The Myth of True Randomness in Humans and Computers

Randomness, often invoked to explain the unpredictability of human actions and natural phenomena, is a concept that provokes deep philosophical and scientific inquiry. However, it is crucial to understand that true randomness is a myth, both in human behavior and computer algorithms. This article explores why randomness is illusory and provides a clearer understanding of the concept of pseudo-randomness.

Understanding True Randomness

True randomness is defined as an event or phenomenon that occurs without any predictability, causing outcomes that are completely uncertain and unfathomable. However, in a deterministic universe governed by cause and effect, true randomness seems an impossible concept. Every action, whether performed by a human or a computer, is influenced by prior states and governed by underlying rules or causes, making it appear highly unlikely that an event could occur without a cause or predictability.

The Role of Human Actions and Free Will

Humans often attribute their actions to a form of free will, suggesting that choices are made without any predictability. However, even this notion of free will is challenged by the interneuronal connections in the brain, which determine the exact sequence of actions based on the current state of the brain's neural networks. No action could have been done other than the exact one that was done, underlining the deterministic nature of human behavior.

Computer Random Number Generators

Contrary to popular belief, computer-generated random numbers are not truly random. They are produced through algorithms that generate sequences of numbers that mimic randomness. These algorithms, while complex, follow specific rules and thus are not truly unpredictable. Even seemingly random physical phenomena, such as a pebble rolling down a hill or a stick tossed into the air, are influenced by external factors and initial conditions, making their outcomes predictable given enough information.

The Illusion of Randomness in Physics

The concept of true randomness is further challenged by the field of quantum mechanics. Many believe that true randomness occurs at the quantum level, particularly due to the indeterminacy postulated by Heisenberg's Uncertainty Principle. However, even at this level, quantum events, while often unpredictable when examined in isolation, are constrained by statistical principles. Particles do not behave in a completely random manner; they follow stochastic behavior, where individual samples are unpredictable but groups exhibit statistical regularities. This is similar to the chaotic systems in mathematics, where deterministic rules can lead to complex, seemingly random patterns.

Chaos and Pseudo-Randomness

Chaos theory provides another perspective on randomness. Chaotic systems, even if deterministic, can exhibit complex behaviors that appear random due to the sensitivity to initial conditions. This sensitivity means that tiny differences in initial conditions can lead to vastly different outcomes, making long-term predictions impossible. Therefore, while chaos is not truly random, it can create the illusion of randomness and is often used in pseudo-random number generators (PRNGs).

Implications and Conclusion

The lack of true randomness in both humans and computers has significant implications for fields ranging from cryptography to artificial intelligence. PRNGs, while not truly random, provide sufficient unpredictability for most practical purposes. The myth of true randomness underscores the importance of understanding the underlying deterministic processes that govern our world.

Furthermore, the concept of pseudo-randomness highlights the limitations of current scientific and technological approaches. While true randomness might be an ideal concept, our understanding of complex systems, from human behavior to quantum mechanics, suggests that true randomness is a myth. Instead, we are better served by embracing and understanding the true randomness that exists in chaotic and stochastic systems.