[pizza]

I think there's nuanced distinctions between:

- something that can't be modeled because there's no training data

- something that can't be modeled because it's fundamentally stochastic

- something that can't be modeled because the discrepancy in simulating the generating process, for your specific model, can, basically, be made arbitrarily large

[paulmd]

why can fundamentally stochastic things not be modeled? monte carlo simulations were literally some of the first computer programs.

[pizza]

can't be modeled is probably not what I should have said, rather I meant more like the error for a globally optimal model is still high