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But in general one suspects that all these rules can be thought of as being like simple computer programs that take some representation of n as their input.
In a sense, therefore, neither of these mechanisms takes any real responsibility for explaining the origins of randomness: they both in the end just say that randomness comes from outside whatever system one happens to be looking at.
motion, in which one takes a small grain, say of pollen, puts it in a liquid, and then looks at its motion under a microscope.
In a device that produces a spark, for example, it inevitably takes some time for the hot gas in the path of the spark to be cleared out.
The basic idea is to take a lump of dough-like material, and repeatedly to stretch this material to twice its original length, cut it in two, then stack the pieces on top of each other.
In most cases the basic approach I take is to try to construct the very simplest possible model for each system.
But with the discovery in this book that it takes only a simple program to produce behavior of great complexity, a quite different—and ultimately much more predictive—kind of explanation immediately becomes possible.
The Need for a New Intuition
The pictures in the previous section plainly show that it takes only very simple rules to produce highly complex behavior.
And in fact, to do so requires absolutely no sophisticated ideas from mathematics or elsewhere: all it takes is an understanding of how to apply simple rules repeatedly.
But as a very simple idealization of the way that information flows in a market, one can, for example, take each color to be given by a fixed rule that is based on each entity looking at the actions of its neighbors on the previous step.