True Random

Welcome one and all to another thought-journey. It’s been a relatively long time, so let’s do this right. We’ll start, as I tend to, with a story.

My roommate and I were walking home from a friend’s apartment. It was 2:30 in the morning, but bright from the street lights so that not a star in the sky could be seen. We were crossing a relatively empty road, no cars, and only a few other late-night travellers.

One of them, walking alone up the street towards the Sylvan dorm system, was singing. He was singing loudly, and in spanish.

I remarked that it was surprising; my roommate disagreed. It appeared to Steve that if a body was already walking up the street alone at 2:30 AM, it was no longer all that surprising for him to be singing at the top of her lungs in Spanish.

I took up the position that it is uncommon for a person to be walking at 2:30 AM, and it is even less common for a person to sing at the top of their lungs in Spanish, therefore it was much more suprising for it to happen at the same time.

This was not the key to our disagreement however; we came upon it next.

In order to explain my argument, I assigned somewhat arbitrary probabilities to each of the events. There might be a 30% probability that a person might find themselves walking alone at 2:30AM. It is uncommon, yes, but not unheard of. However, it is far more uncommon, say, 10% probability, to run into a person in this country singing at the top of their lungs in spanish. Now, if you combine the two probabilities, you have the probability of singing multiplied by the probability of walking at 2:30AM, which brings your probability down to just 3%, which is a startling occurence, because 97% of the time this will not happen.

All of this doesn’t matter. Steve’s response to this explanation was that however logical, he didn’t like the idea of assigning numbers to people.

This is a fairly computer science-wide concept, that probabilities can account for random, and that models can accurately predict what will happen given a scenario and a sufficiently detailed set of training data.

But it does not account for the possibility that a human could be an entirely non-deterministic machine, that there may be such a thing as a Soul, or True Random, that the universe itself does not boil down into one long and elegant math equation which can accurately predict the action of all matter an energy at any point in space and time.

I am actually of the opinion that a human can have a soul, be non-deterministic, and still fit into a probabalistic model. That is not to say that I necessarily believe in the soul, but I believe that the most anyone can ever come to is an estimation of what one will do given circumstances and previous data.

The difference here is almost certainly a matter of semantics, coming to light only because of my background as a Computer Science major versus his as a Philosophy minor. But while it’s subtle, our views allow us an entirely different terminology to express what we think is human at all. Mine is guided by math; by models, bayesian networks, and guesswork, while his is guided by philosophy; by Plato, Aristotle, John Locke, and guesswork.

We’re both at once freed and limited by our fields.

I wonder if this is the Newspeak Effect; George Orwell’s theory that if we lack the words to think of a thing, we won’t think it. Or perhaps it’s sort of a Newspeak Effect in reverse; if we have too many of the words to think of a thing in some specific way, we cannot help but think of it in (more or less) that way.

Effectively… I don’t know.

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