I wonder if that sort of transform is in general useful? Changing your logical uncertainty into an equivalent uncertainty about measure. For the calculator problem you'd say you knew exactly the answer to all multiplication problems, you just didn't know what the calculators had been programmed to calculate. So when you saw the answer 56,088 on your Mars calculator, you'd immediately know that your Venus calculator was flashing 56,088 as well (barring asteroids, etc). This information does not travel faster than light - if someone typed 123x456 on your Mars calculator while someone else typed 123x456 on your Venus calculator, you would not know that they were both flashing 56,088 - you'd have to wait until you learned that they both typed the same input. Or if you told someone to think of an input, then tell someone else who would go to Venus and type it in there, you'd still have to wait for them to get to Venus (which they can do a light speed, whynot).
How about whether P=NP, then? No matter what, once you saw 56,088 on Mars you'd know the correct answer to "what's on the Venus calculator?" But before you saw it, your estimate of the probability "56,088 is on the Venus calculator" would depend on how you transformed the problem. Maybe you knew they'd type 123x45?, so your probability was 1/10. Maybe you knew they'd type 123x???, so your probability was 1/1000. Maybe you had no idea so you had a sort of a complete ignorance prior.
I think this transform comes down to choosing appropriate reference classes for your logical uncertainty.
According to Ingredients of Timeless Decision Theory, when you set up a factored causal graph for TDT, "You treat your choice as determining the result of the logical computation, and hence all instantiations of that computation, and all instantiations of other computations dependent on that logical computation", where "the logical computation" refers to the TDT-prescribed argmax computation (call it C) that takes all your observations of the world (from which you can construct the factored causal graph) as input, and outputs an action in the present situation.
I asked Eliezer to clarify what it means for another logical computation D to be either the same as C, or "dependent on" C, for purposes of the TDT algorithm. Eliezer answered:
I replied as follows (which Eliezer suggested I post here).
If that's what TDT means by the logical dependency between Platonic computations, then TDT may have a serious flaw.
Consider the following version of the transparent-boxes scenario. The predictor has an infallible simulator D that predicts whether I one-box here [EDIT: if I see $1M]. The predictor also has a module E that computes whether the ith digit of pi is zero, for some ridiculously large value of i that the predictor randomly selects. I'll be told the value of i, but the best I can do is assign an a priori probability of .1 that the specified digit is zero.