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.
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.