🚀 go-pugleaf

In article <1992Jul29.132817.7113@Princeton.EDU> rdnelson@phoenix.Princeton.EDU (Roger D Nelson) writes:
>Why don't people interested in "good" RNG's simply use one or another of 
>the physical random sources, such as radioactive decay or quantum tunneling.
>Obviously these would not be appropriate for cases where a repetition of
>the random sequence is desired, but otherwise...?  For a couple of
>thousand dollars, which is a trivial cost compared to the time spent on
>algorithmic RNG research, one can buy excellent, well designed and
>executed commercial RNG's.  I would be interested in some discussion of
>problems or inadequacies (or indeed virtues) of so-called "true" random 
>event generators.

As far as I can tell, there are 4 big problems with using experimental
observations as RNG's.

1.	Says who they're _really_ random? Sure we like to think so, but
	how do you know there isn't a little bias in say, the A/D
	converter, etc. Keep in mind that a high correlation over a
	long period is usually as bad as a periodicity, and that if
	you think about how long a period you get with algorithms,
	it means that you need to have very good control over the
	frequency response of your measurement device over a range
	of frequencies covering more than 10 decades. This is not
	trivial.

2.	They aren't repeatable. This lets out the crypto people who need
	pseudo-random numbers, anyways, and it probably isn't so convenient
	for people with giant Monte Carlo things to debug.

3.	You can't prove theorems about their outputs very easily. This
	is similar to (1), but it can also be a defect of some algorithms
	too.

4.	They tend to have low bandwidth compared to the same quality
	digital algorithms. I'm not really current on this, but last
	time I knew anything about this question, there was just no
	way to beat parallel implementation of algorithmic RNG's on
	chips.

The way I look at it, the problem with 'natural' RNG is that the market
is small for it, and so the use of general purpose hardware is always
economically favored - i.e. algorithmic RNG.


Later,
Andrew Mullhaupt