Bayesian Networks don’t solve Newcomb’s problem, but I assume you’re aware of it. So I’m guessing your point is that if standard counterfactuals can be constructed outside of the counterfactual perspective that more general counterfactuals would most likely be the same?
Does the concept of a variable even make sense without counterfactuals? It’s not immediately obvious that it does, although I haven’t thought through this enough to assert that it doesn’t.
Update: Having spent a few minutes thinking this through, I’ve concluded that the concept of a variable over time makes sense or a variable over space, ect. makes sense without counterfactuals. However, this is a more limited notion of variable than that which we normally deal with as, if for example, the variable L representing the state of a lightswitch is “ON” at t=0, then we wouldn’t have the notion that it could have been “OFF” instead.
Update 2: Upon further thought, this seems more limited than I first thought. For example, we can’t say let a be how many apples there would be at time t if we counted them, because “if we counted them” is invoking counterfactual reasoning, unless we really did count the apples at each time period. In any case, the issue of whether or not Bayesian Networks are circular seems to be complex enough that it is deserving of further investigation.
Yeah, I’m aware of Bayesian Networks.
Two points:
Bayesian Networks don’t solve Newcomb’s problem, but I assume you’re aware of it. So I’m guessing your point is that if standard counterfactuals can be constructed outside of the counterfactual perspective that more general counterfactuals would most likely be the same?
Does the concept of a variable even make sense without counterfactuals? It’s not immediately obvious that it does, although I haven’t thought through this enough to assert that it doesn’t.
Update: Having spent a few minutes thinking this through, I’ve concluded that the concept of a variable over time makes sense or a variable over space, ect. makes sense without counterfactuals. However, this is a more limited notion of variable than that which we normally deal with as, if for example, the variable L representing the state of a lightswitch is “ON” at t=0, then we wouldn’t have the notion that it could have been “OFF” instead.
Update 2: Upon further thought, this seems more limited than I first thought. For example, we can’t say let a be how many apples there would be at time t if we counted them, because “if we counted them” is invoking counterfactual reasoning, unless we really did count the apples at each time period. In any case, the issue of whether or not Bayesian Networks are circular seems to be complex enough that it is deserving of further investigation.