The problem is, unsophisticated models do not predict anything. You apply them in one country and they do ok, and apply them in another and they get it totally and completely wrong.
Unless all important factors are accounted for, they are going to result in incorrect information for someone. Public policy will then be based on incorrect predictions. People will grow tired of the predictions being wrong and they'll give up on data science entirely.
It's already quite bad that people think they can choose their reality by finding numbers that agree with them and ignoring the ones that don't.
I do understand the point you are making, which is like the epicycles argument. But in global warming and epidemics alike, more parameters are actually needed to model reality.
I do agree that those parameters should be based on actual data, not guesses though. But what value of R would you pick? Is that actually well-constrained?
Unless all important factors are accounted for, they are going to result in incorrect information for someone. Public policy will then be based on incorrect predictions. People will grow tired of the predictions being wrong and they'll give up on data science entirely.
It's already quite bad that people think they can choose their reality by finding numbers that agree with them and ignoring the ones that don't.
I do understand the point you are making, which is like the epicycles argument. But in global warming and epidemics alike, more parameters are actually needed to model reality.
I do agree that those parameters should be based on actual data, not guesses though. But what value of R would you pick? Is that actually well-constrained?