I’d argue that this argument doesn’t work because the places where CDT, EDT or some new system diverge from each other are outside of the set of situations in which decision theory is a useful way to think about the problems. I mean it is always possible to simply take the outside perspective and merely describe facts of the form: under such and such situations algorithm A performs better than B.
What makes decision theory useful is that it implicitly accommodates the very common (for humans) situation in which the world doesn’t depend in noticeable ways (ie the causal relationship is so lacking in simple patterns it looks random to our eyes) on the details of the algorithm we’ve adopted to make future choices. The second we get into situations like Newcomb problems where variants of decision theory might say something else there is simply no reason to model the scenario in terms of decisions at all anymore.
Once you have meaningful feedback between the algorithm adopted to make choices and other agent’s choices it’s time to do the kind of analysis we do for fixed points in CS/math not apply decision theory given that the fundamental abstraction of a decision doesn’t really make sense anymore when we get feedback based on our choice algorithm.
Moreover, it’s plausible that decision theory is only useful from an internal perspective and not the perspective of someone designing the an algorithm to make choices. Indeed, one of the reasons decision theory is useful is the kind of limited access we have to our own internal behavioral algorithms. If We are considering a computer program it seems strictly preferable to just reason about decision algorithms directly so we need not stretch the agent idealization too far.
I’d argue that this argument doesn’t work because the places where CDT, EDT or some new system diverge from each other are outside of the set of situations in which decision theory is a useful way to think about the problems. I mean it is always possible to simply take the outside perspective and merely describe facts of the form: under such and such situations algorithm A performs better than B.
What makes decision theory useful is that it implicitly accommodates the very common (for humans) situation in which the world doesn’t depend in noticeable ways (ie the causal relationship is so lacking in simple patterns it looks random to our eyes) on the details of the algorithm we’ve adopted to make future choices. The second we get into situations like Newcomb problems where variants of decision theory might say something else there is simply no reason to model the scenario in terms of decisions at all anymore.
Once you have meaningful feedback between the algorithm adopted to make choices and other agent’s choices it’s time to do the kind of analysis we do for fixed points in CS/math not apply decision theory given that the fundamental abstraction of a decision doesn’t really make sense anymore when we get feedback based on our choice algorithm.
Moreover, it’s plausible that decision theory is only useful from an internal perspective and not the perspective of someone designing the an algorithm to make choices. Indeed, one of the reasons decision theory is useful is the kind of limited access we have to our own internal behavioral algorithms. If We are considering a computer program it seems strictly preferable to just reason about decision algorithms directly so we need not stretch the agent idealization too far.