This isn’t a well thought-out argument, but something is bugging me in your claim. The real impact for your work may have some distribution, but I think the expected impact given career choices can be distributed very differently. Maybe, for example, the higher you aim, the more uncertainty you have, so your expectation doesn’t grow as fast.
I find it hard to believe that in real life you face choices that are reflected much better by your graph than Eric’s.
I share some of that intuition as well, but I have trouble conveying it numerically. Suppose that among realistic options that we might consider, we think ex post impact varies by 9 OOMs (as Thomas’ graph implies). Wouldn’t it be surprising if we have so little information that we only have <10^-9 confidence that our best choice is better than our second best choice?
I’m not very confident in my argument, but the particular scenario you describe sounds plausible to me.
Trying to imagine it in a simpler, global health setting—you could ask which of many problems to try to solve (e.g. malaria, snake bites, cancer), some of which may cause several orders of magnitude more suffering than others every year. If the solutions require things that are relatively straightforward—funding, scaling up production of something, etc. - it could be obvious which one to pick. But if the solutions require more difficult things, like research, or like solving decades-old distribution problems in Africa, then maybe the uncertainty can be strong enough to influence your decision noticeably.
This is tricky, because it’s really an empirical claim for which we need empirical evidence. I don’t currently have such evidence about anyone’s counterfactual choices. But I think even if you zoom in on the top 10% of a skewed distribution, it’s still going to be skewed. Within the top 10% (or even 1%) of researchers, nonprofits, it’s likely only a small subset are making most of the impact.
I think it’s true that “the higher we aim, the higher uncertainty we have” but you make it seem as if that uncertainty always washes out. I don’t think it does. I think higher uncertainty often is an indicator that you might be able to make it into the tails. Consider the monetary EV of starting a really good startup or working at a tech company. A startup has more uncertainty, but that’s because it creates the possibility of tail gains.
Anecdotally I think that certain choices I’ve made have changed the EV of my work by orders of magnitude. It’s important to note that I didn’t necessarily know this at the time, but I think it’s true retrospectively. But I do agree it’s not necessarily true in all cases.
This isn’t a well thought-out argument, but something is bugging me in your claim. The real impact for your work may have some distribution, but I think the expected impact given career choices can be distributed very differently. Maybe, for example, the higher you aim, the more uncertainty you have, so your expectation doesn’t grow as fast.
I find it hard to believe that in real life you face choices that are reflected much better by your graph than Eric’s.
I share some of that intuition as well, but I have trouble conveying it numerically. Suppose that among realistic options that we might consider, we think ex post impact varies by 9 OOMs (as Thomas’ graph implies). Wouldn’t it be surprising if we have so little information that we only have <10^-9 confidence that our best choice is better than our second best choice?
I’m not very confident in my argument, but the particular scenario you describe sounds plausible to me.
Trying to imagine it in a simpler, global health setting—you could ask which of many problems to try to solve (e.g. malaria, snake bites, cancer), some of which may cause several orders of magnitude more suffering than others every year. If the solutions require things that are relatively straightforward—funding, scaling up production of something, etc. - it could be obvious which one to pick. But if the solutions require more difficult things, like research, or like solving decades-old distribution problems in Africa, then maybe the uncertainty can be strong enough to influence your decision noticeably.
This is tricky, because it’s really an empirical claim for which we need empirical evidence. I don’t currently have such evidence about anyone’s counterfactual choices. But I think even if you zoom in on the top 10% of a skewed distribution, it’s still going to be skewed. Within the top 10% (or even 1%) of researchers, nonprofits, it’s likely only a small subset are making most of the impact.
I think it’s true that “the higher we aim, the higher uncertainty we have” but you make it seem as if that uncertainty always washes out. I don’t think it does. I think higher uncertainty often is an indicator that you might be able to make it into the tails. Consider the monetary EV of starting a really good startup or working at a tech company. A startup has more uncertainty, but that’s because it creates the possibility of tail gains.
Anecdotally I think that certain choices I’ve made have changed the EV of my work by orders of magnitude. It’s important to note that I didn’t necessarily know this at the time, but I think it’s true retrospectively. But I do agree it’s not necessarily true in all cases.
I had similar thoughts, discussed here after I tweeted about this post and somebody replied mentioning this comment.
(Apologies for creating a circular link loop, as my tweet links to this post, which now has a comment linking to my tweet)