“You decide either “I am a one-boxer” or “I am a two-boxer,” the boxes get filled according to a rule, and then you pick deterministically according to a rule. It’s all forward reasoning; it’s just a bit weird because the action in question happens way before you are faced with the boxes.”
I realize now that this expressed as a DAG looks identical to precommitment.
Except, I also think it’s a faithful representation of the typical Newcomb scenario.
Paradox only arises if you can say “I am a two-boxer” (by picking up two boxes) while you were predicted to be a one-boxer. This can only happen if there are multiple nodes for two-boxing set to different values.
But really, this is a problem of the kind solved by superspecs in my Onward! paper. There is a constraint that the prediction of two-boxing must be the same as the actual two-boxing. Traditional causal DAGs can only express this by making them literally the same node; super-specs allow more flexibility. I am unclear how exactly it’s handled in FDT, but it has a similar analysis of the problem (“CDT breaks correlations”).
Okay, I see how that technique of breaking circularity in the model looks like precommitment.
I still don’t see what this has to do with counterfactuals though.
“You decide either “I am a one-boxer” or “I am a two-boxer,” the boxes get filled according to a rule, and then you pick deterministically according to a rule. It’s all forward reasoning; it’s just a bit weird because the action in question happens way before you are faced with the boxes.”
So you wouldn’t class this as precommitment?
I realize now that this expressed as a DAG looks identical to precommitment.
Except, I also think it’s a faithful representation of the typical Newcomb scenario.
Paradox only arises if you can say “I am a two-boxer” (by picking up two boxes) while you were predicted to be a one-boxer. This can only happen if there are multiple nodes for two-boxing set to different values.
But really, this is a problem of the kind solved by superspecs in my Onward! paper. There is a constraint that the prediction of two-boxing must be the same as the actual two-boxing. Traditional causal DAGs can only express this by making them literally the same node; super-specs allow more flexibility. I am unclear how exactly it’s handled in FDT, but it has a similar analysis of the problem (“CDT breaks correlations”).