(CDT smokes extra hard, EDT stops smoking. Next statistic collection correlation vanishes, EDT starts to smoke too.)
Why did the correlation exist in the first place?
EDT agents should smoke because they all know that they are EDT agents, and conditional upon being an EDT agent in this scenario, smoking and cancer are independent. P(cancer & smoking | EDT & scenario) = P(cancer | EDT & scenario) P(smoking | EDT & scenario) because P(smoking | EDT & scenario) = 0 or 1 and in either case the equality holds.
Selection effects can’t change anything for EDT agents here.
I meant, each agent has utility function, with smoking somewhere in there and decision theory. You can’t effectively compare the utility functions to find out if you prefer smoking to more things than other agents, as it takes too much resources.
If you have lesion, then smoking is a bit higher in your U, and you are likely to get cancer.
What I mean is that for EDT agents in the population that are in this scenario, there will be zero correlation, and the agents should be able to deduce that and therefore smoke.
Whether there is a positive correlation among people who aren’t in this type of scenario or aren’t EDT agents should be irrelevant to any EDT agents.
More precisely: The factor P(outcome | action, known information) in the EDT formula is not the same thing as P(outcome | action, some prior or other) and yet the correlation stated in the scenario setup is of the latter form. The calculation that supposedly leads an EDT agent to refrain from smoking is incorrect due to this. The agent would have to ignore known information to arrive at the supposed conclusion.
Why did the correlation exist in the first place?
EDT agents should smoke because they all know that they are EDT agents, and conditional upon being an EDT agent in this scenario, smoking and cancer are independent. P(cancer & smoking | EDT & scenario) = P(cancer | EDT & scenario) P(smoking | EDT & scenario) because P(smoking | EDT & scenario) = 0 or 1 and in either case the equality holds.
Selection effects can’t change anything for EDT agents here.
I meant, each agent has utility function, with smoking somewhere in there and decision theory. You can’t effectively compare the utility functions to find out if you prefer smoking to more things than other agents, as it takes too much resources.
If you have lesion, then smoking is a bit higher in your U, and you are likely to get cancer.
What I mean is that for EDT agents in the population that are in this scenario, there will be zero correlation, and the agents should be able to deduce that and therefore smoke.
Whether there is a positive correlation among people who aren’t in this type of scenario or aren’t EDT agents should be irrelevant to any EDT agents.
More precisely: The factor P(outcome | action, known information) in the EDT formula is not the same thing as P(outcome | action, some prior or other) and yet the correlation stated in the scenario setup is of the latter form. The calculation that supposedly leads an EDT agent to refrain from smoking is incorrect due to this. The agent would have to ignore known information to arrive at the supposed conclusion.
Hmmm. You are probably right.