Earlier quoted context omitted.
Are you sure there is even a philosophical difference there? I'm still going to use a program to do the heavy lifting of calculating the errors. I'm happy to argue that there is no philosophical difference; if both approaches are feasible and take comparable amount of time to implement then one gets the exact answer to the question at hand and the other is a poor man's shortcut for people who aren't confident in thei…
I think the value is in checking assumptions - if the analytical approach assumes a normal distribution and then you do MCM and get a vastly different result then it is probably worth checking the assumptions made by the analytical model.
If a modeler has assumed two different models of a situation and are getting vastly different results then that is evidence something is wrong, but it is also evidence that the modeler is out of their depth. It is not appropriate to fit two different models and then claim that the differences are delivering insight. The differences are revealing big gaps in the modelers understanding of key influences, rendering both models highly suspect. They should not be creating models, they should be putting more time into understanding the thing they are modeling.
There are times when a modeler would have two different models, but they should certainly not be surprised that they give different results. Indeed, they should be assuming two different models because they are going to give completely different results. There will probably be other edge cases, but in my experience they are rarer than people just making mistakes about where MCM is appropriate.