Something I imagined while reading this was being part of a strangely massive (~1000 person) extended family whose goal was to increase the net wealth of the family. I think it would be natural to join one of the family businesses, it would be natural to make your own startup, and also it would be somewhat natural to provide services for the family that aren't directly about making the money yourself. Helping make connections, find housing, etc.
Yeah, I think you understand me better now.
And btw, I think if there are particular grants that seem not in scope from a fund, is seems totally reasonable to ask them for their reasoning and update pos/neg on them if the reasoning does/doesn't check out. And it's also generally good to question the reasoning of a grant that doesn't make sense to you.
Though it still does seem to me like those two grants are probably better fits for LTFF.
But this line is what I am disagreeing with. I'm saying there's a binary of "within scope" or not, and then otherwise it's up to the fund to fund what they think is best according to their judgment about EA Infrastructure or the Long-Term Future or whatever. Do you think that the EAIF should be able to tell the LTFF to fund a project because the EAIF thinks it's worthwhile for EA Infrastructure, instead of using the EAIF's money? Alternatively, if the EAIF thinks something is worth money for EA Infrastructure reasons, if the grant is probably more naturally under the scope of "Long-Term Future", do you think they shouldn't fund the grantee even if LTFF isn't going to either?
Yeah, that's a good point, that donors who don't look at the grants (or know the individuals on the team much) will be confused if they do things outside the purpose of the team (e.g. donations to GiveDirectly, or a random science grant that just sounds cool), that sounds right. But I guess all of these grants seem to me fairly within the purview of EA Infrastructure?
The one-line description of the fund says:
The Effective Altruism Infrastructure Fund aims to increase the impact of projects that use the principles of effective altruism, by increasing their access to talent, capital, and knowledge.
I expect that for all of these grants the grantmakers think that they're orgs that either "use the principle of effective altruism" or help others do so.
I think I'd suggest instead that weeatquince name some specific grants and ask the fund managers the basic reason for why those grants seem to them like they help build EA Infrastructure (e.g. ask Michelle why CLTR seems to help things according to her) if that's unclear to weeatquince.
The inclusion of things on this list that might be better suited to other funds (e.g the LTFF) without an explanation of why they are being funded from the Infrastructure Fund makes me slightly less likely in future to give directly to the Infrastructure Fund and slightly more likely to just give to one of the bigger meta orgs you give to (like Rethink Priorities).
I think that different funders have different tastes, and if you endorse their tastes you should consider giving to them. I don't really see a case for splitting responsibilities like this. If Funder A thinks a grant is good, Funder B thinks it's bad, but it's nominally in Funder B's purview, this just doesn't seem like a strong arg against Funder A doing it if it seems like a good idea to them. What's the argument here? Why should Funder A not give a grant that seems good to them?
Thanks for the thoughtful reply.
I do think I was overestimating how robust you're treating your numbers and premises, it seems like you're holding them all much more lightly than I think I'd been envisioning.
FWIW I am more interested in engaging with some of what you wrote in in your other comment than engaging on the specific probability you assign, for some of the reasons I wrote about here.
I think I have more I could say on the methodology, but alas, I'm pretty blocked up with other work atm. It'd be neat to spend more time reading the report and leave more comments here sometime.
Great answer, thanks.
I tried to look for writing like this. I think that people do multiple hypothesis testing, like Harry in chapter 86 of HPMOR. There Harry is trying to weigh some different hypotheses against each other to explain his observations. There isn't really a single train of conditional steps that constitutes the whole hypothesis.
My shoulder-Scott-Alexander is telling me (somewhat similar to my shoulder-Richard-Feynman) that there's a lot of ways to trick myself with numbers, and that I should only do very simple things with them. I looked through some of his posts just now (1, 2, 3, 4, 5).
Here's an example of a conclusion / belief from Scott's post Teachers: Much More Than You Wanted to Know:
In summary: teacher quality probably explains 10% of the variation in same-year test scores. A +1 SD better teacher might cause a +0.1 SD year-on-year improvement in test scores. This decays quickly with time and is probably disappears entirely after four or five years, though there may also be small lingering effects. It’s hard to rule out the possibility that other factors, like endogenous sorting of students, or students’ genetic potential, contributes to this as an artifact, and most people agree that these sorts of scores combine some signal with a lot of noise. For some reason, even though teachers’ effects on test scores decay very quickly, studies have shown that they have significant impact on earning as much as 20 or 25 years later, so much so that kindergarten teacher quality can predict thousands of dollars of difference in adult income. This seemingly unbelievable finding has been replicated in quasi-experiments and even in real experiments and is difficult to banish. Since it does not happen through standardized test scores, the most likely explanation is that it involves non-cognitive factors like behavior. I really don’t know whether to believe this and right now I say 50-50 odds that this is a real effect or not – mostly based on low priors rather than on any weakness of the studies themselves. I don’t understand this field very well and place low confidence in anything I have to say about it.
I don't know any post where Scott says "there's a particular 6-step argument, and I assign 6 different probabilities to each step, and I trust that outcome number seems basically right". His conclusions read more like 1 key number with some uncertainty, which never came from a single complex model, but from aggregating loads of little studies and pieces of evidence into a judgment.
I think I can't think of a post like this by Scott or Robin or Eliezer or Nick or anyone. But would be interested in an example that is like this (from other fields or wherever), or feels similar.
One thing that I think would really help me read this document would be (from Joe) a sense of "here's the parts where my mind changed the most in the course of this investigation".
Something like (note that this is totally made up) "there's a particular exploration of alignment where I had conceptualized it as kinda like about making the AI think right but now I conceptualize it as about not thinking wrong which I explore in section a.b.c".
Also maybe something like a sense of which of the premises Joe changed his mind on the most – where the probabilities shifted a lot.