In counterfactual regret minimization and in reinforcement learning this shows up too. The solution there tends to discount early iterations with something like

In counterfactual regret minimization and in reinforcement learning this shows up too. The solution there tends to discount early iterations with something like a weighted average. It makes convergence to the true values happen faster. A similar thing happens in k mean clustering but is more horrifying because you can get arbitrary wrong solutions rather than just taking longer to converge. The solution there is to construct a sampling distribution when making your initial decision - instead of making a decision according to the principle of indifference to instead make a decision after getting more informed about the problem. This doesn't mean you don't have bad solutions, but it gives a bounding on just how bad those solutions can be. It happens in other places too, I'm sure, because this isn't really a software problem - this is a fundamental problem. Uniformed choices are uninformed but influence the choices that come after them. Certain heuristics like delaying your decision to the last responsible moment can help to avoid making your decisions with insufficient information, but it is important to realize the type of decision you are making. Are you in a case where getting things wrong just means you go slower but eventually converge? Or at you in a case where you can't reverse course and will have your outcomes determined by the decision you make without any guarantee that it will turn out well and no simple path backward. I hear people talk about this with terms like "one way doors" versus "two way doors". In the latter case you are much better off thinking more deeply through the problem. We're not really solving new fundamental problems. We're solving fundamental problems in a new context.