Thanks for the comment! The reasoning looks good, and was thought-provoking.
If I instead go for the best of both worlds, it seems intuitively more likely that I end up with something which is mediocre on both axes—which is a bit better than mediocre on one and irrelevant on the other
I think I disagree with you here. I model being bad at choosing good interventions as randomly sampling from the top n% (e.g. 30%) from the distribution when I’m trying to choose the best thing along the axis of e.g. non-x-risk impact. If this is a good way of thinking about it, then I don’t think that things change a lot—because of the concavity of the frontier, things I choose from that set are still going to be quite good from a non-x-risk perspective, and pretty middling from the x-risk perspective.
I am very unsure about this, but I think it might look like in this image:
When you choose from the top 30% on popularity, you get options from the purple box at random, and same for options in the green box for effectiveness.
If you want to push axes, I guess you’re going to aim for selecting from the intersection of both boxes, but I’m suspicious you actually can do that, or whether you end up selecting from the union of the boxes instead60%. Because if you can select from the intersection, you get options that are pretty good along both axes, pretty much by definition.
I could use my code to quantify how good this would be, though a concrete use case might be more illuminating.
if you can select from the intersection, you get options that are pretty good along both axes, pretty much by definition.
Isn’t this an argument for always going for the best of both worlds, and never using a barbell strategy?
a concrete use case might be more illuminating.
This isn’t super concrete (and I’m not if the specific examples are accurate), but for illustrative purposes, what if:
Portable air cleaners score very highly for non-x-risk benefits, and low for x-risk benefits
Interventions which aim to make far-UVC commercially viable look pretty good on both axes
Deploying far-UVC in bunkers scores very highly for x-risk benefits, and very low for non-x-risk benefits
I think a lot of people’s intuition would be that the compromise option is the best one to aim for. Should thinking about fat tails make us prefer one or other of the extremes instead?
Thanks for the comment! The reasoning looks good, and was thought-provoking.
I think I disagree with you here. I model being bad at choosing good interventions as randomly sampling from the top n% (e.g. 30%) from the distribution when I’m trying to choose the best thing along the axis of e.g. non-x-risk impact. If this is a good way of thinking about it, then I don’t think that things change a lot—because of the concavity of the frontier, things I choose from that set are still going to be quite good from a non-x-risk perspective, and pretty middling from the x-risk perspective.
I am very unsure about this, but I think it might look like in this image:
When you choose from the top 30% on popularity, you get options from the purple box at random, and same for options in the green box for effectiveness.
If you want to push axes, I guess you’re going to aim for selecting from the intersection of both boxes, but I’m suspicious you actually can do that, or whether you end up selecting from the union of the boxes instead60%. Because if you can select from the intersection, you get options that are pretty good along both axes, pretty much by definition.
I could use my code to quantify how good this would be, though a concrete use case might be more illuminating.
Thanks! I’m now unsure what I think.
Isn’t this an argument for always going for the best of both worlds, and never using a barbell strategy?
This isn’t super concrete (and I’m not if the specific examples are accurate), but for illustrative purposes, what if:
Portable air cleaners score very highly for non-x-risk benefits, and low for x-risk benefits
Interventions which aim to make far-UVC commercially viable look pretty good on both axes
Deploying far-UVC in bunkers scores very highly for x-risk benefits, and very low for non-x-risk benefits
I think a lot of people’s intuition would be that the compromise option is the best one to aim for. Should thinking about fat tails make us prefer one or other of the extremes instead?