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out that there is behavior which does not happen to fit in with these assumptions, then typically the experiment will fail to notice it.

In my experience, however, the way to have the best chance of discovering new phenomena in a computer experiment is to make the design of the experiment as simple and direct as possible. It is usually much better, for example, to do a mindless search of a large number of possible cases than to do a carefully crafted search of a smaller number. For in narrowing the search one inevitably makes assumptions, and these assumptions may end up missing the cases of greatest interest.

Along similar lines, I have always found it much better to look explicitly at the actual behavior of systems, than to work from some kind of summary. For in making a summary one inevitably has to pick out only certain features, and in doing this one can remove or obscure the most interesting effects.

But one of the problems with very direct experiments is that they often generate huge amounts of raw data. Yet what I have typically found is that if one manages to present this data in the form of pictures then it effectively becomes possible to analyze very quickly just with one's eyes. And indeed, in my experience it is typically much easier to recognize unexpected phenomena in this way than by using any kind of automated procedure for data analysis.

It was in a certain sense lucky that one-dimensional cellular automata were the first examples of simple programs that I investigated. For it so happens that in these systems one can usually get a good idea of overall behavior just by looking at an array of perhaps 10,000 cells—which can easily be displayed in few square inches.

And since several of the 256 elementary cellular automaton rules already generate great complexity, just studying a couple of pages of pictures like the ones at the beginning of this chapter should in principle have allowed one to discover the basic phenomenon of complexity in cellular automata.

But in fact I did not make this discovery in such a straightforward way. I had the idea of looking at pictures of cellular automaton evolution at the very beginning. But the technological difficulty of producing these pictures made me want to reduce their number as

out that there is behavior which does not happen to fit in with these assumptions, then typically the experiment will fail to notice it.

In my experience, however, the way to have the best chance of discovering new phenomena in a computer experiment is to make the design of the experiment as simple and direct as possible. It is usually much better, for example, to do a mindless search of a large number of possible cases than to do a carefully crafted search of a smaller number. For in narrowing the search one inevitably makes assumptions, and these assumptions may end up missing the cases of greatest interest.

Along similar lines, I have always found it much better to look explicitly at the actual behavior of systems, than to work from some kind of summary. For in making a summary one inevitably has to pick out only certain features, and in doing this one can remove or obscure the most interesting effects.

But one of the problems with very direct experiments is that they often generate huge amounts of raw data. Yet what I have typically found is that if one manages to present this data in the form of pictures then it effectively becomes possible to analyze very quickly just with one's eyes. And indeed, in my experience it is typically much easier to recognize unexpected phenomena in this way than by using any kind of automated procedure for data analysis.

It was in a certain sense lucky that one-dimensional cellular automata were the first examples of simple programs that I investigated. For it so happens that in these systems one can usually get a good idea of overall behavior just by looking at an array of perhaps 10,000 cells—which can easily be displayed in few square inches.

And since several of the 256 elementary cellular automaton rules already generate great complexity, just studying a couple of pages of pictures like the ones at the beginning of this chapter should in principle have allowed one to discover the basic phenomenon of complexity in cellular automata.

But in fact I did not make this discovery in such a straightforward way. I had the idea of looking at pictures of cellular automaton evolution at the very beginning. But the technological difficulty of producing these pictures made me want to reduce their number as


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From Stephen Wolfram: A New Kind of Science [citation]