossary from book
  · bibliography
  · slide show
Source Code
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  · download source code
  · java applets
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  · reviews & awards
  · errata
  · for educators
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Three Themes

Basically, there are three key ideas that keep reappearing throughout the book. Each idea is relatively simple in principle, but all three must be presented within the context of the entire book in order to be appreciated since all of them make some reference to the contents of the five book parts: computation, fractals, chaos, complex systems, and adaptation.

Taken in order, these three ideas roughly build on one another, so it helps to consider them in the following order:

1. The whole is greater than the sum of the parts
By this I mean that the topics of computation, fractals, chaos, complex systems, and adaptation are far more interesting considered together than by themselves. Each of the topics is related to the others in a non-trivial way; moreover, each can be seen as the result of a few simple principles such as recursion, parallelism, and nonlinearity.

2. The interesting stuff is in the middle
``Beauty,'' i.e., that which makes something interesting, is related to a mixture of regularity and irregularity. When things are too regular, we usually find them to be uninteresting because they yield no surprises for us. Complementary to this, highly irregular things are often uninteresting because they make no sense. In the middle, between regularity and irregularity, lies a place where things can be understood, but not completely.

3. Science is doomed to uncertainty---but this is a good thing
The interesting things mentioned above are often found to have computationally profound qualities. For instance, even if you had a perfect model or theory of how something worked, chances are that it would still be impossible to perfectly predict the future of that which you have modeled. A related result guarantees just the opposite: regardless of how much ``data'' one collects, it's not always possible to build a perfect theory. Taken together, these two results insure that there will never be an end to science, or suprises.
