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Random Number Generator Software

One of the most important elements that makes life so interesting is that it is unpredictable. Just pause for a moment and think of where you were 10 years ago, think again about where you were 5 years ago and then lastly think about where you were a year ago. Can you say that you are exactly in the position you thought you were going to be in all those years back? Probably not. This is because life brings the unexpected and that’s the beauty of it all – the element of surprise.

There are many things that catch us off guard purely based on the randomness of events and this is a concept that video games companies know all too well by implementing Random Number Generator software into their games. Random Number Generator software, or simply RNG, refers to the hardware that generates symbols or numbers which cannot be reasonably predicted other than by chance. In essence, this means RNG is responsible for creating randomness.

RNG software comprises of different types and formats, so let’s delve into more detail about this concept to explore its technicalities and how it works.

 

Physical RNG Methods

Physical RNG methods have been in existence for centuries and were mostly used to determine the randomness of different outcomes. Physical RNG methods include the rolling of a dice, shuffling cards, coin flipping, divination and raffle draws, among other activities. It is also more mechanical and less scientific and due to its nature, it a less attractive proposition in today’s modern world. In the past raffle draws were quite a common practice, but today, it’s almost impossible to physically calculate random numbers based on large sample data with hundreds, thousands and in some instances, millions of participants. In this case, physical RNG methods are too slow to cater for huge numbers.

 

Calculating RNG by Computational Systems

With physical RNG methods becoming less used in the current, computational RNG systems were developed to replace an outdated method of determining randomness. There are a number of computational RNG methods including the middle square method, the Weyl sequence and the popular pseudorandom number generator method, among many others.

Pseudorandom number generator or PRNG uses algorithms that generate symbols or numbers that cannot be predicted. It also uses the seed value, which is more like a range and there is a certain limit to the symbols and numbers generated by the hardware. Suppose the seed value is set at ‘1’, it means the PRNG will have a ‘range’ of 0 to 1 that is to say that the hardware will generate numbers that cannot be predicted between 0 to 1. However, almost all PRNGs come with a non-blocking feature, which essentially means that the PRNG continues running even after reaching its limit. When it reaches the limit it repeats the same sequence. Although designed in this way, it’s rare to see a PRNG reach its limit and have a repetition occur.

Instances of the use of PRNGs can be seen mostly in casino games where randomness is a key element in determining fair play. In other words, the house and the player both need to have the same or almost the same chances of winning and losing. In order to ensure that all casino games operate in an unbiased manner, fair casino methods are employed by using RNG to make sure that all of the games’ outcomes cannot be reasonably predicted.

 

Random Numbers Generated by Humans

Not necessarily classified as software per se, however humans can also be seen as random number generators in their own right. Naturally, by having a person calculate random numbers is way too slow or inaccurate in comparison to computational RNG and even physical RNG methods. Human RNGs basically involve the use of the brain in shuffling numbers or any other activity. As an example, imagine that you are doing street interviews to gauge the views of the public on a burglary problem your community is facing. How will you select your interviewees? There is no system whatsoever other than picking your interviewees at random, perhaps from a number of names thrown in a hat. By doing so, you are effectively a human random number generator!

 

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