I am sympathetic to the sentiment underlying this post, but I would stress that the value of “realism” depends on what you’re trying to model, and why. If your purpose is to generate reasonable solutions to non-extreme problems/parlor games, then you can lose your purpose by artificially hardening your models beyond what such parlor games require. But if your purpose is to find generally-applicable decision rules that will be robust to extreme circumstances, then you can lose by failing to harden your models sufficiently.
Is there any reason to believe that there are generally-applicable decision rules that will be robust to extreme circumstances, and yet are simple enough to use for the vast majority of non-extreme circumstances?
I am sympathetic to the sentiment underlying this post, but I would stress that the value of “realism” depends on what you’re trying to model, and why. If your purpose is to generate reasonable solutions to non-extreme problems/parlor games, then you can lose your purpose by artificially hardening your models beyond what such parlor games require. But if your purpose is to find generally-applicable decision rules that will be robust to extreme circumstances, then you can lose by failing to harden your models sufficiently.
Is there any reason to believe that there are generally-applicable decision rules that will be robust to extreme circumstances, and yet are simple enough to use for the vast majority of non-extreme circumstances?