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Tow things. First, probability is real: it's a construct in one's mind, and minds are just as real as dice or coins. https://www.lesswrong.com/posts/f6ZLxEWaankRZ2Crv/probabilit... Second, (and the author may be leading up to this), there's Solomonoff's theory of inductive inference, which he has proven complete: when we apply Occam's razor (where the prior probability of each possible theory drops exponentially with its size), the amount of error a perfect Bayesian makes as they observe event and bet on the next one, ad infinitum, is finite. Roughly proportional to the complexity of the simplest theory that correctly predict the whole sequence of events. It's one of the most convincing proofs that Bayesian reasoning works. https://en.wikipedia.org/wiki/Solomonoff's_theory_of_inducti... There's just a little snag. Perfect Bayesian reasoning is impossible to compute, so us mortals have to resort to approximations. Just as perfect certainty isn't possible, perfect reasoning is not attainable. Oh well. |
There's a little assumption that you're leaving out, namely that the Kolmogorov complexity of the data generating process is finite. From Wikipedia:
> expected cumulative errors made by the predictions based on Solomonoff's induction are upper-bounded by the Kolmogorov complexity of the (stochastic) data generating process
Whether the universe (or our observations of the universe) have finite complexity is very much an unresolved philosophical question.