[bambax]

> The model said, that there is a lot of uncertainty, and as it happens, it was entirely correct. A World Cup chance of 18.5 percent means, that 4 out of 5 times the team will not win, and that that is the highest chance does not say much about the model.

But do you need a sophisticated model and lots of so-called "AI" to arrive at the conclusion that there's a lot of uncertainty?? The point of the model is to reduce uncertainty, not find that it's there and do nothing about it.

[thousandautumns]

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.

[sandov]

I think the right way to measure the correctness of the model is to compare it with various other predictions:

-Predictions from the general public

-Predictions from football experts

-Predictions from other mathematical models

For example: If over time, the new model is 5% better than the best of the old models, then it's very good.

Doesn't make much sense to compare it with reality and jump to the conclussion that the model doesn't work because no prediction can be 100% accurate.

[jtolmar]

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?

[ghayes]

Think about the actual uncertainty since anything can happen in the game itself. The easiest way to look at this is to play the game 100 times in a row (preferably in parallel universes, as you say). If team A wins in 60% of games, then that caps the ability to predict the result. You can predict a die roll to be 6 with an certainty of 17%. You can’t do any better.

[jtolmar]

Say I have a bag of dice one of each of the usual D&D denominations (d4, d6, d8, d10, d12, d20). I draw one at random, ask the models for predictions, and roll it. Model A ignores the information about which one I drew, and predicts a correct distribution of rolls (12.9% chance of rolling a 6). Model B correctly processes the information about which one I drew, and predicts a correct distribution given that information (I drew the d6 so 17% chance of rolling a 6). Both models give correct results overall, but Model B has higher probabilities on average, and I would say it is a better model.

A model should be judged both on how accurately it characterizes its uncertainty and how much evidence it's able to successfully make use of.

[thaumasiotes]

You can do better if you have foreknowledge or retroactive foreknowledge of the outcome of the die roll, which is the obvious suggestion of jtolmar's comment. If I know the recorded outcomes of a sequence of die rolls, I can have models that predict those outcomes to any accuracy I want. But they're not doing it by measuring the uncertainty involved in prospectively rolling the die.

[thousandautumns]

No, 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.