That is, the post displays exactly the confusion that Ilya mentioned.
As Paul has pointed out in the comments, the “confusion” in the post amounts to nothing more than a terminological dispute as far as I can see. It’s not a dispute over what CDT or EDT mean; it’s a dispute over what “causal network” means, and as far as I can see it’s irrelevant to the thrust of Paul’s argument.
That is stepping outside of what CDT is.
How? CDT is totally consistent with a situation in which you include yourself in your model. I can have a model (which I can’t compute explicitly) in which my actions are all caused by some inputs, but the algorithm I use to make decisions is “which action gets me the highest expected utility if I condition on that action’s do operator?”
This means I effectively ignore the causal determinants of my own decision when making the decision, but that doesn’t mean my model of the world must be ignorant of them.
EDT knows nothing of causation...
This is Paul’s whole point.
...and CDT knows nothing of including the deciding agent in the causal graph.
I’ve already responded to this above.
This is why EDT fails on the Smoking Lesion...
Paul’s point is that EDT fails on the smoking lesion problem if the EDT-ist neglects to condition on all the facts that he knows about the situation. If the EDT-ist correctly conditions on their utility function, they’ll notice that there’s actually no correlation among people with that utility function between smoking and lesions, so they’ll correctly decide to smoke if they think it’s positive expected utility.
Since Paul’s argument re: equivalence between CDT and EDT under his conditions is sound, it really has to be like this. The apparent failure of EDT has to go away once the problem is sufficiently formalized such that the EDT-ist can condition on all inputs to their decision process. However, Paul also says that CDT fails more gracefully than EDT, in the sense that if the EDT-ist neglects to condition on some relevant facts then they can fall into the trap of not smoking in the smoking lesion problem. CDT is more robust to this kind of failure.
Redefining the two terms to mean the same thing does not change the fact that the decision theories they originally named are not the same thing, any more than writing “London” and “Paris” next to Berlin on a map will make London and Paris the same city in Germany. All it does is degrade the usefulness of the map.
Paul doesn’t redefine either EDT or CDT, so I don’t know what you’re talking about here.
Instead of redefining the terms to mean whatever improved decision theory one comes up with, it would be better to come up with that improved theory and give it a new name. See, for example, TDT, UDT, etc.
I agree, but Paul hasn’t come up with an improved decision theory, so I don’t see why he should invent a new label for a new theory that doesn’t exist.
As Paul has pointed out in the comments, the “confusion” in the post amounts to nothing more than a terminological dispute as far as I can see. It’s not a dispute over what CDT or EDT mean; it’s a dispute over what “causal network” means, and as far as I can see it’s irrelevant to the thrust of Paul’s argument.
How? CDT is totally consistent with a situation in which you include yourself in your model. I can have a model (which I can’t compute explicitly) in which my actions are all caused by some inputs, but the algorithm I use to make decisions is “which action gets me the highest expected utility if I condition on that action’s do operator?”
This means I effectively ignore the causal determinants of my own decision when making the decision, but that doesn’t mean my model of the world must be ignorant of them.
This is Paul’s whole point.
I’ve already responded to this above.
Paul’s point is that EDT fails on the smoking lesion problem if the EDT-ist neglects to condition on all the facts that he knows about the situation. If the EDT-ist correctly conditions on their utility function, they’ll notice that there’s actually no correlation among people with that utility function between smoking and lesions, so they’ll correctly decide to smoke if they think it’s positive expected utility.
Since Paul’s argument re: equivalence between CDT and EDT under his conditions is sound, it really has to be like this. The apparent failure of EDT has to go away once the problem is sufficiently formalized such that the EDT-ist can condition on all inputs to their decision process. However, Paul also says that CDT fails more gracefully than EDT, in the sense that if the EDT-ist neglects to condition on some relevant facts then they can fall into the trap of not smoking in the smoking lesion problem. CDT is more robust to this kind of failure.
Paul doesn’t redefine either EDT or CDT, so I don’t know what you’re talking about here.
I agree, but Paul hasn’t come up with an improved decision theory, so I don’t see why he should invent a new label for a new theory that doesn’t exist.