This is cool, thanks!
One scenario I am thinking about is how to prioritise biorisk interventions, if you care about both x-risk and non-x-risk impacts. I’m going to run through some thinking, and ask if you think it makes sense:
I think it is hard (but not impossible) to compare between x-risk and non-x-risk impacts
I intuitively think that x-risk and non-x-risk impacts are likely to be lognormally distributed (but this might be wrong)
This seems to suggest that if I want to do the most good, I should max out on on one, even if I care about both equally. I think the intuition for this is something like:
If x-risk and non-x-risk impacts were normally distributed, you’d expect that there are plenty of interventions which score well on both. The EV for both is reasonably smoothly distributed; it’s not very unlikely to draw something which is between 50th and 75th percentile on both, and that’s pretty good EV wise.
But if they are log normal instead, the EV is quite skewed: the best interventions for x-risk and for non-x-risk impacts are a lot better than the next-best. But it’s statistically very unlikely that the 99th percentile on one axis is also the 99th on the other
If I care about EV, but not about whether I get it via x-risk or non-x-risk impacts (I care equally about x-risk and non-x-risk impacts), I should therefore pick the very best interventions on either axis, rather than trying to compromise between them
However, I think that assumes that I know how to identify the very best interventions on one or both axes
Actually I expect it to be quite hard to tell whether an intervention is 70th or 99th percentile for x-risk/non-x-risk impacts
What should I do, given that I don’t know how to identify the very best interventions along either axis?
If I max out, I may end up doing something which is mediocre on one axis, and totally irrelevant on the other
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
So maybe I should go for the best of both worlds in any case?
What do you think? I’m not sure if that reasoning follows/if I’ve applied the lessons from your post in a sensible way.
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?