Random Number Generators Explained:
Applications and How They Work
Ever played a game or entered a prize draw where names were drawn out of a hat? The idea is that the winners were picked at random. The outcome couldn’t be premeditated easily, and everyone who enters knows they have the same chance.
Similar methods are still used for drawing cup fixtures in football or other sports. The world needs a digital alternative though, and that is where random number generators (RNGs) come in.
RNG Applications
Understanding RNG applications also helps us to get to grips with exactly how they work.
Gaming is one application a lot of us have probably experienced from time to time. Many games require some element of randomness and sometimes this is to keep them fair. RNGs create unpredictable elements in video games, and they can even decide how characters (NPCs) in the games act.
In casino gambling games they can keep the outcome fair and random. Roulette is a good example – when the world moved from playing inperson to online, roulette was one of the games that posed a problem. Inherently, roulette with a wheel is unique, but playing it online requires something to step in and change the mechanics and keep it random. That is an RNG! When people
sign up and play online roulette games
the games are not decided by a physical real spinning in most scenarios. The outcome has been determined by an RNG.
RNGs play such a crucial role in the casino industry and need to be verified as providing totally random outcomes. No single spin should have more chances than another.
Outside of gaming, plenty of RNG uses exist. RNGs generate keys that encrypt data, keeping information secure. This makes it harder for people to access sensitive information, for instance. Numbers like this can be a bit like an extrasecure passcode attached to data. Unpredictable and random numbers keep things
much more secure.
Sampling and statistics can be another use. When conducting surveys, RNGs select random participants to ensure the sample represents the whole population. Think of it like this, if there are 1000 people that can be chosen to answer a questionnaire, ensuring that they are all picked totally at random can help to ensure a more accurate result.
Imagine a store carrying out a survey, but they choose to carry out that survey during lunchtime hours, they might only get a certain demographic of people, such as office workers on a break from lunch. This could skew results or lead them a certain way. Instead, if they randomly select customers to ask they are going to get more accurate results.
Quality control is another top example, as manufacturers use RNGs to select random samples of products for quality testing. If they just checked five products in a row then they may have all been constructed by the same person, for example. Checking them throughout the day means a more random and steady check, which is more thorough.
How RNGs Work
Random number generators need to be exactly that; completely random. In mathematical terms, this is actually a complex idea and sometimes needs explaining in simple terms, with mathematics books being a good way to do this.
Pseudorandom number generators (PRNGs) are where mathematics comes in. PRNGs use algorithms to produce sequences of numbers that mimic the properties of random sequences.
One of the simplest and most popular PRNGs is the
Linear Congruential Generator
. It is defined by the recurrence relation:
Xn+1=( aXn+c) mod m
To explain:
 XXX is the sequence of pseudorandom numbers.
 nnn is the index of the sequence.
 aaa is the multiplier.
 ccc is the increment.
 mmm is the modulus.
 X0X_0X0 is the seed.
Inputting parameters (such as minimum and maximums) can also help to give the equation more definition and provide random numbers on demand.
PRNG algorithms start with an initial value or a seed and then apply a series of mathematical operations to produce a sequence of numbers. The sequence is random, even though it is generated by a deterministic process.
True Random Number Generators (TRNGs) are another option – they rely on physical processes to generate randomness. They use unpredictable physical phenomena, such as electronic noise, knowing it will generate random numbers. TRNGs are used in cryptography a lot, keeping everything completely unpredictable.
Conclusion
You can see things are a bit more sophisticated than pulling pieces of paper out of a hat nowadays!
RNGs have grown to the point where they play a role in many different aspects of life, they’re even used in forecasting the weather! Gaming has seen this technology become crucial, and the RNGs are programmed into so many different games to ensure that there are consistently different experiences or a random, even chance of things happening. Mathematics truly is everywhere!
