The concept of "randomness" in computing is quite fascinating and involves both mathematical theory and practical implementation considerations.
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Deterministic Algorithms: In most computational contexts, what we refer to as "random numbers" are generated by algorithms known as Pseudorandom Number Generators (PRNGs). These algorithms produce sequences of numbers that appear random but are actually determined by an initial value called a seed.
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Seeding: The PRNG starts with this seed value and uses a deterministic process to generate the sequence. If you use the same seed, the PRNG will produce the same sequence every time.
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Statistical Properties: Well-designed PRNGs aim to mimic true randomness by passing various statistical tests that evaluate their ability to distribute numbers uniformly across possible values without discernible patterns.
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Physical Phenomena: Unlike PRNGs, True Random Number Generators (TRNGs) rely on physical processes, such as electronic noise or radioactive decay, which are inherently unpredictable and not deterministic.
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Usage Scenarios: TRNGs are often used in cryptographic applications where the unpredictability of random numbers is crucial for security.
The scientific approach to randomness involves understanding how systems can appear random due to complexity or chaotic behavior (e.g., weather patterns) but still follow underlying laws of physics or mathematics. This duality is at the heart of why we differentiate between pseudorandom and true random processes in computing.
In summary, while computers use deterministic algorithms for most practical applications of randomness, there are ways to achieve true randomness through physical phenomena when needed. The choice depends on the requirements of your application, particularly concerning predictability and security needs.