I think one important consideration here is who are the agents for which we are doing the prioritization.
If our goal is to start a new charity and we are comparing causes, then all we should care about is the best intervention (we can find) - the one which we will end up implementing.
This is a good point that I hadn’t thought of.
But I slightly disagree with charity example. The main reason is that the intervention that’s in general best may not be the one that’s best for whatever audience we’re talking to, due to personal fit. (In both cases, “best” should be interpreted as “best in expectation, on the margin, given our current knowledge and time available for searching”, but that’s irrelevant to the point I want to make.)
This is most obvious if we’re planning to ourselves run the charity. It’s less obvious if we’re doing something more like what Charity Entrepreneurship does, where we’ll ultimately seek out people from a large pool, since then we can seek people out partly based on personal fit for our charity idea. But:
our pool may still tend to be stronger in some areas than others, as is the case with EAs
if we have to optimise strongly for personal fit, we might have to sacrifice some degree of general competence/career capital/whatever, such that ultimately more good would’ve been done by a different founder running a charity that’s focused on an intervention that’d be less good in general (ignoring personal fit)
A smaller reason why I disagree is that, even if our primary goal is to start a new charity, it may be the case that a non-negligible fraction of the impact of our research comes from other effects (e.g., informing donors, researchers, people deciding on careers unrelated to charity entrepreneurship). This seems to be the case for Charity Entrepreneurship, and analogous things seem to be the case for 80,000 Hours, GiveWell, etc. But this point feels more like a nit-pick.
In any case, as mentioned, I do think that your point is a good one, and I think I only slightly disagree :)
This is a good point that I hadn’t thought of.
But I slightly disagree with charity example. The main reason is that the intervention that’s in general best may not be the one that’s best for whatever audience we’re talking to, due to personal fit. (In both cases, “best” should be interpreted as “best in expectation, on the margin, given our current knowledge and time available for searching”, but that’s irrelevant to the point I want to make.)
This is most obvious if we’re planning to ourselves run the charity. It’s less obvious if we’re doing something more like what Charity Entrepreneurship does, where we’ll ultimately seek out people from a large pool, since then we can seek people out partly based on personal fit for our charity idea. But:
our pool may still tend to be stronger in some areas than others, as is the case with EAs
if we have to optimise strongly for personal fit, we might have to sacrifice some degree of general competence/career capital/whatever, such that ultimately more good would’ve been done by a different founder running a charity that’s focused on an intervention that’d be less good in general (ignoring personal fit)
A smaller reason why I disagree is that, even if our primary goal is to start a new charity, it may be the case that a non-negligible fraction of the impact of our research comes from other effects (e.g., informing donors, researchers, people deciding on careers unrelated to charity entrepreneurship). This seems to be the case for Charity Entrepreneurship, and analogous things seem to be the case for 80,000 Hours, GiveWell, etc. But this point feels more like a nit-pick.
In any case, as mentioned, I do think that your point is a good one, and I think I only slightly disagree :)