Admittedly they could have been clearer, but I still think you’re misinterpreting the FDT paper. Sorry, what I meant was that smoking was correlated with an increased chance of cancer. Not that there was any causal link.
Right, sorry, I let my frustration get the best of me. I possibly am misinterpreting the FDT paper, though I am not sure where and how.
To answer your question, yes, obviously desire to smoke is correlated with the increased chance of cancer, through the common cause. If those without the lesion got utility from smoking (contrary to what the FDT paper stipulates), then the columns 3,4 and 7,8 would become relevant, definitely. We can then assign the probabilities and utilities as appropriate. What is the formulation of the problem that you have in mind?
Smoking lesion is an interesting problem in that it’s really not that well defined. If an FDT agent is making the decision, then its reference class should be other FDT agents, so all agents in the same class make the same decision, contrary to the lesion which should affect the probability. The approach that both of us take is to break the causal link from the lesion to your decision. I really didn’t express my criticism well above, because what I said also kind of applies to my post. However, the difference is that you are engaging in world counting and in world counting you should see the linkage, while my approach involves explicitly reinterpreting the problem to break the linkage. So my issue is that there seems to be some preprocessing happening before world counting and this means that your approach isn’t just a matter of world counting as you claim. In other words, it doesn’t match the label on the tin.
Smoking lesion is an interesting problem in that it’s really not that well defined. If an FDT agent is making the decision, then its reference class should be other FDT agents, so all agents in the same class make the same decision, contrary to the lesion.
Wha...? Isn’t like saying that Newcomb’s is not well defined? In the smoking lesion problem there is only one decision that gives you highest expected utility, no?
Admittedly they could have been clearer, but I still think you’re misinterpreting the FDT paper. Sorry, what I meant was that smoking was correlated with an increased chance of cancer. Not that there was any causal link.
Right, sorry, I let my frustration get the best of me. I possibly am misinterpreting the FDT paper, though I am not sure where and how.
To answer your question, yes, obviously desire to smoke is correlated with the increased chance of cancer, through the common cause. If those without the lesion got utility from smoking (contrary to what the FDT paper stipulates), then the columns 3,4 and 7,8 would become relevant, definitely. We can then assign the probabilities and utilities as appropriate. What is the formulation of the problem that you have in mind?
Smoking lesion is an interesting problem in that it’s really not that well defined. If an FDT agent is making the decision, then its reference class should be other FDT agents, so all agents in the same class make the same decision, contrary to the lesion which should affect the probability. The approach that both of us take is to break the causal link from the lesion to your decision. I really didn’t express my criticism well above, because what I said also kind of applies to my post. However, the difference is that you are engaging in world counting and in world counting you should see the linkage, while my approach involves explicitly reinterpreting the problem to break the linkage. So my issue is that there seems to be some preprocessing happening before world counting and this means that your approach isn’t just a matter of world counting as you claim. In other words, it doesn’t match the label on the tin.
Wha...? Isn’t like saying that Newcomb’s is not well defined? In the smoking lesion problem there is only one decision that gives you highest expected utility, no?