The question “which decision theory is superior?” has this flavor of “can my dad beat up your dad?”
CDT is what you use when you want to make decisions from observational data or RCTs (in medicine, and so on).
TDT is what you use when “for some reason” your decisions are linked to what counterfactual versions/copies of yourself decided. Standard CDT doesn’t deal with this problem, because it lacks the language/notation to talk about these issues. I argue this is similar to how EDT doesn’t handle confounding properly because it lacks the language to describe what confounding even means. (Although I know a few people who prefer a decision algorithm that is in all respects isomophic to CDT, but which they prefer to call EDT for I guess reasons having to do with the formal epistemology they adopted. To me, this is a powerful argument for not adopting a formal epistemology too quickly :) )
I think it’s more fruitful to think about the zoo of decision theories out there in terms of what they handle and what they break on, rather than in terms of anointing some of them with the label “rational” and others with the label “irrational.” These labels carry no information. There is probably no total ordering from “best to worst” (for example people claim EDT correctly one boxes on Newcomb, whereas CDT does not. This does not prevent EDT from being generally terrible on the kinds of problems CDT handles with ease due to a worked out theory of causal inference).
I don’t like the notion of using different decision theories depending on the situation, because the very idea of a decision theory is that it is consistent and comprehensive. Now if TDT were formulated as a plugin that seamlessly integrated into CDT in such a way that the resulting decision theory could be applied to any and all problems and would always yield optimal results, then that would be reason for me to learn about TDT. However, from what I gathered this doesn’t seem to be the case?
TDT performs exactly as well as CDT on the class of problems CDT can deal with, because for those problems it essentially is CDT. So in practice you just use normal CDT algorithms except for when counterfactual copies of yourself are involved. Which is what TDT does.
I argue that there’s an mapping in the opposite direction: if you add extra nodes to any problem that looks like a problem where TDT and CDT disagree, and adjust which node is the decision node, then you can make CDT and TDT agree (and CDT give the “TDT solution”). This is obvious in the case of Newcomb’s Problem, for example.
I guess it’s true that CDT needed lots of ideas to work. TDT has one idea: “link counterfactual decisions together.” So it is not an unreasonable view that TDT is an addendum to CDT, and not vice versa, since CDT is intellectually richer.
The question “which decision theory is superior?” has this flavor of “can my dad beat up your dad?”
CDT is what you use when you want to make decisions from observational data or RCTs (in medicine, and so on).
TDT is what you use when “for some reason” your decisions are linked to what counterfactual versions/copies of yourself decided. Standard CDT doesn’t deal with this problem, because it lacks the language/notation to talk about these issues. I argue this is similar to how EDT doesn’t handle confounding properly because it lacks the language to describe what confounding even means. (Although I know a few people who prefer a decision algorithm that is in all respects isomophic to CDT, but which they prefer to call EDT for I guess reasons having to do with the formal epistemology they adopted. To me, this is a powerful argument for not adopting a formal epistemology too quickly :) )
I think it’s more fruitful to think about the zoo of decision theories out there in terms of what they handle and what they break on, rather than in terms of anointing some of them with the label “rational” and others with the label “irrational.” These labels carry no information. There is probably no total ordering from “best to worst” (for example people claim EDT correctly one boxes on Newcomb, whereas CDT does not. This does not prevent EDT from being generally terrible on the kinds of problems CDT handles with ease due to a worked out theory of causal inference).
I don’t like the notion of using different decision theories depending on the situation, because the very idea of a decision theory is that it is consistent and comprehensive. Now if TDT were formulated as a plugin that seamlessly integrated into CDT in such a way that the resulting decision theory could be applied to any and all problems and would always yield optimal results, then that would be reason for me to learn about TDT. However, from what I gathered this doesn’t seem to be the case?
TDT performs exactly as well as CDT on the class of problems CDT can deal with, because for those problems it essentially is CDT. So in practice you just use normal CDT algorithms except for when counterfactual copies of yourself are involved. Which is what TDT does.
I argue that there’s an mapping in the opposite direction: if you add extra nodes to any problem that looks like a problem where TDT and CDT disagree, and adjust which node is the decision node, then you can make CDT and TDT agree (and CDT give the “TDT solution”). This is obvious in the case of Newcomb’s Problem, for example.
I guess it’s true that CDT needed lots of ideas to work. TDT has one idea: “link counterfactual decisions together.” So it is not an unreasonable view that TDT is an addendum to CDT, and not vice versa, since CDT is intellectually richer.