I have at least one issue with this, depending on how broad your exclusion of "computational chemistry and physics from all this" is meant to be.

My "view" is very narrow. Stops at the protein level. Maybe at the cell membrane level.

Aren't you projecting from population genetics to computer simulations in general? Is this fair? How does the existence of more or less robust theories in a field affect your claim?

You might be correct. I take the bias from my background (which actually includes also a similar field in the spread of drug resistance). I don't want to go into social issues for now, but my argument is complexity based: Our computational feasible models of chemical reality (sorry I have some old background in theoretical chemistry, the physics that I know comes mostly from there) can't even simulate a large molecule reliably for more than a few seconds, that I suggest that the the bigger the system, the most approximations have to be done, the bigger the possible mistake.
By the way, what about the butterfly effect? Especially in the context of simulations that are approximate...
Any way, your argument carries weight and I will need some time to think about it.

Anyway people use computer models to make decision on things from drug policies to climate change, to finance quantitative methods (other people here are more prepared than me to talk about quants). I think there is enough practical evidence for not taking the results are the gospel.

In fact your case seems to me, to be a discussion on how one cannot extract a theory by statistical means alone. Or how a lack of theory makes models blind. A theory lacking depth, leads to models lacking depth.

I am inferring correctly that more depth will imply more complexity? The truth is that we are dealing with a system with a mass amount of variables and a mass amount of unknowns. Many things are not known (and are not knowable) and most inference methods are dependent on assumptions about unknown parameters. For instance some models require a certain demography, but we really don't know the past demography of many species.

By the way that is much modeling done on medicine (now talking about another domain, which I know something) where many fundamental variables are not known: pharmacokinetics and pharmacodynamics of drugs plus their mechanisms of action are many many times not know, just speculated. How can you reliably model this? Even when you know, how can you study the epidemiological behavior of a certain disease to a predicable level on real scenarios on the terrain when you sometimes have unpredictable events like wars causing massive changes in reality? Again, I am not saying that models are useless, but to predict the future?

Anyway. The proof of the pudding is in the eating: Epistimology aside, doesn't the predictive ability (where it is an aim) of a model, validate at least practically its usefulness?

Take meteorology. I don't think there is any doubt (correct me if I'm wrong) that weather forecasting has

For climate modeling I don't know anything at all. For metereology, at least in Liverpool, the BBC cannot give an accurate prediction on rain with hours of distance ;) . This one I am painfully aware.


Of all forms of caution, caution in love is perhaps the most fatal to true happiness - Bertrand Russell