In many fields, research work prior to the 21st century that relied on random selection or on Monte Carlo simulations, or in other ways relied on PRNGs, were much less reliable than ideal as a result of using poor-quality PRNGs. It was seriously flawed, but its inadequacy went undetected for a very long time. An example was the RANDU random number algorithm used for decades on mainframe computers. Distances between where certain values occur are distributed differently from those in a random sequence distribution.ĭefects exhibited by flawed PRNGs range from unnoticeable (and unknown) to very obvious.Poor dimensional distribution of the output sequence.Lack of uniformity of distribution for large quantities of generated numbers.Shorter-than-expected periods for some seed states (such seed states may be called 'weak' in this context).In practice, the output from many common PRNGs exhibit artifacts that cause them to fail statistical pattern-detection tests.

2 Generators based on linear recurrences.John von Neumann cautioned about the misinterpretation of a PRNG as a truly random generator, and joked that 'Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin.' In general, careful mathematical analysis is required to have any confidence that a PRNG generates numbers that are sufficiently close to random to suit the intended use. Good statistical properties are a central requirement for the output of a PRNG. Cryptographic applications require the output not to be predictable from earlier outputs, and more elaborate algorithms, which do not inherit the linearity of simpler PRNGs, are needed. for procedural generation), and cryptography. for the Monte Carlo method), electronic games (e.g. PRNGs are central in applications such as simulations (e.g. Although sequences that are closer to truly random can be generated using hardware random number generators, pseudorandom number generators are important in practice for their speed in number generation and their reproducibility. The PRNG-generated sequence is not truly random, because it is completely determined by an initial value, called the PRNG's seed (which may include truly random values). For the formal concept in theoretical computer science, see Pseudorandom generator.Ī pseudorandom number generator ( PRNG), also known as a deterministic random bit generator ( DRBG), is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. This page is about commonly encountered characteristics of pseudorandom number generator algorithms.

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