I think this probably depends on the field. In machine learning, solving problems under worst-case assumptions is usually impossible because of the no free lunch theorem. You might assume that a particular facet of the environment is worst-case, which is a totally fine thing to do, but I don’t think it’s correct to call it the “second-simplest solution”, since there are many choices of what facet of the environment is worst-case.
Even in ML it seems like it depends on how you formulated your problem/goal. Making good predictions in the worst case is impossible, but achieving low regret in the worst case is sensible. (Though still less useful than just “solve existing problems and then try the same thing tomorrow,” and generally I’d agree “solve an existing problem for which you can verify success” is the easiest thing to do.) Hopefully having your robot not deliberately murder you is a similarly sensible goal in the worst case though it remains to be seen if it’s feasible.
Even in ML it seems like it depends on how you formulated your problem/goal. Making good predictions in the worst case is impossible, but achieving low regret in the worst case is sensible. (Though still less useful than just “solve existing problems and then try the same thing tomorrow,” and generally I’d agree “solve an existing problem for which you can verify success” is the easiest thing to do.) Hopefully having your robot not deliberately murder you is a similarly sensible goal in the worst case though it remains to be seen if it’s feasible.