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Indeed, given the rules for a discrete system, it is usually a rather straightforward matter to do a computer experiment to find out how the system will behave.
Long-range communication of information is in principle possible, but it does not always occur—for any particular change is only communicated to other parts of the system if it happens to affect one of the localized structures that moves across the system.
Why does it yield nested patterns?
But how do all these various processes get organized to produce an actual animal?
But if the universe is at an underlying level just a discrete network of nodes then how does curvature work?
So do these fluctuations represent evidence that sequences (g) and (h) are not in fact random?
But as we have seen many times in this book, more complicated rules do not necessarily produce behavior that is fundamentally any more complicated.
But how can one be sure that there really is absolutely no easy way to do this?
But as of now I do not know of any fundamental reason why this might be so, and following my arguments in Chapter 8 I would not be at all surprised if the process of biological evolution had simply missed even methods of perception that are, in some sense, fairly obvious.
But the remarkable assertion that the Principle of Computational Equivalence makes is that in practice this is not the case, and that instead there is essentially just one highest level of computational sophistication, and this is achieved by almost all processes that do not seem obviously simple.