Considering the giant leaps technology has made in recent years, you’d think a program that generates random numbers would be reasonably straightforward. RNGs, or Random Number Generators, have existed for quite some time, but that doesn’t make them easy to create.
Most RNGs you use in your daily life, whether directly or indirectly, are actually “pseudo-random number generators” or PRNGs. This might sound like a bad thing, but that’s not exactly the case. To learn why, let’s delve into the technology behind modern RNGs and how the different types of generators work.
What Is a Random Number Generator?
As the name suggests, a Random Number Generator is a tool that generates random numbers. This includes physical methods of creating these numbers, such as throwing dice, drawing cards or yarrow stalks, or flipping a coin.
There are some problems with these manual methods, however. They are easy to manipulate and use to cheat. Additionally, these tools are limited to a certain number of outcomes. These issues may not be a big problem when it comes to a game of cards or a slot machine, but modern RNGs are used for much more than that.
For more advanced needs, like statistical sampling and computer simulations or cryptographic protocols, RNGs need to be genuinely random, safe from outside interference and capable of working with enormous sets of numbers.
Pseudo-Random Number Generators
Unlike those “classical” types of RNGs like dice and cards, most RNGs today can be divided into two groups: pseudo-random number generators and true random number generators. PRNGs are not inferior to RNGs; they are actually the most widespread type of RNG software today, from smartphone apps to banking.
PRNGs are systems made to reliably produce many random bits from a smaller set of random bits. That initial set of bits might be a key, a code, or a physical event, such as your mouse clicks. A PRNG then takes this group of random events and generates an exponentially more extensive set of random values.
The issue with PRNGs is that they can’t produce a truly random set of numbers without those initial bits. The seed used for a PRNG is essential, as it determines how random the RNG truly is. For PRNGs to be used for any significant purpose, they often combine with other PRNGs to consistently produce randomised results.
This mixing of PRNGs and pools of initial random bits of data makes these algorithms suitable for many applications, including fiscally sensitive purposes in banks, casinos and more.
True Random Number Generators
True Random Number Generators are algorithms that rely on physical phenomena which produce information in a genuinely random and inexhaustible fashion. This includes sources of thermal noises, radioactive decay, hardware activity on a small scale, or specific types of hardware based on quantum physics (the Quantum Random Number Generators or QRNGs).
Because of their much more complex seeds, TRNGs can produce much more extensive, truly random and “unhackable” results. A single PRNG can be hacked if someone gets access to the initial limited seed because it has a repeatable pattern that can be figured out. A TRNG, on the other hand, keeps producing truly random results indefinitely.
Are PRNGs “Worse” Than TRNGs?
It may sound like PRNGs are inefficient compared to TRNGs, but PRNGs are widespread in many fields, including fiscally sensitive tasks. This is because of two factors:
PRNGs can produce reliably random outcomes if they are seeded properly, especially when the seed pools of two or more PRNGs are combined.
Hacking a PRNG’s seed pool is no easy task, as institutions employ various types of software protections to keep their algorithms safe from hackers.
What Are PRNGs and TRNGs Used For?
RNGs of both types are used in various industries for multiple tasks. The biggest use is security mechanisms, but they are also employed for scientific modelling, statistical sampling, simulations, AI, automation applications, games and more. The next time you consider playing a lottery or an online slot you found in a guide to the best Irish casinos, you’ll better understand the complex algorithms they use based on multiple safe and unhackable PRNGs.
Conclusion
RNGs are simple in principle but much more complex in practice. This isn’t because it’s challenging to produce a random set of numbers (everyone can pick up a set of dice and throw them) but because RNGs are complicated. The random values they produce must be not only truly random and sizable but also impossible to predict.
Pseudo-Random Number Generators can’t be used for everything; if they were, people would eventually figure out the seed pool and be able to predict the algorithm’s outcomes. TRNGs, or ever-changing combinations of PRNGs, are necessary for banks, casinos and many other institutions to reliably output truly random results.
