Right, so your perspective is that due to the multiple embeddings of yourself being in the heads scenario, it is the 1001:1 option. That line of reasoning is kind of what I thought as well, but it was against the 1:1 odds as would be suggested by my intuition. I guess this is the same as the halfer vs thirder debate, where 1:1 is the halfer position and the 1001:1 is the thirder position.
I suppose the lollipops are indeed an unnecessary addition, so the final question can really be reframed as “what is the probability that you will see heads?”
You don’t need a coin flip, I’m fine with lollipops randomly given to 1000 out of 1001 participants. This is not about “being in the head”, this is an experimental result, assume you run a large number of experiments like that. The stipulation is that it is impossible to tell from the inside if it is a simulation or the original, so one has to use the uniform prior.
Not an expert, either, hah. But yeah, what I meant is that the distribution is uniform over all instances, whether originals or copies, since there is no way to distinguish internally between the twem.
Right, so your perspective is that due to the multiple embeddings of yourself being in the heads scenario, it is the 1001:1 option. That line of reasoning is kind of what I thought as well, but it was against the 1:1 odds as would be suggested by my intuition. I guess this is the same as the halfer vs thirder debate, where 1:1 is the halfer position and the 1001:1 is the thirder position.
I suppose the lollipops are indeed an unnecessary addition, so the final question can really be reframed as “what is the probability that you will see heads?”
You don’t need a coin flip, I’m fine with lollipops randomly given to 1000 out of 1001 participants. This is not about “being in the head”, this is an experimental result, assume you run a large number of experiments like that. The stipulation is that it is impossible to tell from the inside if it is a simulation or the original, so one has to use the uniform prior.
Ah I see. Sorry for not being too familiar with the lingo but does uniform prior just mean equal probability assigned to each possible embedding?
Not an expert, either, hah. But yeah, what I meant is that the distribution is uniform over all instances, whether originals or copies, since there is no way to distinguish internally between the twem.