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The book is full of incredibly interesting ideas. He explains ontogeny (the transformation of a fertilized egg to a highly complex and differentiated organism) using a simple model of on/off enzymes which allows him to build a Boolean network in which different cell types correspond to different "attractors", which are intrinsic in such a network. He shows that the same relationship that holds between number of attractors and size of a network, also holds between number of cell types and size of DNA of a wide range of organisms. Very impressive. He goes on to discuss things like fitness landscapes and genetic algorithms, the edge between boring order and supracritical instability where the really interesting stuff happens, the co-evolution of coupled systems, the structure of efficient companies or countries, and more.
The only criticism I have is about his poetical language that does indeed resemble fluff; anyone who even partly understands his ideas would be excited enough without all that sauce. Also I missed a deeper development, the book does point into one interesting direction and then jumps into another matter, leaving one hungering for more. But maybe this is the author's intent.
This is an excellent book even though it resembles more a symphony of ideas than a theorem. Very highly recommended: a mind opener.
I made the mistake of reading it on holidays, with no access to a computer. Big mistake! I kept wanting to program, to check out what the author was saying, to try variants and elaborations. I.e. to have lots of hands-on fun -- it's that sort of a book and I can thing of no higher recommendation. But please, oh please, somebody introduce Kaufman to a good editor!
There is a lot of good stuff in here. The descriptions of the patch procedure and simulated annealing, for instance, are very nice. This book can be useful to the motivated general reader, and to a scientist who wants to see the very basics of some novel ideas. It can also be useful for those familiar with complexity as an account of how different pieces fit together.
It's important to remember that the book is not a text in, say, biochemistry. Rather, it's about a way to see the world. At this stage of the idea development life cycle and in a basic treatment like this, it would be counterproductive to insist that these modeling tools reproduce everything we know or start at the level of complication of a mature science. If the book deals in toy examples that relate to a different view for pieces of the world and how they relate, it has done most of its job.
On the other hand, the book definitely has the mildly unpleasant tenor of a popularization. So, for example, any new idea is dressed up as revolutionary. Kauffman is actually better about this than many authors, especially in this field, but it's still palpable.
It is also written with all the mid-'90s euphoria over complexity. It is not clear that it will take as far as the gurus envision, but it is fun to think about -- and this book is a good way to start.
1) It assumes that the probability of a molecule to catalyze a... Lesen Sie weiter...
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