CarlShulman comments on Giving What We Can, 80,000 Hours, and Meta-Charity - Less Wrong
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Comments (182)
This is a really interesting issue, and it applies to any exceptional giving candidate, not just to meta-charities. In order to get exceptional value for money you need to (correctly) believe that you are smarter than the big donors - otherwise they'd already have funded whatever you're planning on funding to the point where the returns diminish to the same level as everything else.
This relates to the issue of collecting lots of hard data because rationality is partly about the ability to make the right decision given a relatively small amount of data.
My tentative conclusion is that if you have no good reason to believe you're more rational than the big money then the best thing is to invest your resources in improving your own rationality.
And sensibly collecting obtainable data that could make a big difference for a decision. Making correct decisions with less data is harder, and so more taxing of epistemic rationality, but that difficulty means it's often instrumentally rational to avoid such difficulty.
Yep, totally agree - see this comment and this post.
I'd treat the graph of GiveWell's money moved as evidence in favour of meta (and in particular CEA) being promising, under three assumptions:
In a way you could regard any charity fundraising as "meta" in some sense, but the market there is already saturated in a way that I don't think "effective giving" is. So I wouldn't expect people to be getting such huge returns from fundraising (even if they're trying a somewhat novel approach), but I wouldn't count this as strong evidence against meta.
Definitely curious about what other kinds of evidence I should be on the lookout for, or for reasons why I shouldn't take GW's big takeoff so seriously.
Yes, that and the stats for Giving What We Can/CEA look pretty good.
I think competition tends to be good! It keeps people on their toes, and provides a check on problems. Consider your other point:
With competitors you could check the rate of concordance, when they disagree, or look to see which organizations identify problems with data first, that sort of thing.
Cannot upvote this enough. Neglected Virtue of Scholarship and all that.