Random number generation is a fascinating concept that has been widely studied in computer science. Whether true randomness exists or whether everything can be theoretically calculated in advance is the subject of debate. But is it all so ambiguous?
Pseudorandom number generators (PRNGs)
In practice, random number generators are algorithms designed to produce sequences of numbers that appear to be random. However, they are usually deterministic in nature, which means that they produce the same sequence of numbers for the same initial condition or initial value. Such PRNGs are known as pseudorandom number generators (PRNGs) and are widely used in computer programs.
PRNGs generate numbers based on mathematical formulas and initial conditions such as system time, process ID, or other parameters. Although the generated sequence may seem random for many purposes, it is theoretically possible to predict it if you know the algorithm and initial conditions.
True random number generators (RNGs)
True randomness, on the other hand, is a concept that implies the absence of any regularity or predictability. True random number generators (RNGs) seek to generate truly random numbers by relying on inherently unpredictable physical processes, such as radioactive decay, atmospheric noise, or electronic noise.
PRNGs provide a higher degree of unpredictability than PRNGs, but they often require specialized hardware to record random events. These hardware DSPs are considered more reliable in terms of generating true randomness. But is true randomness true?
It is important to note that even DSPs may have limitations and may not achieve perfect randomness due to various factors, such as errors in the measurement process or potential flaws in implementation. Does true randomness even exist in our world?
Random number generation, commonly used in computer systems, is based on deterministic algorithms. These algorithms tend to produce sequences of numbers that appear to be random, but they can be predicted if one knows the algorithm and initial conditions.
Cryptographic algorithms and pseudorandomness
There are also cryptographic algorithms that can generate pseudorandom numbers that have certain desirable properties for security purposes. These algorithms use complex mathematical functions and secret keys to generate sequences of numbers that are statistically indistinguishable from true random numbers. Although they are not truly random, they can effectively serve the purposes of cryptographic protocols.
The nature of physical processes
It is generally accepted that true randomness is theoretically possible due to physical processes. But are physical processes themselves random?
Physical processes, as we understand them, can exhibit a combination of deterministic and random behavior. Although some physical processes may seem random in practice, they are often influenced by underlying deterministic laws and can be influenced by various factors.
For example, radioactive decay is often considered a random process because it is impossible to predict exactly when a particular radioactive atom will decay. However, the decay itself follows a probabilistic pattern governed by the laws of quantum mechanics. Similarly, atmospheric noise or electron noise, which can be used to generate random numbers, is influenced by many factors, but can still exhibit statistical randomness.
Rethinking true randomness
Does true randomness exist or is it the result of our limited understanding and inability to accurately measure and predict certain phenomena? Particle behavior at the fundamental level also relies on hidden variables or deterministic processes that are currently beyond our knowledge or measurement capabilities. But this, in fact, does not make physical processes really random. Physical processes are also pseudo-random in nature. Which, of course, leads to the fact that all events in our world are also pseudo-random in nature, which ultimately overturns our awareness and understanding of our world as such.
Conclusion
The question of true randomness in physical processes is a complex and philosophical one. Although physical processes may exhibit seemingly random behavior, they may still be influenced by underlying deterministic laws or hidden factors that we have yet to discover or fully understand. Consequently, physical processes themselves can be considered pseudo-random in nature, which challenges our traditional understanding of the world.
