I agree with the first point, and I don’t have solid solutions to this. There’s also the fact that some games are easier to optimize than others (name a number game I described at the end vs. chess), and this complexity is impossible to capture while staying computation-agnostic. Maybe one can use the length of the shortest proof that taking action a leads to utility u(a) to account for these issues..
The second point is more controversial, my intuition is that first agent is an equally good optimizer, even if it did better in terms of payoffs. Also, at least in the setting of deterministic games, utility functions are arbitrary up to encoding the same preference orderings (once randomness is introduced this stops being true)
I think decision problems with incomplete information are a better model in which to measure optimization power than deterministic decision problems with complete information are. If the agent knows exactly what payoffs it would get from each action, it is hard to explain why it might not choose the optimal one. In the example I gave, the first agent could have mistakenly concluded that the .9-utility action was better than the 1-utility action while making only small errors in estimating the consequences of each of its actions, while the second agent would need to make large errors in estimating the consequences of its actions in order to think that the .1-utility action was better than the 1-utility action.
I agree with the first point, and I don’t have solid solutions to this. There’s also the fact that some games are easier to optimize than others (name a number game I described at the end vs. chess), and this complexity is impossible to capture while staying computation-agnostic. Maybe one can use the length of the shortest proof that taking action a leads to utility u(a) to account for these issues..
The second point is more controversial, my intuition is that first agent is an equally good optimizer, even if it did better in terms of payoffs. Also, at least in the setting of deterministic games, utility functions are arbitrary up to encoding the same preference orderings (once randomness is introduced this stops being true)
I think decision problems with incomplete information are a better model in which to measure optimization power than deterministic decision problems with complete information are. If the agent knows exactly what payoffs it would get from each action, it is hard to explain why it might not choose the optimal one. In the example I gave, the first agent could have mistakenly concluded that the .9-utility action was better than the 1-utility action while making only small errors in estimating the consequences of each of its actions, while the second agent would need to make large errors in estimating the consequences of its actions in order to think that the .1-utility action was better than the 1-utility action.