I omitted the “|before” for brevity, as is customary in Bayes’ theorem.
That is not correct. The prior that is customary in using Bayes’ theorem is the one which applies in the absence of additional information, not before an event that changes the numbers of observers.
For example, suppose we know that x=1,2,or 3. Our prior assigns 1⁄3 probability to each, so P(1) = 1⁄3. Then we find out “x is odd”, so we update, getting P(1|odd) = 1⁄2. That is the standard use of Bayes’ theorem, in which only our information changes.
OTOH, suppose that before time T there are 99 red door observers and 1 blue door one, and after time T, there is 1 red door are 99 blue door ones. Suppose also that there is the same amount of lifetime before and after T. If we don’t know what time it is, clearly P(red) = 1⁄2. That’s what P(red) means. If we know that it’s before T, then update on that info, we get P(red|before)=0.99.
Note the distinction: “before an event” is not the same thing as “in the absence of information”. In practice, often it is equivalent because we only learn info about the outcome after the event and because the number of observers stays constant. That makes it easy for people to get confused in cases where that no longer applies.
Now, suppose we ask a different question. Like in the case we were considering, the coin will be flipped and red or blue door observers will be killed; and it’s a one-shot deal. But now, there will be a time delay after the coin has been flipped but before any observers are killed. Suppose we know that we are such observers after the flip but before the killing.
During this time, what is P(red|after flip & before killing)? In this case, all 100 observers are still alive, so there are 99 blue door ones and 1 red door one, so it is 0.01. That case presents no problems for your intuition, because it doesn’t involve changes in the #’s of observers. It’s what you get with just an info update.
Then the killing occurs. Either 1 red observer is killed, or 99 blue observers are killed. Either outcome is equally likely.
In the actual resulting world, there is only one kind of observer left, so we can’t do an observer count to find the probabilities like we could in the many-worlds case (and as cupholder’s diagram would suggest). Whichever kind of observer is left, you can only be that kind, so you learn nothing about what the coin result was.
Actually, if we consider that you could have been an observer-moment either before or after the killing, finding yourself to be after it does increase your subjective probability that fewer observers were killed. However, this effect goes away if the amount of time before the killing was very short compared to the time afterwards, since you’d probably find yourself afterwards in either case; and the case we’re really interested in, the SIA, is the limit when the time before goes to 0.
I just wanted to follow up on this remark I made. There is a suble anthropic selection effect that I didn’t include in my original analysis. As we will see, the result I derived applies if the time after is long enough, as in the SIA limit.
Let the amount of time before the killing be T1, and after (until all observers die), T2. So if there were no killing, P(after) = T2/(T2+T1). It is the ratio of the total measure of observer-moments after the killing divided by the total (after + before).
If the 1 red observer is killed (heads), then P(after|heads) = 99 T2 / (99 T2 + 100 T1)
If the 99 blue observers are killed (tails), then P(after|tails) = 1 T2 / (1 T2 + 100 T1)
P(after) = P(after|heads) P(heads) + P(after|tails) P(tails)
For example, if T1 = T2, we get P(after|heads) = 0.497, P(after|tails) = 0.0099, and P(after) = 0.497 (0.5) + 0.0099 (0.5) = 0.254
So here P(tails|after) = P(after|tails) P(tails) / P(after) = 0.0099 (.5) / (0.254) = 0.0195, or about 2%. So here we can be 98% confident to be blue observers if we are after the killing. Note, it is not 99%.
Now, in the relevant-to-SIA limit T2 >> T1, we get P(after|heads) ~ 1, P(after|tails) ~1, and P(after) ~1.
In this limit P(tails|after) = P(after|tails) P(tails) / P(after) ~ P(tails) = 0.5
So the SIA is false.