Furthermore, I remind one and all that Bayes’ Theorem is asymptotic. Even if the conditions hold, the “true” probability is approached only in the infinite time horizon. This could occur so slowly that it might stay on the “wrong” side of 50% well past the time that any finite viewer might hang around to watch.
There is also the black swan problem. It could move in the wrong direction until the black swan datum finally shows up pushing it in the other direction, which, again, may not occur during the time period someone is observing. This black swan question is exactly the frame of discussion here, as it is Taleb who has gone on and on about this business about evidence and absence thereof.
You cannot predict a black swan. That’s why it can screw up your expectation.
However, once you have a black swan you’d be an irrational fool not to include it in your expectation.
That’s the point. That’s why theories get updated—new data that nobody was aware of before does not match expectations. This new evidence adjusts the probability that the theory was correct, and it gets thrown out if a different theory now has a higher probability in light of the new evidence.
This is not a shortcoming of Bayes Theorem, it’s a shortcoming of observation. That you should certainly be aware of. I.e. “I might not have all the facts.”
Furthermore, I remind one and all that Bayes’ Theorem is asymptotic. Even if the conditions hold, the “true” probability is approached only in the infinite time horizon. This could occur so slowly that it might stay on the “wrong” side of 50% well past the time that any finite viewer might hang around to watch.
There is also the black swan problem. It could move in the wrong direction until the black swan datum finally shows up pushing it in the other direction, which, again, may not occur during the time period someone is observing. This black swan question is exactly the frame of discussion here, as it is Taleb who has gone on and on about this business about evidence and absence thereof.
You cannot predict a black swan. That’s why it can screw up your expectation.
However, once you have a black swan you’d be an irrational fool not to include it in your expectation.
That’s the point. That’s why theories get updated—new data that nobody was aware of before does not match expectations. This new evidence adjusts the probability that the theory was correct, and it gets thrown out if a different theory now has a higher probability in light of the new evidence.
This is not a shortcoming of Bayes Theorem, it’s a shortcoming of observation. That you should certainly be aware of. I.e. “I might not have all the facts.”