I don't see how this is useful. Let's take a concrete example, let's have decision problem A, Omega offers you the choice of $1,000,000, or being slapped in the face with a wet fish. Which would you like your decision theory to choose?
Now, No-mega can simulate you, say, 10 minutes before you find out who he is, and give you 3^^^3 utilons iff you chose the fish-slapping. So your algorithm has to include some sort of prior on the existence of "fish-slapping"-No-megas.
My algorithm "always get slapped in the face with a wet fish where that's an option", does better than any sensible algorithm on this particular problem, and I don't see how this problem is noticeably less realistic than any others.
In other words, I guess I might be willing to believe that you can get around diagonalisation by posing some stringent limits on what sort of all-powerful Omegas you allow (can anyone point me to a proof of that?) but I don't see how it's interesting.
Now, No-mega can simulate you, say, 10 minutes before you find out who he is, and give you 3^^^3 utilons iff you chose the fish-slapping. So your algorithm has to include some sort of prior on the existence of "fish-slapping" No-megas.
Actually, no, the probability of fish-slapping No-megas is part of the input given to the decision theory, not part of the decision theory itself. And since every decision theory problem statement comes with an implied claim that it contains all relevant information (a completely unavoidable simplifying assumptio...
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