Today's post, Pascal's Mugging: Tiny Probabilities of Vast Utilities was originally published on 19 October 2007. A summary (taken from the LW wiki):
An Artificial Intelligence coded using Solmonoff Induction would be vulnerable to Pascal's Mugging. How should we, or an AI, handle situations in which it is very unlikely that a proposition is true, but if the proposition is true, it has more moral weight than anything else we can imagine?
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There's probably something that I'm missing, so sorry if this solution has already been posted in the original thread. I don't really have the "oomph" the read them all... Anyway, hasn't this class of problems already been solved in chapter 5 of Jaynes' book?
If the AI has some tiny probability that the data he has received originated through some kind of deception, I think it's only sensible that the hypothesis that the mugger is lying steals all the probability mass in the posterior distribution, at least linearly with the number of people he claims he can affect (but I would say exponentially).
The expected utility shouldn't really be calculated on the posterior of the hypothesis "mugger possess magical power" but on the posterior of "mugger can affect the Matrix + mugger is lying".
ETA: This allows you to control the posterior probability of the hypothesis independently from the claim of the mugger, thereby shielding the AI from acting on enormous disutility depending only from slightly less enormous improbability.
Will someone who downvoted explain what's wrong with this solution? Feedback is much appreciated, thanks :)