The parents that you referred to are now at 17 and 22 points, which seems a bit mad to me. Spotting the errors in P&R’s reasoning isn’t really the problem. The problem is to come up with a general decision algorithm that both works (in the sense of making the right decisions) and (if possible) makes epistemic sense.
So far, we know that UDT works but it doesn’t compute or make use of “probability of being at X” so epistemically it doesn’t seem very satisfying. Does TDT give the right answer when applied to this problem? If so, how? (It’s not specified formally enough that I can just apply it mechanically.) Does this problem suggest any improvements or alternative algorithms?
Awesome. I’m steadily upgrading my expected utilities of handing decision-theory problems to Less Wrong.
Again, that seems to imply that the problem is solved, and I don’t quite see how the parent comments have done that.
The problem is to come up with a general decision algorithm that both works (in the sense of making the right decisions) and (if possible) makes epistemic sense.
I presented a solution in a comment here which I think satisfies these: It gives the right answer and consistently handles the case of “partial knowledge” about one’s intersection, and correctly characterizes your epistemic condition in the absent-minded case.
I don’t see why the problem is not solved. The probability of being at X depends directly on how I am deciding whether to turn. So I cannot possibly use that probability to decide whether to turn; I need to decide on how I will turn first, and then I can calculate the probability of being at X. This results in the original solution.
This also shows that Eliezer was mistaken in claiming that any algorithm involving randomness can be improved by making it deterministic.
The parents that you referred to are now at 17 and 22 points, which seems a bit mad to me. Spotting the errors in P&R’s reasoning isn’t really the problem. The problem is to come up with a general decision algorithm that both works (in the sense of making the right decisions) and (if possible) makes epistemic sense.
So far, we know that UDT works but it doesn’t compute or make use of “probability of being at X” so epistemically it doesn’t seem very satisfying. Does TDT give the right answer when applied to this problem? If so, how? (It’s not specified formally enough that I can just apply it mechanically.) Does this problem suggest any improvements or alternative algorithms?
Again, that seems to imply that the problem is solved, and I don’t quite see how the parent comments have done that.
I presented a solution in a comment here which I think satisfies these: It gives the right answer and consistently handles the case of “partial knowledge” about one’s intersection, and correctly characterizes your epistemic condition in the absent-minded case.
I don’t see why the problem is not solved. The probability of being at X depends directly on how I am deciding whether to turn. So I cannot possibly use that probability to decide whether to turn; I need to decide on how I will turn first, and then I can calculate the probability of being at X. This results in the original solution.
This also shows that Eliezer was mistaken in claiming that any algorithm involving randomness can be improved by making it deterministic.