Pseudorandom number generator
algorithm that generates a sequence of numbers whose properties approximate those of sequences of true random numbers
A 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. The PRNG-generated sequence is not truly random, because it is completely determined by an initial value or state, usually called the PRNG's seed (which may be based on truly random values). 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.
PRNGs are central in applications such as simulations (e.g. for the Monte Carlo method), electronic games (e.g. for procedural generation), and cryptography. 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.
Appropriate statistical properties are a central requirement for the output of a PRNG. Careful mathematical analysis is required to have reasonable confidence that a given PRNG generates numbers that are sufficiently close to random to suit the intended use. John von Neumann cautioned about possible misinterpretation of a PRNG as a truly random generator, joking that "Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin."
Potential issues
In practice, the output from many common PRNGs exhibit artifacts that cause them to fail statistical pattern-detection tests. These include:
Shorter-than-expected periods for some seed states (such seed states may be called "weak" in this context);
Lack of uniformity of distribution for large quantities of generated numbers;
Correlation of successive values;
Poor dimensional distribution of the output sequence;
Distances between where certain values occur are distributed differently from those in a random sequence distribution.
Begin with the source’s own compact description: “Pseudorandom number generator” is algorithm that generates a sequence of numbers whose properties approximate those of sequences of true random numbers. The dossier treats that line as a proposition to test through Pseudorandom, number and generator, not as a finished interpretation.
Why this record matters
The phrase “algorithm that generates a sequence of numbers whose properties approximate those of sequences of true random numbers” supplies a clear boundary for inquiry. It also exposes the unanswered questions: who defined that boundary, when it became stable and which sources sit outside it.
The citation trail is more important than the brevity of the summary: it shows where individual claims can be examined in context. The source revision retrieved here is dated Sep 4, 2026. The linked authority identifier is Q1623338. None of the 0 selected statements returned an explicit reference.
The absence of detail may reflect summary conventions rather than a lack of surviving documentation. The source lead contains qualifying language; that uncertainty should survive quotation, summary and reuse. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.
How to read it
Use the entry as an orientation point, then follow its citations and revision history. Names, dates and institutional relationships should be checked against the original record.
- Subject orientation
- Search vocabulary
- Locating named sources
The closest primary source, responsible institution and strongest cited specialist reference.
Three-step research path
- Establish the record: confirm the title “Pseudorandom number generator”, its source revision and the description used here.
- Expand the search: follow Pseudorandom number generator primary sources, Pseudorandom number generator archive and Pseudorandom research across catalogues and specialist indexes.
- Test the account: compare the strongest cited source with the responsible institution’s current record and note any disagreement.
Questions for further research
- Which source most directly establishes the central claim about “Pseudorandom number generator”?
- Which cited source is closest to the event, object or claim?
- What terminology or title could unlock a more precise catalogue search?
Search terms from this dossier
This entry incorporates text from “Pseudorandom number generator” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.