``Computational models of nature'' covers a multitude of sins. Most of them are, in this case, topics fashionable in popular science writing, at least within recent memory: fractals (five chapters, including one of the Mandelbrot set alone), chaos (another five chapters), neural networks (two chapters), the Prisoners' Dilemma, and ``birds and bees'' self-organization (nest-building termites, flocking birds). Others, less trendy but in many ways more meaty, come out of Flake's training and research as a computer scientist, like cellular automata, genetic algorithms, classifier systems and the control of chaos. Most impressively, and very confidently, Flake opens the book with the theory of abstract computation, Turing Machines and their equivalents, and Turing's results on uncomputability, of which Gödel's Theorem is an easy corollary. I have for some years now been collecting proofs of that theorem aimed at non-logicians, and Flake's is one of the smoothest I've seen.

Flake's treatment of even the most trendy subjects keeps to this high level:
clear, accurate, level-headed and detailed, without being overwhelming. In
addition to the excellent job on Turing and Gödel, he also has the first
description of the control of chaos I've seen which is neither hand-waving nor
forbiddingly technical, and is all the more welcome because, to read most books
on this level or below, you'd think controlling chaos was a contradiction in
terms. Bright high school students or college freshmen should be able to grasp
much of the book, and are probably its ideal audience; a third or fourth year
science undergraduate should get all of it. (It probably *would* make a
good text for a survey course.) I didn't learn anything important from this
book, but I would've been disturbed if I had, and I did have a lot of fun.
Part of that was playing with Flake's very nice programs (free, with source
code, from an MIT Press
web-site), which, in effect, let readers make their own picture books, if
they want. Part of the fun was seeing how all this ``neat nonlinear nonsense''
looks to somebody habituated to discrete programs representing imaginary
worlds, rather than stochastic processes to be fit to Real Data (TM).

I admit that I did wince once or twice, peering into this looking glass, particularly the stuff on phase transitions (pp. 328ff). Despite their exorcism from other precincts, the shades of several Nice Tries --- Wolfram's four-fold classification of cellular automata, Langton's lambda parameter for ditto, Bak's self-organized criticality, the edge of chaos, even insect colonies as super-organisms --- continue to haunt Flake's pages. Classifier systems (the subject of ch. 21) might also belong among the Ghosts of Notions Past; but I left them off, probably for the same reason that Flake gave the others the nod: it's not my specialty at all. In his grand comparative tables (pp. 430--1), I would have marked every row after the third with a star, as being ``merely analogous'' to real computability. But I don't think any of this will do much harm, and in any case a reader who gets really interested in one or another of Flake's special topics will be well-prepared to dig into the more dedicated (if not dessicated) literature; the ghosts are harmless.

I do have a more substantial bone to pick with Flake's asides on philosophy. His dismissal of reductionism is both conventional and mis-guided (cf. my review of John Holland's Emergence). When he talks about recursion as a physical, natural process, rather than a mathematical notion, I am irresistibly reminded of Pythagoras proclaiming that All Is Number. He adopts Greg Chaitin's idea that algorithmic incompressibility puts important limits on scientific knowledge, but I am thoroughly unimpressed by this bastard offspring of Ernst Mach and Andrei Kolmogorov. (This last, however, connects to my own line of research, so I'm probably unnaturally picky.)

Peter Medawar used to say that that a scientific hypothesis was an imaginary
world, or a part of one, and that the point of experiment was to see how well
that world approximated the real one. Now, Flake is a computer scientist,
which means, as he tells us at the start of the book, that he ``studies toy
problems and fudges his results.'' But in this he is unduly modest (especially
in Part I, where he's showing us real math). As imaginary worlds, all his
models are perfectly fine, and most of them even have a family resemblance to
the Realized World. Fudging would begin only when we said that it was more
than a family resemblance, that coast-lines are truly fractal, that immune
systems are classifier systems, that social interactions are Prisoners' Dilemma
games, that real nervous systems are neural networks. They would fail the test
of experiment; some of them *have* failed the test of experiment. Does
this mean that the book is mis-leadingly titled after all, that it's not about
the computational beauty of *nature* at all? Not really; it's just a
highly stylized picture of nature, more akin to geometrical flowers of
traditional Islamic design than one of Dürer's ready-to-plant
dandelions. Much of their charm, and all of their suitability for
recreational computing, lies in the fact that they *are* caricatures,
with every line not absolutely essential to recognition erased. Even in this
stripped-down condition, some of them actually have practical utility, like
genetic algorithms; others have served as starting-points for the construction
of more messy and more realistic models, imaginary worlds bearing a closer
resemblance to reality; still others are give us information about the ``limits
of the possible,'' or at least about what's possible within bounds we find
plausible. Such considerations do not sully this book, nor should they. Let's
be honest; people practice ``fact-free science'' for the fun of playing with
mind-toys, of crafting and exploring microcosms, the early-morning
caffeine-fueled rush, the silicon revelation of the imaginary worlds
themselves. This is what gets amateurs hooked; give them this book and a
machine to play with and they will be. What more could one ask?

xx + 495 pp., black and white diagrams, end-of-chapter references, complete bibliography, documentation for programs, glossary (mildly self-contradictory; s.v.

Cellular Automata / Computers and Computing / Self-Organization, Complexity, etc.

Out of print as a hardback, ISBN 0-262-06200-3 [Buy from Powell's], in print as a paperback, US$40, ISBN 0-262-56127-1 [Buy from Powell's]

1 November/4 December 1998