Rohin’s opinion: I agree that this is an important distinction to keep in mind. It seems to me that the distinction is whether the optimizer has knowledge about the environment: in canonical examples of the first kind of optimizer, it does not. If we somehow encoded the dynamics of the world as a SAT formula and asked a super-powerful SAT solver to solve for the actions that accomplish some goal, it would look like the second kind of optimizer.
It seems to me that a SAT solver can be arbitrarily competent at solving SAT problems without being the second kind of optimizer (i.e. without acting upon its environment to change it), even while it solves SAT problems that encode the dynamics of our world. For example, this seems to be the case for a SAT solver that is just a brute force search with arbitrarily large amount of computing power.
[EDIT: When writing this comment, I considered “the environment of a SAT solver” to be the world that contains the computer running the SAT solver. However, this seem to contradict what Joar had in mind in his post].
It seems to me that a SAT solver can be arbitrarily competent at solving SAT problems without being the second kind of optimizer (i.e. without acting upon its environment to change it), even while it solves SAT problems that encode the dynamics of our world. For example, this seems to be the case for a SAT solver that is just a brute force search with arbitrarily large amount of computing power.
[EDIT: When writing this comment, I considered “the environment of a SAT solver” to be the world that contains the computer running the SAT solver. However, this seem to contradict what Joar had in mind in his post].