I think the assumption is that your decision theory is fixed, and the lesion has an influence on your utility function via how much you want to smoke (though in a noisy way, so you can’t use it to conclude with certainty whether you have the lesion or not).
What would EDT do if it has evidence (possibly obtained from theory about the physics, derived from empirical evidence in support of causality) that it is (or must be) the desire to smoke that is correlated with the cancer? Shouldn’t it ‘cancel out’ the impact of correlation of the decision with the cancer, on the decision?
It seems to me that good decision theories can disagree on the decisions made with imperfect data and incomplete model. The evidence based decision theory should be able to process the evidence for the observed phenomenon of ‘causality’, and process it all the way to the notion that decision won’t affect cancer.
At same time if an agent can not observe evidence for causality and reason about it correctly, that agent is seriously crippled in many ways—would it even be able to figure out e.g. newtonian physics from observation, if it can’t figure out causality?
The CDT looks like a hack where you hard-code causality into an agent, which you (mankind) figured out from observation and evidence (and it took a while to figure it out and figure out how to apply it). edit: This seem to go for some of the advanced decision theories too. You shouldn’t be working so hard inventing the world-specific stuff to hard-code into an agent. The agent should figure it out from properties of the real world and perhaps considerations for hypothetical examples.
I think the assumption is that your decision theory is fixed, and the lesion has an influence on your utility function via how much you want to smoke (though in a noisy way, so you can’t use it to conclude with certainty whether you have the lesion or not).
That also works.
What would EDT do if it has evidence (possibly obtained from theory about the physics, derived from empirical evidence in support of causality) that it is (or must be) the desire to smoke that is correlated with the cancer? Shouldn’t it ‘cancel out’ the impact of correlation of the decision with the cancer, on the decision?
It seems to me that good decision theories can disagree on the decisions made with imperfect data and incomplete model. The evidence based decision theory should be able to process the evidence for the observed phenomenon of ‘causality’, and process it all the way to the notion that decision won’t affect cancer.
At same time if an agent can not observe evidence for causality and reason about it correctly, that agent is seriously crippled in many ways—would it even be able to figure out e.g. newtonian physics from observation, if it can’t figure out causality?
The CDT looks like a hack where you hard-code causality into an agent, which you (mankind) figured out from observation and evidence (and it took a while to figure it out and figure out how to apply it). edit: This seem to go for some of the advanced decision theories too. You shouldn’t be working so hard inventing the world-specific stuff to hard-code into an agent. The agent should figure it out from properties of the real world and perhaps considerations for hypothetical examples.