On Randomness and Certainty

Let’s say you read a headline announcing an advance in the way we generate random numbers. You might think to yourself, "First of all, why do we need random numbers in the first place?" Random numbers are important for many science experiments, and the field of statistics is ultimately based on randomness. Scientists and mathematicians need random numbers as a part of their toolbox in order to do good work. Next, you might ask, how do we get random numbers then? Old school methods involve the flipping of a coin, or the rolling of dice. These methods take a lot of time if you’re trying to produce a huge list of random numbers. Nowadays we use computers. The problem is, even computers produce results that are predictable. Hard to predict, but predictable nonetheless.

What are the chances? You mean to tell me that we can’t actually produce randomness? The paradox is that computers generate random numbers based on an algorithm. Everything they do is based on an algorithm, a set of instructions. Yet, nowhere in a set of instructions can it say, “Generate a random number.” Algorithms cannot think for themselves; technically, we the human programmers do the thinking. (And yet even we can’t generate random numbers.) I’m starting to confuse even myself here, so I should cut to the chase.

In a world where information avails itself to us in an ever-accelerating fashion, we might be led to think that one day we will be sure about everything – a  theory of everything, an omniscience of all future events. But if we remember that science – the thing we use to “be certain” about things – needs randomness to work, and yet we don’t really know how to get absolute randomness, then we can temper our visions of a fully programmed world where all existence is automated. Uncertainty will always be with us.

Notes:

Random number generator 'improved'

May 2016, BBC News