= 27d567bc2d48d9f40e9ccc20ea370b15)
Vladimir_Nesov comments on Formalizing Newcomb's - Less Wrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (111)
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.