I only now realized that Raising for Effective Giving (wiki) is a pun. Apologies. 

hear -> hair, right?

This is really strong, and I think the practical takeaways are pretty good. Kyle, maybe you want to publish this (or only their "impact" section) as a full-on post? 

[This is 75% a joke, as Peter developed squigglepy based on QURI's squiggle]

Squiggle vs squigglepy? 

Relatedly, how much of the funding (both for 2022 and for 2024) is for the production of research outputs, compared to how much it is for other operations (like fiscal sponsorships or incubation)?

woops, submitted too early..

I've started to draft a formal proof that under reasonable assumptions we would indeed get a linear relationship between the additive test results increase and the log of the effect on income, but accidently submitting too soon got me thinking that I'm spending too much time on this 🤓 If anyone is interested, I will continue with this proof

Oh, it seems like we've both made the same mistake 😊

If one SD results in a 10% increase, then I think the relevant effect size should be 110% and 2 SD be (110%)^2 rather than 120%, so that generally the logarithm of the effect would be linear with the test results increase (in SDs). Then, I think it makes more sense to approximate it as a geometric mean of these (all numbers > 0).

I've done the wrong calculation earlier, taking the geometric mean of the added percentage which doesn't make sense, as you say. Correcting this, I got 16.4% increase. 

[Note that for small enough effect size this would be very similar, as 

]

• I could do some network analysis to find links between topics, and potentially graph (or animate?) how this network has changed over time.

I think this could help with improving the topics - figure out which aren't used, which can be merged, what hierarchy is most useful etc.

(and really cool idea, btw!)