[api]

This is known to anyone who's ever monkeyed with any type of machine learning: genetic algorithms, Bayesian filters, anything.

I agree with many of the commenters in this article. This should be common knowledge.

I also, like many commenters, couldn't help but think of model-based climate predictions.

[roel_v]

The problem is that you're comparing statistical methods with process-based methods. The mathematically inclined tend to have a reflex to approach modeling wiht this sort of black box methods. The thing is that for modeling processes like geomorphology, hydrology but also less quantitative processes like quality of life in urban environments, black box methods cannot be verified nor reasoned about - with issues like overfitting etc. becoming a problem.

On the other hand, you can model by building conceptual models, calibrating them by hand (using computer methods for the number crunching only) and reasoning about divergences between model results and observed data rather than computing them away with raw power. This is what modeling should be about - a tool for understanding.

(this topic is dear to my heart - I have had this discussion so often. Models are not crystal balls, they are tools for understanding processes. Which is why I am so desperate when another economist, mathematician or computer scientist stands up and wants to model processes that require understanding with their barbaric brute force statistical methods to not have to study things that are outside of their comfort zone. When all you have is a hammer etc.)

[winestock]

"All models are wrong. Some models are useful." - George Box