Some people would say that understanding (some aspect of) the past is an exercise in data compression: A good model is one which implies (generates) the past, but has a much simpler structure.

While I understand that reasoning and partially agree with it, I would say that is worth pointing out that we are far from having complete information about the past  (if that was such the case, courts would have 90% of the work done).

In fact you can create models of the past that are perfect at regenerating it and still be completely oblivious to the (hidden) fundamental variables. Actually, when you increase the complexity of a model, you have more chances of finding a "correct" recreation of the past as the model is able (just by the added complexity) to search a big chunk of the search space.

I have nothing against models (especially "rough" models are fundamental), I just think their use and the expectations on their ability to recreate the past/predict the future are highly overrated.

Believe it or not, I am currently doing sensitivity analysis to a model (studying the spread of malaria drug resistance considering different drug deployment policies).