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Probabilities makes sense only with absolutely certain things like a fair coin or a dice. In cases where there is no absolute certainty about how many sides or

Probabilities makes sense only with absolutely certain things like a fair coin or a dice. In cases where there is no absolute certainty about how many sides or dimensions your "dice" has and that it is not biased and that there is no other forces or factors in play probability ceases to make sense. Probability of A, given B becomes meaningless when either A or B aren't precisely defined (like in the case of a "fair coin") and so is the relationship between the two. Application of the Bayesian rule to "estimated" probabilities is just wrong and unscientific (in the face of ambiguity avoid the temptation to guess). Multiplying and dividing nonsense by nonsense yields nonsense. The global financial crisis and recent cock-sure consensus about outcome of the brexit referendum the day before voting are good evidences.

greenshackle 10y ago
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).
Houshalter 10y ago
That's wrong. With the Bayesian interpretation of probability, you can assign probability to any event. In fact all certainty about belief is just probability in disguise. There were betting markets for the brexit vote. They assigned 25% probability to brexit. Of course markets aren't perfect. But anyone who really believes they know better should be able to get rich off them. And somehow that doesn't happen. So they are the best estimates of probability we have.

greenshackle 10y ago

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).

Houshalter 10y ago

That's wrong. With the Bayesian interpretation of probability, you can assign probability to any event. In fact all certainty about belief is just probability in disguise. There were betting markets for the brexit vote. They assigned 25% probability to brexit. Of course markets aren't perfect. But anyone who really believes they know better should be able to get rich off them. And somehow that doesn't happen. So they are the best estimates of probability we have.