How, what you are describing, is different from just a coin toss, where you win $1 if its Heads and loose 3$ if it’s Tails. Obviously negative EV. But then, when the coin is tossed you see that it happened to be Heads and you now wish that you’ve taken the bet, given your new knowledge of the outcome of the random event?
It is not dramatically different but there are 2 random variables: the first is a coin toss, and the 2nd random variable has p(green | heads) = 0.9, p(red | heads) = 0.1, p(green | tails) = 0.1, p(red | tails) = 0.9. So you need to multiply that out to get the conditional probabilities/payouts.
But my claim is that the seemingly complex bit where 18 vs 2 copies of you are created conditional on an event is identical to regular conditional probability. In other words my claim (which I thought was similar to your point in the post) is that regular probability is equivalent to measure over identical observers in the multiverse.
I don’t see how it’s equivalent.
How, what you are describing, is different from just a coin toss, where you win $1 if its Heads and loose 3$ if it’s Tails. Obviously negative EV. But then, when the coin is tossed you see that it happened to be Heads and you now wish that you’ve taken the bet, given your new knowledge of the outcome of the random event?
It is not dramatically different but there are 2 random variables: the first is a coin toss, and the 2nd random variable has p(green | heads) = 0.9, p(red | heads) = 0.1, p(green | tails) = 0.1, p(red | tails) = 0.9. So you need to multiply that out to get the conditional probabilities/payouts.
But my claim is that the seemingly complex bit where 18 vs 2 copies of you are created conditional on an event is identical to regular conditional probability. In other words my claim (which I thought was similar to your point in the post) is that regular probability is equivalent to measure over identical observers in the multiverse.