If that were the case, the camera might show the person being killed; indeed, that is 50% likely.
Yep. But Stuart_Armstrong’s description is asking us to condition on the camera showing ‘you’ surviving.
Pre-selection is not the same as our case of post-selection. My calculation shows the difference it makes.
It looks to me like we agree that pre-selecting someone who happens to survive gives a different result (99%) to post-selecting someone from the pool of survivors (50%) - we just disagree on which case SA had in mind. Really, I guess it doesn’t matter much if we agree on what the probabilities are for the pre-selection v. the post-selection case.
Now, if the fraction of observers of each type that are killed is the same, the difference between the two selections cancels out. That is what tends to happen in the many-shot case, and we can then replace probabilities with relative frequencies.
I am unsure how to interpret this...
One-shot probability is not relative frequency.
...but I’m fairly sure I disagree with this. If we do Bernoulli trials with success probability p (like coin flips, which are equivalent to Bernoulli trials with p = 0.5), I believe the strong law of large numbers implies that the relative frequency converges almost surely to p as the number of Bernoulli trials becomes arbitrarily large. As p represents the ‘one-shot probability,’ this justifies interpreting the relative frequency in the infinite limit as the ‘one-shot probability.’
But Stuart_Armstrong’s description is asking us to condition on the camera showing ‘you’ surviving.
That condition imposes post-selection.
I guess it doesn’t matter much if we agree on what the probabilities are for the pre-selection v. the post-selection case.
Wrong—it matters a lot because you are using the wrong probabilities for the survivor (in practice this affects things like belief in the Doomsday argument).
I believe the strong law of large numbers implies that the relative frequency converges almost surely to p as the number of Bernoulli trials becomes arbitrarily large. As p represents the ‘one-shot probability,’ this justifies interpreting the relative frequency in the infinite limit as the ‘one-shot probability.’
You have things backwards. The “relative frequency in the infinite limit” can be defined that way (sort of, as the infinite limit is not actually doable) and is then equal to the pre-defined probability p for each shot if they are independent trials. You can’t go the other way; we don’t have any infinite sequences to examine, so we can’t get p from them, we have to start out with it. It’s true that if we have a large but finite sequence, we can guess that p is “probably” close to our ratio of finite outcomes, but that’s just Bayesian updating given our prior distribution on likely values of p. Also, in the 1-shot case at hand, it is crucial that there is only the 1 shot.
But not post-selection of the kind that influences the probability (at least, according to my own calculations).
Wrong—it matters a lot because you are using the wrong probabilities for the survivor (in practice this affects things like belief in the Doomsday argument).
Which of my estimates is incorrect—the 50% estimate for what I call ‘pre-selecting someone who happens to survive,’ the 99% estimate for what I call ‘post-selecting someone from the pool of survivors,’ or both?
You can’t go the other way; we don’t have any infinite sequences to examine, so we can’t get p from them, we have to start out with it.
Correct. p, strictly, isn’t defined by the relative frequency—the strong law of large numbers simply justifies interpreting it as a relative frequency. That’s a philosophical solution, though. It doesn’t help for practical cases like the one you mention next...
It’s true that if we have a large but finite sequence, we can guess that p is “probably” close to our ratio of finite outcomes, but that’s just Bayesian updating given our prior distribution on likely values of p.
...for practical scenarios like this we can instead use the central limit theorem to say that p’s likely to be close to the relative frequency. I’d expect it to give the same results as Bayesian updating—it’s just that the rationale differs.
Also, in the 1-shot case at hand, it is crucial that there is only the 1 shot.
It certainly is in the sense that if ‘you’ die after 1 shot, ‘you’ might not live to take another!
Yep. But Stuart_Armstrong’s description is asking us to condition on the camera showing ‘you’ surviving.
It looks to me like we agree that pre-selecting someone who happens to survive gives a different result (99%) to post-selecting someone from the pool of survivors (50%) - we just disagree on which case SA had in mind. Really, I guess it doesn’t matter much if we agree on what the probabilities are for the pre-selection v. the post-selection case.
I am unsure how to interpret this...
...but I’m fairly sure I disagree with this. If we do Bernoulli trials with success probability p (like coin flips, which are equivalent to Bernoulli trials with p = 0.5), I believe the strong law of large numbers implies that the relative frequency converges almost surely to p as the number of Bernoulli trials becomes arbitrarily large. As p represents the ‘one-shot probability,’ this justifies interpreting the relative frequency in the infinite limit as the ‘one-shot probability.’
That condition imposes post-selection.
Wrong—it matters a lot because you are using the wrong probabilities for the survivor (in practice this affects things like belief in the Doomsday argument).
You have things backwards. The “relative frequency in the infinite limit” can be defined that way (sort of, as the infinite limit is not actually doable) and is then equal to the pre-defined probability p for each shot if they are independent trials. You can’t go the other way; we don’t have any infinite sequences to examine, so we can’t get p from them, we have to start out with it. It’s true that if we have a large but finite sequence, we can guess that p is “probably” close to our ratio of finite outcomes, but that’s just Bayesian updating given our prior distribution on likely values of p. Also, in the 1-shot case at hand, it is crucial that there is only the 1 shot.
But not post-selection of the kind that influences the probability (at least, according to my own calculations).
Which of my estimates is incorrect—the 50% estimate for what I call ‘pre-selecting someone who happens to survive,’ the 99% estimate for what I call ‘post-selecting someone from the pool of survivors,’ or both?
Correct. p, strictly, isn’t defined by the relative frequency—the strong law of large numbers simply justifies interpreting it as a relative frequency. That’s a philosophical solution, though. It doesn’t help for practical cases like the one you mention next...
...for practical scenarios like this we can instead use the central limit theorem to say that p’s likely to be close to the relative frequency. I’d expect it to give the same results as Bayesian updating—it’s just that the rationale differs.
It certainly is in the sense that if ‘you’ die after 1 shot, ‘you’ might not live to take another!