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 assumption), this probability is set to zero.
Decision theory is not about determining what sorts of problems are plausible, it’s about getting from a fully-specified problem description to an optimal answer. Your diagonalization argument requires that the problem not be fully specified in the first place.
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 assumption), this probability is set to zero.
Decision theory is not about determining what sorts of problems are plausible, it’s about getting from a fully-specified problem description to an optimal answer. Your diagonalization argument requires that the problem not be fully specified in the first place.