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rwallace comments on Ingredients of Timeless Decision Theory - Less Wrong
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(I don't remember where I first read that variant, but Martin Gardner sounds likely.) Yes, I agree with your analysis of it -- but that doesn't contradict the assertion that you can solve these problems by extending your utility function across parallel versions of you who received slightly different sensory data. I will conjecture that this turns out to be the only elegant solution.
Sorry, that doesn't make any sense. It's a probability distribution that's the issue, not a utility function. UDT tosses out the probability distribution entirely. TDT still uses it and therefore fails on Counterfactual Mugging.
It's precisely the assertion that all such problems have to be solved at the probability distribution level that I'm disputing. I'll go so far as to make a testable prediction: it will be eventually acknowledged that the notion of a purely selfish agent is a good approximation that nonetheless cannot handle such extreme cases. If you can come up with a theory that handles them all without touching the utility function, I will be interested in seeing it!
None of the decision theories in question assume a purely selfish agent.
No, but most of the example problems do.