Random Number Generators
Randomness is not a new concept; however, computers are not good at it. Computers depend on a sequence of programmed operations to predict a given outcome. Randomness does not require dependence on programmed aspects, but monitoring certain phenomenon naturally to make predictions. Therefore, software has been created to generate random numbers that can serve multiple purposes.
Random number generators have diverse applications as they are used in gambling, cryptography, and computer simulation. Random number generators seek to create unique characters that cannot be replicated or guesses. Live casinos wok under the same concept, whereby there are some aspects that cannot be replicated (Niel & Laffan, 2003). Online casinos use the concepts applied in live casinos by using a software that can imitate actions in the latter. The software communicates to a random number generator each moment it needs to generate an outcome. The number that the RNG generates at that given moment is then fed into the software to give the possible outcome.
Initially, random number generators were primarily used for computer simulation. Random number generators are still used to carry out simulation involving natural phenomena, such as the simulation in nuclear fission to obtain neutron transport patterns. Pseudo-random numbers are used in statistical sampling to create randomness in the simulation of statistical events (Soto, 1999). Random number generators and pseudo-random number generators can be further used in completely randomized designs and other areas that require an unpredictable result. However, random number generators have some weaknesses that can affect the overall outcome, especially in statistical sampling. In some cases, uniformity might not be achieved, especially in distribution of large quantities. There is also a possibility of having extremely large distances between some vales occur. Another disadvantage of using random number generators is that it might result to low-quality dimensional distribution.
References
Niel, V.K. & Laffan, W, S. (2003). Gambling with randomness: the use of pseudo-random
number generators in GIS. International Journal of Geographic Information Science,
17(1).
Soto, J. (1999). Statistical Testing of Random Number Generators