Chapter 12: The Principle of Computational Equivalence

Section 4: The Validity of the Principle

Continuous computation

Various models of computation that involve continuous elements have been proposed since the 1930s, and unlike those with discrete elements they have often not proved ultimately equivalent. One general class of models based on the work of Alan Turing in 1936 follow the operation of standard digital computers, and involve looking at real numbers in terms of digits, and using discrete processes to generate these digits. Such models inevitably handle only computable reals (in the sense defined above), and can never do computations beyond those possible in ordinary discrete systems. Functions are usually considered computable in such models if one can take the procedure for finding the digits of x and get a procedure for finding the digits of f[x]. And with this definition all standard mathematical functions are computable—even those from chaos theory that excavate digits rapidly. (It seems possible however to construct functions computable in this sense whose derivatives are not computable.) The same basic approach can be used whenever numbers are represented by constructs with discrete elements (see page 143), including for example symbolic formulas.

Several times since the 1940s it has been suggested that models of computation should be closer to traditional continuous mathematics, and should look at real numbers as a whole, not in terms of their digit or other representations. In a typical case, what is done is to generalize the register machines of page 97 to have registers that hold arbitrary real numbers. It is then usually assumed, however, that the primitive operations performed on these registers are just those of ordinary arithmetic, with the result that only a very limited set of functions (not including for example the exponential function) can be computed in a finite number of steps. Introducing other standard mathematical functions as primitives does not usually help much, unless one somehow gives the system the capability to solve any equation immediately (see below). (Other appropriate primitives may conceivably be related to the solubility of Hilbert's Thirteenth Problem and the fact that any continuous function with any number of arguments can be written as a one-argument function of a sum of a handful of fixed one-argument functions applied to the arguments of the original function.)

Most of the types of programs that I have discussed in this book can be generalized to allow continuous data, often just by having a continuous range of values for their elements (see e.g. page 155). But the programs themselves normally remain discrete, typically involving discrete choices made at discrete steps. If one has a table of choices, one can imagine generalizing this to a function of a real number. But to specify this function one normally has no choice but to use some type of finite formula. And to set up any kind of continuous evolution, the most obvious approach is to use traditional mathematical ideas of calculus and differential equations (see page 161). This leads to models in which possible computations are assumed, say, to correspond to combinations of differential equations—as in Claude Shannon's 1941 general-purpose analog computer. And if one assumes—as is usually implicitly done in traditional mathematics—that any solutions that exist to these equations can somehow always be found then at least in principle this allows computations impossible for discrete systems to be done.

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