I disagree. There's a large grey area between 'completely unknown' and 'scientific certainty'. I prefer guessing and doing computations with my guesses than thr

I disagree. There's a large grey area between 'completely unknown' and 'scientific certainty'. I prefer guessing and doing computations with my guesses than throwing my hands up in the air and calling it unknowable. When presented with new evidence it's better to write a number down for your degree in belief in X, ask how much you should change that belief based on the new data, and update your probability estimate, than to just go with your feelings. You're not doing actual bayesian computations, that's totally untractable for anything that's not a very well defined problem, but doing 'pseudo-bayesian' updating is better than not. I think of it as the fermi approximation of probabilities. You won't get accurate numbers that way but you'll get better numbers than if you just invent the answer. EDIT to add: most of the time you should then throw out the number. Just like a fermi estimate, you get a ballpark sense for the answer, not a precise answer. In the superforecasting experiment by Tetlock the best forecasters did this. They were writing down probability estimates and methodically updating them based on new data (news articles, data, etc.). They were forecasting geopolitical events, not dice rolls, and it worked (better than the alternative, obviously no one can forecast geopolitical events with high certainty).