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Doesn’t really generate a random number
- Surprise surprise, the answer is that Math.random() doesn’t really generate a random number. Not exactly. It just does a really good job of simulating randomness. Algorithmic random number generation can’t exactly be random, per se; which is why they’re more aptly called pseudo-random number generators (PRNGs).
hackernoon.com/how-does-javascripts-math-random-generate-random-numbers-ef0de6a20131How does JavaScript’s Math.random() generate random numbers?
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Most of the time, when a computer is generating a "random" number, it's using something called a psuedo-random number generator. Take a simple example like: X_{i+1} = (a * X_i + c) mod n. Here, I need to provide values for a, c, and n, and then I have a function that will produce a series of values. Let's set a=2, c=5, and n=10.
Indeed, it is fundamentally impossible to produce truly random numbers on any deterministic device. Von Neumann [Neu63] said it best: “Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin.”
Dec 13, 2016 · As this method uses random events rather than a pre-defined algorithm, it can generate a truly random number. You can find one such online solution at https://www.random.org/ . Random.org uses atmospheric noise to create randomness.
RANDOM.ORG offers true random numbers to anyone on the Internet. The randomness comes from atmospheric noise, which for many purposes is better than the pseudo-random number algorithms typically used in computer programs. People use RANDOM.ORG for holding drawings, lotteries and sweepstakes, to drive online games, for scientific applications ...
A pseudo-random number generator is just an approximation to true randomness. It in fact does not have to be random at all. A simple implementation goes like this: the generator is defined by some function f which maps n bits to 2n bits, and its state is described by some n-bit string s.
Random Number Generation. Based on original algorithms developed at Wolfram Research, the Wolfram Language's core randomness generation is both highly efficient and of exceptional quality. The Wolfram Language can produce both discrete and continuous randomness, with a wide range of distributions conveniently specified in symbolic form.
