A random number generator is often referred to as an RNG. A random number is selected from a pool of unlimited or limited numbers with no pattern capable of being predicted. The numbers in the pool are usually independent of one another. There may be a specific distribution to the pool. A good example is a neighborhood of children with a normal distribution of height. If one child is chosen at random, the odds are the child will be close to the standard height as opposed to being considered extremely short or tall for their age. Since the numbers generated are random, they are evenly dispersed among the possible values.
A random number generator is essentially a device generating one or more numbers using A defined scope. An RNG can be a pseudo-random or hardware-based number generator. The hardware-based version generally involves using coins or dice for flipping for numerous different devices. The pseudo-random version uses an algorithm to generate a sequence of numbers. This enables an approximation for sequences containing random numbers. An RNG can also be computer-based. These are usually pseudo-random. The numbers generated by pseudo-random generators are not actually random. The generation of random numbers is sufficient for the majority of applications.
Pseudo-random generators should not be used for cryptographic purposes. The basis for a truly random number is a physical phenomenon including thermal noise, atmospheric noise, and general quantum phenomena. Compensation for all potential biases must be compensated for to generate a truly random number. An RNG should not display any generation or appearance of a discernible pattern. This is what makes them random. RNGs are used for the formation of blocks of code or function for software applications requiring chance including numerous types of games.
The Modern Application
Randomness devices have been around since ancient times including devices for drawing straws, flipping coins and shuffling cards. RNGs are simply the modern version. Modern computing implements RNGs through programming. The basis is deterministic computation. This is not really random because when the seed values are known, the outcome becomes predictable. Actual randomness is not always necessary. A good example is a music player with a random function. This is not actually random or the same track could play several times in a row. Algorithms are often used to maintain control of the selection process.
For an RNG to be truly random, they are unable to rely on computational algorithms or mathematical equations. When an equation is involved, the number is not random. One example of a true RNG is a device capable of measuring radio noise, extracting the value obtained and using this value in the application. This must include entropy sources such as radioactive decay. The laws of quantum mechanics explain both the randomness and unpredictability. Applications benefiting the most from truly random numbers include games related to gambling such as the lottery, card games, and bingo.
RNGs are beneficial for video games involving the random collection of prizes or loot. Frustration often results from a pseudo-random generator because it can take a long time to reach the target number. This number can also occur numerous times in a row.
The Different Types of Random Number Generators
RNGs are available in different types. Casinos use pseudo-random number generators. These are unique because no external data or numbers are necessary to produce an output. All that is required is a seed number and an algorithm. New seed numbers produce results for every millisecond. This is accomplished by using the last couple of numbers and using a mathematical operation such as division, multiplication, subtraction or addition for the creation of a new random outcome. There is nothing random regarding mathematical operations. Just as two plus two always equals four, the same input will produce the same output.
RNGs can be hacked because the algorithms are fixed. There are a lot of known algorithms all over the world. Casinos could be cheated out of millions if an individual knew the seed numbers and algorithms being used. RNGs are used for virtual games by both brick and mortar and online casinos since no dealer is necessary. This includes virtual roulette, blackjack, video poker, keno, and video slot machines. When an RNG is used for slot games, each symbol on the reel is assigned a value. A good example is a slot machine with five reels and twelve symbols on each one.
The value determined by the RNG would be one through twelve for each reel resulting in five different symbols. When the five random symbols form a winning combination, the chart determines how much the individual wins. Although it is possible to cheat an RNG, most individuals are not capable of doing so. There have been individuals in the past who have figured out a way to cheat the random number generators used in casinos but these instances are rare. Any gambling institution using an RNG must have their software tested through a third party company. This prevents the RNG from being rigged.
The Steps Taken to Ensure The Fairness of RNGs
As long as no individual is attempting to determine the pattern of an RNG during a lengthy period of time, they are believed to be fair. Testing and service evaluations are performed on a regular basis by a third party company to ensure the RNGs are fair. This includes:
Evaluations for games and mathematics
Evaluations for betting exchanges, sportsbooks and live dealers
Audits for poker systems
Regular evaluations for RNGs
Evaluations for pari-mutuel and lottery systems
Ongoing game payout or RNG reporting and verification
Full security audits
Penetration testing
When an RNG is truly at random and fair, the game will be given a certificate and a small badge. This means outside variables have not influenced the RNG such as the VIP cards, the amount of the potential payout or the number of credits being used. The majority of RNGs are pseudo-random. Although it is possible to cheat them if the individual has the right criteria, the instances of this happening are rare since most people do not understand the algorithms, seed numbers, and mathematics used for the creation of an RNG.
