where we explicitly set up the color of each cell to be determined by some short computation from the numbers that represent its position.
When we look at such patterns most of them appear to us quite simple. But as the pictures on the previous page demonstrate, it turns out to be possible to find examples where this is not so, and where instead the patterns appear to us at least somewhat complex.
But for such patterns to yield meaningful examples of computational reducibility it must also be possible to produce them by some process of evolution—say by repeated application of a cellular automaton rule. Yet for the majority of cases shown here there is at least no obvious way to do this.
I have however found one class of systems—already mentioned in Chapter 10—whose behavior does not appear simple, but nevertheless turns out to be computationally reducible, as in the pictures on the facing page. However, I strongly suspect that systems like this are very rare, and that in the vast majority of cases where the behavior that we see in nature and elsewhere appears to us complex it is in the end indeed associated with computational irreducibility.
So what does this mean for science?
In the past it has normally been assumed that there is no ultimate limit on what science can be expected to do. And certainly the progress of science in recent centuries has been so impressive that it has become common to think that eventually it should yield an easy theory—perhaps a mathematical formula—for almost anything.
But the discovery of computational irreducibility now implies that this can fundamentally never happen, and that in fact there can be no easy theory for almost any behavior that seems to us complex.
It is not that one cannot find underlying rules for such behavior. Indeed, as I have argued in this book, particularly when they are formulated in terms of programs I suspect that such rules are often extremely simple. But the point is that to deduce the consequences of these rules can require irreducible amounts of computational effort.
One can always in effect do an experiment, and just watch the actual behavior of whatever system one wants to study. But what one