What about the scenario, where Omega uses the strategy: Simulate telling the human that they got the answer wrong. Define the resulting answer as wrong, and the other as right.
In that case it should be modeled like this:
def P(color):
wrong_color = Omega_Predict(S, "you're wrong")
if S("you're wrong") == wrong_color:
outcome = "die"
else:
outcome = "live"
Thanks. Is there an easier way to get a tab into the comment input box than copy paste from an outside editor?
Not that I'm aware of.
Hm, I think the difference in our model programs indicates something that I don't understand about UDT, like a wrong assumption that justified an optimization. But it seems they both produce the same result for P(S("you're wrong")), which is outcome="die" for all S.
Do you agree that this problem is, and should remain, unsolvable? (I understand "should remain unsolvable" to mean that any supposed solution must represent some sort of confusion about the problem.)
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.