I think how confident you can be in the math of your own confidence and the laws of probability, according to your priors of the laws of probability, is pretty much as far down as you can go.
Then this confidence prior in the laws of probability lets you apply it to your memory and do conjunction/disjunction math on the many instances of something turning out “correct”, so you get a reliability of memory given a certain amount of memory datapoints and a certain reliability of probabilities.
Then you kind of keep doing more bayes, building layer after layer relying on the reliability of your own memory and the applicability of this whole process altogether.
That seems like an acceptable upper bound to me.
And yes, that’s probably rather equivalent to saying “What are your priors for Bayes, Occam, laws of probability and your memory all correct and functional? There, that’s your upper bound.”
Would you care to offer any estimates of /your/ priors for Bayes, etc? Or what your own inputs or outputs for the overall process you describe might be?
I haven’t calculated the longer version yet, but my general impression so far is that I’m around the ~60 deciban mark as my general upper bound for any single piece of knowledge.
I’m not sure I’m even capable of calculating the longer version, since I suspect there’s a lot more information problems and more advanced math required for calculating things like the probability distributions of causal independence over individually-uncertain memories forged from unreliable causes in the (presumably very complex) causal graph representing all of this and so on.
I think how confident you can be in the math of your own confidence and the laws of probability, according to your priors of the laws of probability, is pretty much as far down as you can go.
Then this confidence prior in the laws of probability lets you apply it to your memory and do conjunction/disjunction math on the many instances of something turning out “correct”, so you get a reliability of memory given a certain amount of memory datapoints and a certain reliability of probabilities.
Then you kind of keep doing more bayes, building layer after layer relying on the reliability of your own memory and the applicability of this whole process altogether.
That seems like an acceptable upper bound to me.
And yes, that’s probably rather equivalent to saying “What are your priors for Bayes, Occam, laws of probability and your memory all correct and functional? There, that’s your upper bound.”
Would you care to offer any estimates of /your/ priors for Bayes, etc? Or what your own inputs or outputs for the overall process you describe might be?
I haven’t calculated the longer version yet, but my general impression so far is that I’m around the ~60 deciban mark as my general upper bound for any single piece of knowledge.
I’m not sure I’m even capable of calculating the longer version, since I suspect there’s a lot more information problems and more advanced math required for calculating things like the probability distributions of causal independence over individually-uncertain memories forged from unreliable causes in the (presumably very complex) causal graph representing all of this and so on.