sequences. And in practice if the initial conditions are not explicitly specified, what Mathematica does, for example, is to use as an initial condition a representation of various features of the exact state of the computer system at the time when Random was first called.
The rule 30 cellular automaton provides a particularly clear and good example of intrinsic randomness generation. But in previous chapters we have seen many other examples of systems that also intrinsically produce apparent randomness. And it turns out that one of these is related to the method used since the late 1940s for generating random numbers in almost all practical computer systems.
The pictures on the facing page show what happens if one successively multiplies a number by various constant factors, and then looks at the digit sequences of the numbers that result. As we first saw on page 119, the patterns of digits obtained in this way seem quite random. And the idea of so-called linear congruential random number generators is precisely to make use of this randomness.
For practical reasons, such generators typically keep only, say, the rightmost 31 digits in the numbers at each step. Yet even with this restriction, the sequences generated are random enough that at least until recently they were almost universally what was used as a source of randomness in practical computing.
So in a sense linear congruential generators are another example of the general phenomenon of intrinsic randomness generation. But it turns out that in some respects they are rather unusual and misleading.
Keeping only a limited number of digits at each step makes it inevitable that the sequences produced will eventually repeat. And one of the reasons for the popularity of linear congruential generators is that with fairly straightforward mathematical analysis it is possible to tell exactly what multiplication factors will maximize this repetition period.
It has then often been assumed that having maximal repetition period will somehow imply maximum randomness in all aspects of the sequence one gets. But in practice over the years, one after another linear congruential generator that has been constructed to have maximal repetition period has turned out to exhibit very substantial deviations from perfect randomness.