This is a super valuable article, thank you for sharing!

Based on this, would it be sensible or interesting to fund a third-party M&E organisation, whose costs are considered somewhat separately, that provides independent evaluation at a relatively early stage and that builds more knowledge around how to do this well over time? Are there funders that might be interested in this?

The exchange rate & inflation fluctuation argument is also interesting. Clearly, EA organisations won't plausibly be able to predict this (otherwise they should be trading currencies!). 
However, the volatility can be modelled reasonably well, and this is a source of uncertainty in CE estimates that should be considered. However, it is usually not communicated. RCTs' uncertainty, implementation biases and publication bias are somewhat similar in nature. They are tricky to model, but even a rough model would be great.

I would love to see more CE estimates whose output is an entire distribution and not just a point estimate (looking at you, GiveWell ;) ). This can be done, even very approximately, and it would be an improvement over what is commonly shared. I feel a reasonable version of this could be vibecoded fairly quickly?

There does indeed seem to be a split in the community, but I’m not sure it’s great to work towards that rather than against it. 
I kind of try to speak to folks in EA 2.0 occasionally despite being pretty squarely in EA 1.0 and that’s probably net positive, e.g. to avoid a complete echo chamber?

Yes, funding eg “research that also produces forecasts” seems in a completely different category to me than eg prediction markets or platform building. 

I feel the original article perhaps conflates different types of “forecast” funding a bit too much, although I tend to agree with its overall sentiment. 

Yes, exactly. My point is that people are pretty aware and claiming otherwise is a bit of a straw man type fallacy - but I might be wrong, perhaps I interact with different people :D 

I think this is essentially a straw man?
Everyone I know who doesn’t like donating to AI safety basically thinks it’s because p(influencing the outcome positively) is too low. 

Prediction markets seem to be a great business (mostly gambling with all the problems associated with it) so “funding” in the sense of investing in them could be sensible while “funding” in the donation sense not. (And then later donation to AMF or similar). 

In general, I’m hesitant to donate to stuff that’s plausibly just a really good business in its own right. 

Note that in the context of trading/investing, the two terms are often used differently. There, “mean reversion” often means negative autocorrelation of returns, which can either be ~causal or driven by price level noise (which in turn is more like a “regression to the mean” idea). If you invest in a mean reversion strategy you tend to have an actual mechanism in mind though.  

“Regression to the mean” is a less ambiguous  term and generally means what you describe. 

Thanks a lot Joey, this is definitely worth reading for people in the wider EA space, not only larger scale donors or people working in philanthropy directly. 

What I’ve found particularly helpful are the rough quantitative guidelines regarding “charity time consumed per amount donated” and “how to donate as a function of annual amount and time spent per week”. 
 
This is very valuable to better position myself from an earning-to-give perspective. 

I think it might perhaps be interesting to write a short summary of that for the forum, perhaps targeted more at a median e2g EA? (If that doesn’t exist already.)

Separately, it’s great to see that the book really embraces plurality in what areas donors prioritise without too strong a view on what’s preferable in the author’s opinion. 

Yes thanks 

boy did this age in favour of "good judgement" as a factor!