Pixel,
- I do think that this is the weakest of my interim conclusions. But so far, I still think that I don't need to be a special case in this case.
- See if this works.
- Hypothesis A has a prior probability of 55%; naturally enough, ~A has a prior probability of 45%.
- We do an experiment and get a result that approaches zero likelihood if A is true.
- You have to bet the farm on A or ~A -- it's up to you.
- Which would you bet your farm on?
Jabba, your post suggests that you don't understand a fundamental principle of Bayesian inference, namely, that the amount of change in probability from prior to posterior depends on the relative probability of the evidence under each hypothesis. Your question is unanswerable because you did not say how likely the evidence is under ~A. Even though the evidence is improbable under A, it could be even less probable under ~A, making the posterior probability of ~A greater than that of A, and hence making A the better bet.
As for the you-being-special fallacy, it has been explained to you an uncountable number of times, including at least once by me. The "special" fallacy is easier to see if you apply your formula to someone other than yourself. Whether or not you think you're special, hopefully you have the humility to admit that you or no more or less special than any other individual. Therefore, you should arrive at the same posterior whether you apply your formula to you or someone else. Therefore, let's apply your formula to your nearest neighbor, the person living geographically closest to you, but not in your own home. Let's say this person is "Jack." Under the hypothesis that Jack was randomly produced the probability of his existence would be tiny (according to your "scientific" model), but the fallacy is that even if Jack had never been born, you would always have some nearest neighbor (unless you were literally the last person on earth), thus no matter who your nearest neighbor was, you would always observe a nearest neighbor. Therefore, your evidence is not that Jack is a nearest neighbor; it is that you have some nearest neighbor. That likelihood is basically 1 under both of your hypotheses. Therefore the likelihood ratio is 1, and the posterior probabilities of your hypotheses equal their priors. The evidence has no power to discriminate between the hypotheses.
It is confusing when you apply the formula to yourself because it is unclear what the alternative would be if you had not been born. But, when you attempt to apply the formula to someone else it is easy to see that if that specific person wasn't there, then you would simply apply the formula to someone else. There would always be someone to apply the formula to; hence the conditional probability of the evidence is 1 under any hypothesis about a world in which there is someone to ask the question.
It's even easier to understand the fallacy using a deck of cards. You shuffle a deck, deal out all the cards, and observe the sequence 2H, KC, AS, 5H, 5D, 7H, . . . . The probability of that particular sequence is 1 in 8 × 10
67, or in your terms "essentially zero." Yet if the shuffle hadn't produced that particular sequence it would have produced some other one. Do you think shuffle sequences have souls? I doubt it. Hopefully you can see the fallacy. You would have observed
some sequence. Therefore, your actual evidence is simply that a sequence was observed, not a specific sequence. The conditional probability that a sequence is observed under the hypothesis that the shuffle was random is 1. Analogously, the conditional probability that you would observe some human being under the hypothesis that individuals are randomly produced is 1. It's just like the shuffle.