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Comment by Tomte | Hacker News Reader

Starts interesting, then veers into the usual "true random number" bullshit. Use radioactive decay as source of your random numbers!

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> Use radioactive decay

It's a lot easier to use diodes (light emitting and otherwise).

How do we know it's truly random?

The only known explanation of what's going on in quantum mechanics is a multiversal one^[1]. Using radioactive decay of an atom as an example: there are an uncountably infinite number of universes that are initially "fungible" (identical in every way), and over time the universes gradually differentiate themselves with the atom going from a non-decayed to decayed state, at different times in each universe. But you will be in all of those universes. So if you thought the atom would decay in, let's say 5 seconds, there would be some universes where you were right and some where you were wrong. That makes it impossible to ever make reliable specific predictions about when the atom will decay. So, in practice that just looks like perfect randomness.

^[1]: There are other interpretations, of course. And those other interpretations are equally explanatory. But they do not claim to be explanations of what is actually happening to unobserved quantum particles. There is also Bohmian mechanics, but I don't know how many people take it seriously.

We don't. We know with great certainty it's either random or needs non-local variables, which would let information travel faster than c, among other things. (This is a consistent result of the Bell test.)

Most prefer to believe in randomness.

> usual "true random number" bullshit

What's bullshit about it? This is how TRNGs in security enclaves work. They collect entropy from the environment, and use that to continuously reseed a PRNG, which generates bits.

If you're talking "true" in the philosophical sense, that doesn't exist -- the whole concept of randomness relies on an oracle.

I don't think hardware random number generators are bullshit, but it's easy to overstate their importance. Outside of cryptography, there aren't a whole lot of cases that truly require that much care in how random numbers are generated. For the kind of examples the article opens with (web page A/B testing, clinical trials, etc.) you'll never have sample sizes large enough to justify worrying about the difference between a half-decent PRNG and a "true" random number generator.

Yes, agreed. In many cases, the determinism is a feature, particularly being able to store the seed for reproducibility.

What PRNGs lack compared to TRNGs is security (i.e. preventing someone from being able to use past values to predict future values). It's not that they somehow produce statistically invalid results (e.g. they generate 3s more often than 2s or something). Unless they're very poorly constructed.

Maybe people have bad memories from linear congruential generators, these could go really bad (https://en.wikipedia.org/wiki/Marsaglia%27s_theorem)

While LCGs are bad by themselves, they (together with Galois field counters, which have a large number of possible implementations, e.g. LFSRs, GFSRs, XorShift etc.) have some very desirable properties for a PRNG: known period, it is possible to make jumps through the sequence and it is possible to extract sub-sequences from it that are certain to not overlap, e.g. for a multithreaded simulation.

Because of this, the best non-cryptographic PRNGs are made from either a LCG or a GFC that ensures the properties mentioned above, together with a non-linear mixing function that scrambles the output, for much better statistical properties than a linear generator would have alone.

The good cryptographic RNGs have the same kind of structure, but where a one-way hash function or a block cipher function is used to scramble the output of a counter. The counter ensures in a simpler way the same properties as a LCG or GFC. A simple counter can be used here because the output mixing function is much more complex.

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