Decision problems have the form of “What do you do in situation X to maximize a defined utility function?”
Yes, but what you are describing is a modelling problem. “Is the drug killing them or helping them?” is not a decision problem, although “Which drug should we give them to save their lives?” is. These are two very different problems, possibly with different answers!
It is very easy to transform any causal modeling example into a decision problem.
Yes, but in the process it becomes a new problem. Although, you are right that modelling is in some respects an ‘easier’ problem than making decisions. That’s also the reason I wrote my top-level comment, saying that it is true that something you can identify in an AI is the ability to model the world.
I guess my point was that there is a trivial reduction (in the complexity theory sense of the word) here, namely that decision theory is “modeling-complete.” In other words, if we had algorithm for solving a certain class of decision problems correctly, we automatically have an algorithm for correctly handling the corresponding model (otherwise how could we get the decision problem right?)
Prediction cannot solve causal decision problems, but the reason it cannot is that it cannot solve the underlying modeling problem correctly. (If it could, there is nothing more to do, just integrate over the utility).
Yes, but what you are describing is a modelling problem. “Is the drug killing them or helping them?” is not a decision problem, although “Which drug should we give them to save their lives?” is. These are two very different problems, possibly with different answers!
Yes, but in the process it becomes a new problem. Although, you are right that modelling is in some respects an ‘easier’ problem than making decisions. That’s also the reason I wrote my top-level comment, saying that it is true that something you can identify in an AI is the ability to model the world.
I guess my point was that there is a trivial reduction (in the complexity theory sense of the word) here, namely that decision theory is “modeling-complete.” In other words, if we had algorithm for solving a certain class of decision problems correctly, we automatically have an algorithm for correctly handling the corresponding model (otherwise how could we get the decision problem right?)
Prediction cannot solve causal decision problems, but the reason it cannot is that it cannot solve the underlying modeling problem correctly. (If it could, there is nothing more to do, just integrate over the utility).