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orthonormal comments on Decision Theories: A Less Wrong Primer - Less Wrong
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The decision theories need somewhat specific models of the world to operate correctly. In The Smoking Lesion, for example, the lesion has to somehow lead to you smoking. E.g. the lesion could make you follow CDT while absence of the lesion makes you follow EDT. It's definitely worse to have CDT if it comes at the expense of having the lesion.
The issue here is selection. If you find you opt to smoke, your prior for having lesion goes up, of course; and so you need to be more concerned about the cancer - if you can't check for the lesion you have to perhaps do chest x-rays more often, which cost money. So there's that negative consequence of deciding to smoke, except the decision theory you use needs not be concerned with this particular consequence when deciding to smoke, because the decision is itself a consequence of the lesion in the cases whereby the lesion is predictive of smoking, and only isn't a consequence of the lesion in the cases where lesion is not predictive.
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.