SUMMARY
A random number generator (RNG) will eventually repeat its outputs due to the finite number of possible states it can produce. Pseudo-random number generators (PRNGs), commonly used in digital computing, are deterministic and will cycle through a limited set of numbers, leading to repetition. True random number generators, which draw from physical phenomena, can theoretically avoid repetition but are impractical for infinite outputs. The discussion highlights the mathematical implications of randomness, particularly in relation to the work of Nassim Nicholas Taleb, author of "Fooled by Randomness."
PREREQUISITES
- Understanding of random number generation concepts
- Familiarity with pseudo-random number generators (PRNGs)
- Basic knowledge of probability theory
- Awareness of Nassim Nicholas Taleb's work on randomness and uncertainty
NEXT STEPS
- Research the differences between pseudo-random and true random number generators
- Explore the mathematical foundations of randomness and probability distributions
- Study Nassim Nicholas Taleb's "Fooled by Randomness" for insights on randomness and decision-making
- Investigate physical sources of randomness for cryptographic applications
USEFUL FOR
Mathematicians, computer scientists, software developers, and anyone interested in the principles of randomness and its implications in computing and decision-making.