Earlier quoted context omitted.
The point of the model is absolutely not to reduce uncertainty, it is to quantify it, which are two very different things. No model reduces uncertainty in a probabilistic sense. And no, you don’t need statistics or machine learning to say “there is a lot of uncertainty”, but you do in order to quantify that uncertainty.
Say I have two models - model A returns around 20% likelihood that the top team wins the world cup, and model B returns around 80% likelihood. I use both of the modeling techniques a few thousand times in various parallel universes, and both of them are exactly right - 20% of 20% predictions result in a win, and so on. Despite them both quantifying uncertainty accurately, isn't model B still better?
Re: Goldman Sachs model to predict World Cup game results didn’t come close
#131No, because if the underlying phenomenon happened 20% of the time, that’s what you want your model to predict. The point of the model is to describe reality as accurately as possible. So a model that predicts a particular outcome to happen 80% of the time, and the outcome actually does happen 80% of the time, isn’t any better or worse than a model that predicts an outcome to happen 20% of the time that happens 20% of the time.