But now, you seem to be complaining that a method that explicitly avoids Troll Bridge would be too restrictive?
No, I think finding such a no-learning-needed method would be great. It just means your learning-based approach wouldn’t be needed.
You seem to be arguing that being susceptible to Troll Bridge should be judged as a necessary/positive trait of a decision theory.
No. I’m saying if our “good” reasoning can’t tell us where in Troll Bridge the mistake is, then something that learns to make “good” inferences would have to fall for it.
But there are decision theories which don’t have this property, such as regular CDT, or TDT (depending on the logical-causality graph). Are you saying that those are all necessarily wrong, due to this?
A CDT is only worth as much as its method of generating counterfactuals. We generally consider regular CDT (which I interpret as “getting its counterfactuals from something-like-epsilon-exploration”) to miss important logical connections. “TDT” doesn’t have such a method. There is a (logical) causality graph that makes you do the intuitively right thing on Troll Bridge, but how to find it formally?
A strong candidate from my perspective is the inference from ¬(A∧B) to C(A|B)=0
Isn’t this just a rephrasing of your idea that the agent should act based on C(A|B) instead of B->A? I don’t see any occurance of ~(A&B) in the troll bridge argument. Now, it is equivalent to B->~A, so perhaps you think one of the propositions that occur as implications in troll bridge should be parsed this way? My modified troll bridge parses them all as counterfactual implication.
For example, I could have a lot of prior mass on “crossing gives me +10, not crossing gives me 0”. Then my +10 hypothesis would only be confirmed by experience. I could reason using counterfactuals
I’ve said why I don’t think “using counterfactuals”, absent further specification, is a solution. For the simple “crossing is +10″ belief… you’re right its succeeds, and insofar as you just wanted to show that its rationally possible to cross, I suppose it does.
This… really didn’t fit into my intuitions about learning. Consider that there is also the alternative agent who believes that crossing is −10, and sticks to that. And the reason he sticks to that isn’t that hes to afraid and VOI isn’t worth it: while its true that he never empirically confirms it, he is right, and the bridge would blow up if he were to cross it. That method works because it ignores the information in the problem description, and has us insert the relevant takeaway without any of the confusing stuff directly into its prior. Are you really willing to say: Yup, thats basically the solution to counterfactuals, just a bit of formalism left to work out?
No, I think finding such a no-learning-needed method would be great. It just means your learning-based approach wouldn’t be needed.
No. I’m saying if our “good” reasoning can’t tell us where in Troll Bridge the mistake is, then something that learns to make “good” inferences would have to fall for it.
A CDT is only worth as much as its method of generating counterfactuals. We generally consider regular CDT (which I interpret as “getting its counterfactuals from something-like-epsilon-exploration”) to miss important logical connections. “TDT” doesn’t have such a method. There is a (logical) causality graph that makes you do the intuitively right thing on Troll Bridge, but how to find it formally?
Isn’t this just a rephrasing of your idea that the agent should act based on C(A|B) instead of B->A? I don’t see any occurance of ~(A&B) in the troll bridge argument. Now, it is equivalent to B->~A, so perhaps you think one of the propositions that occur as implications in troll bridge should be parsed this way? My modified troll bridge parses them all as counterfactual implication.
I’ve said why I don’t think “using counterfactuals”, absent further specification, is a solution. For the simple “crossing is +10″ belief… you’re right its succeeds, and insofar as you just wanted to show that its rationally possible to cross, I suppose it does.
This… really didn’t fit into my intuitions about learning. Consider that there is also the alternative agent who believes that crossing is −10, and sticks to that. And the reason he sticks to that isn’t that hes to afraid and VOI isn’t worth it: while its true that he never empirically confirms it, he is right, and the bridge would blow up if he were to cross it. That method works because it ignores the information in the problem description, and has us insert the relevant takeaway without any of the confusing stuff directly into its prior. Are you really willing to say: Yup, thats basically the solution to counterfactuals, just a bit of formalism left to work out?