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.
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.