3. Science is doomed to uncertainty---but
this is a good thing|
The interesting things mentioned
above are often found to have computationally profound qualities. For
instance, even if you had a perfect model or theory of how something
worked, chances are that it would still be impossible to perfectly
predict the future of that which you have modeled. A related result
guarantees just the opposite: regardless of how much ``data'' one
collects, it's not always possible to build a perfect theory. Taken
together, these two results insure that there will never be an end to
science, or suprises.
The bottom line here is that whenever something is so rich as to
be related to something akin to a universal computer, it is impossible
(in the general case) to completely understand the underlying
phenomenon. This has deep implications for how we do science.
Consider the figure below (from Chapter 24).
In the past, there were two different methods for how science was
performed. In one method, an experimentalist fiddles with some
natural phenomenon so that data can be collected in the form of
observations. These data are then compared with the current state of
Coming from the opposite direction, a theorist derives a
model for how a natural process works. The new model is then used to
make predictions for how the natural process will behave at a future
With the advent of computers, a new and hybrid method of science
has emerged. This new type of scientist, known as a simulationist,
simultaneously performs experimentation in a universe of theories by
simulating natural phenomena within the confines of a computer.
Neither of these methods of performing science is intrinsically
superior to the others. Each of them are, in a sense, incomplete.
But simulation allows for a type of exploration that neither
experimentation nor theorization can provide. Pure theory fails when
a phenomenon does not obey models that can be analytically solved. Pure
experimentation fails when complex effects cannot be correlated to
simple causes. But simulation hovers about these two extremes,
allowing for a new type of science that mirrors (if only
approximately) the complexity and beauty that we see in the real
The good news here is that there will never be an end to science.
For science to end, one of two things must happen: (1) we would need
a Theory of Everything that could be used to predict and explain all
things, or (2) we would reach a clear and identifiable point at which
no further progress could be made. But because nature's most
beautiful phenomenon contain computational richness, there will always
be surprises and new discoveries just beyond our reach.