Pseudo Random NumbersPseudo Random NumbersThis model represents one simple way to generate random-seeming numbers between 0 and 1. Although there is no way to generate true randomness, it can be simulated through complex algorithms that yield a relatively even distribution of decimals. Random numbers are useful in many types of mathematic and statistical analysis.
The core of the pseudorandom number generator (PRNG) is the multiplier and shift. These are two arbitrarily chosen large numbers that combine to give all of the subsequent “random” values. An initial seed gives a starting value, which is then divided by 1024, with the remainder kept. This generates a pseudorandom number between 0 and 1023, which is then divided by 1024 to get a number between 0 and 1. The next number is generated in the same way, but using the previous number as a seed.
Then, then pseudorandom numbers are substituted in the equation for a circle in order to conduct a “Monte Carlo” analysis of the circle’s area. These numbers are then averaged to get an approximation for the area of a unit circle - Pi.
This model is very simple. Just choose multipliers, shifts, and seeds and it will automatically generate pseudorandom numbers.
By definition, the numbers generated by this model are not truly random. Indeed, there are only 1024 possible numbers generated here, as opposed to an infinite number of potential random numbers. However, this gives a good approximation of randomness and can be used to come up with a statistical approximation for the value of Pi.