Ok, we know that we can’t just maximize expected utility, but the four strategies you give seem pretty arbitrary and unlikely to be even close to optimal. Why did you propose them?
Let me suggest another strategy that I think might make more sense. Start by considering what distributions of outcomes are feasible (intuitively). Then, among the set of seemingly feasible distributions, decide which one you most prefer, and try to work out a plan that results in that distribution. If it turns out (while trying to work out the plan) that you were wrong about its feasibility, then adjust your intuition, and reselect the most preferred feasible distribution of outcomes. Repeat this process until you end up with a plan.
This way, you get a plan that at least somewhat approximates optimality, given computational constraints and the fact that you don’t know how to express your values as a utility function.
Ok, we know that we can’t just maximize expected utility, but the four strategies you give seem pretty arbitrary and unlikely to be even close to optimal. Why did you propose them?
Let me suggest another strategy that I think might make more sense. Start by considering what distributions of outcomes are feasible (intuitively). Then, among the set of seemingly feasible distributions, decide which one you most prefer, and try to work out a plan that results in that distribution. If it turns out (while trying to work out the plan) that you were wrong about its feasibility, then adjust your intuition, and reselect the most preferred feasible distribution of outcomes. Repeat this process until you end up with a plan.
This way, you get a plan that at least somewhat approximates optimality, given computational constraints and the fact that you don’t know how to express your values as a utility function.
I’m not sure I know how to consider distributions of outcomes.
That’s more rational (and more difficult), but still only about halfway to expectation maximization.