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eli_sennesh comments on Causal decision theory is unsatisfactory - Less Wrong
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I think it might be helpful to be more precise about problem classes, e.g. what does "Newcomb-like" mean?.
That is, the kinds of things that I can see informally arising in settings humans deal with (lots of agents running around) also contain things like blackmail problems, which UDT does not handle. So it is not really fair to say this class is "Newcomb-like," if by that class we mean "problems UDT handles properly."
Thanks, I think you're right.
(For reference, I'll be defining "Newcomblike" roughly as "other agents have knowledge of your decision algorithm". You're correct that this includes problems where UDT performs poorly, and that UDT is by no means the One Final Answer. In fact, I'm not planning to discuss UDT at all in this sequence; my goal is to motivate the idea that we don't know enough about decision theory yet to be comfortable constructing a system capable of undergoing an intelligence explosion. The fact that Newcomblike problems are fairly common in the real world is one facet of that motivation.)
What problems does UDT fail on?
Why would a self-improving agent not improve its own decision-theory to reach an optimum without human intervention, given a "comfortable" utility function in the first place?
A self-improving agent does improve its own decision theory, but it uses its current decision theory to predict which self-modifications would be improvements, and broken decision theories can be wrong about that. Not all starting points converge to the same answer.
Oh. Oh dear. DERP. Of course: the decision theory of sound self-improvement is a special case of the decision theory for dealing with other agents.