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Vladimir_Nesov comments on Formalizing Newcomb's - Less Wrong
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Because the discussion here didn't seem interesting relative to the discussions I've already read in philosophy; see the edited volume Paradoxes of Rationality and Cooperation or start googling on "evidential decision theory" and "causal decision theory".
I've never launched into a full-fledged discussion of Newcomb's Problem because that would quickly degenerate into a full-blown sequence in which I presented the general solution (tentatively labeled "timeless decision theory").
From my perspective this is a big, difficult, complicated, long-standing, controversial, overdetermined, elegant, solved problem, like the interpretation of quantum mechanics. Though in both cases there's a couple of leftover problems, the Born statistics for QM and some matters of mathematical representation for Newcomb, which may or may not represent a gateway to other mysteries after the original main problem has been solved.
I'll repeat yet again my standing offer to do my PhD thesis on Newcomblike problems if anyone will let me come in and just do a PhD thesis rather than demanding 8 years of class attendance.
I'd appreciate a short extended abstract of what you've collected (on related technical topics), without explanations, just outlining what it's about and linking to the keywords. I'm currently going through the stage of formalizing the earlier intuitions, and it looks like a huge synthesis, lots of stuff yet to learn, so some focus may be useful.
Sorry, too huge. There's a nice dissertation on the subject here: http://kops.ub.uni-konstanz.de/volltexte/2000/524/pdf/ledwig.pdf
I think I grasp this problem well enough, I'm not sure it's useful to plough through the existing philosophy at this point (am I wrong, is there something technically useful in e.g. that thesis?).
The examples of problems I was trying to figure out these last weeks is e.g. representation of preference order (lattices vs. probabilities vs. graphical models vs. other mathematical structures), relation and conversions between different representations of the state space (variables/predicates/etc.), representation of one agent by another, "agents" as efficient abstractions of regularities in the preference order, compound preferences and more global optimization resulting from cooperation of multiple agents, including the counterfactual agents and agents acting at different local areas in time/space/representation of state space, etc.
There's actually quite a lot of this in James Joyce's The foundations of causal decision theory, at what appears to me to be a gratuitiously high math level.