it’s basically trying to think about the statistics of environments rather than their internals
That’s not really true because the structure of infra-environments reflects the structure of those Newcombian scenarios. This means that the sample complexity of learning them will likely scale with their intrinsic complexity (e.g. some analogue of RVO dimension). This is different from treating the environment as a black-box and converging to optimal behavior by pure trial and error, which would yield much worse sample complexity.
I agree that infra-bayesianism isn’t just thinking about sampling properties, and maybe ‘statistics’ is a bad word for that. But the failure on transparent Newcomb without kind of hacky changes to me suggests a focus on “what actions look good thru-out the probability distribution” rather than on “what logically-causes this program to succeed”.
There is some truth in that, in the sense that, your beliefs must take a form that is learnable rather than just a god-given system of logical relationships.
There’s actually an upcoming post going into more detail on what the deal is with pseudocausal and acausal belief functions, among several other things, I can send you a draft if you want. “Belief Functions and Decision Theory” is a post that hasn’t held up nearly as well to time as “Basic Inframeasure Theory”.
That’s not really true because the structure of infra-environments reflects the structure of those Newcombian scenarios. This means that the sample complexity of learning them will likely scale with their intrinsic complexity (e.g. some analogue of RVO dimension). This is different from treating the environment as a black-box and converging to optimal behavior by pure trial and error, which would yield much worse sample complexity.
I agree that infra-bayesianism isn’t just thinking about sampling properties, and maybe ‘statistics’ is a bad word for that. But the failure on transparent Newcomb without kind of hacky changes to me suggests a focus on “what actions look good thru-out the probability distribution” rather than on “what logically-causes this program to succeed”.
There is some truth in that, in the sense that, your beliefs must take a form that is learnable rather than just a god-given system of logical relationships.
There’s actually an upcoming post going into more detail on what the deal is with pseudocausal and acausal belief functions, among several other things, I can send you a draft if you want. “Belief Functions and Decision Theory” is a post that hasn’t held up nearly as well to time as “Basic Inframeasure Theory”.
Thanks for the offer, but I don’t think I have room for that right now.