>The shape of your action profiles depends on your probability function

Are you saying that there is no expected utility just because people have different expectations?

>and your utility function

Well, of course. That doesn’t mean there is no expected utility! It’s just different for different agents.

>I’m arguing that even if you ignore infinitely valuable outcomes, there’s still a big problem having to do with infinitely many possible finite outcomes,

That in itself is not a problem, imagine a uniform distribution from 0 to 1.

>if the profiles are funnel-shaped then what you end up doing will be highly arbitrary, determined mostly by whatever is happening at the place where you happened to draw the cutoff.

If you do something arbitrary like drawing a cutoff, then of course how you do it will have arbitrary results. I think the lesson here is not to draw cutoffs in the first place.

>That’s what I’d like to think, and that’s what I do think. But this argument challenges that; this argument says that the low-hanging fruit metaphor is inappropriate here: there is no lowest-hanging fruit or anything close; there is an infinite series of fruit hanging lower and lower, such that for any fruit you pick, if only you had thought about it a little longer you would have found an even lower-hanging fruit that would have been so much easier to pick that it would easily justify the cost in extra thinking time needed to identify it… moreover, you never really “pick” these fruit, in that the fruit are gambles, not outcomes; they aren’t actually what you want, they are just tickets that have some chance of getting what you want. And the lower the fruit, the lower the chance...

There must be a lowest hanging fruit out of any finite set of possible actions, as long as “better intervention than” follows basic decision theoretic properties which come automatically if they have expected utility values.

Also, remember the conservation of expected evidence. When we think about the long run effects of a given intervention, we are updating our prior to go either up or down, not predictably making it seem more attractive.

>The shape of your action profiles depends on your probability function

Are you saying that there is no expected utility just because people have different expectations?

>and your utility function

Well, of course. That doesn’t mean there is no expected utility! It’s just different for different agents.

>I’m arguing that even if you ignore infinitely valuable outcomes, there’s still a big problem having to do with infinitely many possible finite outcomes,

That in itself is not a problem, imagine a uniform distribution from 0 to 1.

>if the profiles are funnel-shaped then what you end up doing will be highly arbitrary, determined mostly by whatever is happening at the place where you happened to draw the cutoff.

If you do something arbitrary like drawing a cutoff, then of course how you do it will have arbitrary results. I think the lesson here is not to draw cutoffs in the first place.

>That’s what I’d like to think, and that’s what I do think. But this argument challenges that; this argument says that the low-hanging fruit metaphor is inappropriate here: there is no lowest-hanging fruit or anything close; there is an infinite series of fruit hanging lower and lower, such that for any fruit you pick, if only you had thought about it a little longer you would have found an even lower-hanging fruit that would have been so much easier to pick that it would easily justify the cost in extra thinking time needed to identify it… moreover, you never really “pick” these fruit, in that the fruit are gambles, not outcomes; they aren’t actually what you want, they are just tickets that have some chance of getting what you want. And the lower the fruit, the lower the chance...

There must be a lowest hanging fruit out of any finite set of possible actions, as long as “better intervention than” follows basic decision theoretic properties which come automatically if they have expected utility values.

Also, remember the conservation of expected evidence. When we think about the long run effects of a given intervention, we are updating our prior to go either up or down, not predictably making it seem more attractive.