I have no data on catastrophe relief, and no idea besides googling a bunch to make myself an idea.

For scalable interventions in preventive health, there are some typical EA examples like:
 - bednets to prevent malaria
 - seasonal chemoprevention for malaria
 - vitamin A supplementation
 - vaccination incentives

I personally don't have any data on the latter, but GiveWell has done a bunch of practical research aggregation / outreach, for instance here:
https://www.givewell.org/how-much-does-it-cost-to-save-a-life

"your money goes a long way" do you have any numbers on this? think that we have to compare to scalable interventions in preventive health, for instance, the bar is quite high

What do you mean by "tested"? What outcomes are you measuring / or do you plan to do surveys...?

The difference between the most vs least spooky X-risks is way more than a 100X difference.

I think I would agree with this, if I had to put a number.

What I mean in my comment is, with this model, if you say okay let's pick a bigger n so that we see bigger differences in OOMs, then you are also introducing more points of failure in the estimation, and that effect dominates.

Do you have an a priori reason to discard this? Besides the conclusion being wacky, which is a good reason to discard a model anyways.

the OOM of variation in "ground truth" come from alpha and n, not xmin

alpha, we could talk all day, but the model is not extremely sensitive to it

on the other hand, if you say let's have more OOMs in the possible values of ground truth, following the power law, that means jacking n up

and when you jack n up you have even more opportunities for errors to be crazy big, and this effect dominates (at least that's what I read from the OP) and the curse becomes worse

now if we change alpha and n at the same time, idk

my honest opinion is that numbers are just one way to process information, and using them for this is so out of distribution that it's essentially meaningless (as it is when discussing p(doom) and stuff like that)

Ties in with the more meta-level fact that numbers are used a lot in EA/rationalist spaces, even when there are kinds of uncertainty that don't go along with them.

I'm sure people have written about this many times but don't know who to cite.

Hi, could you share some info for reference:

• Breakdown of expenses
• How much did it cost (in time) to run the stuff
• Schedule

Hi, I don't love talking to GPT but:

1. I agree with you that there is a vibe that if you aren't doing The Most Impactful Thing (TM), you don't count. That's uncomfortable.
2. I disagree with your split between "practitioners" and "NGO workers". NGOs have practitioners, if they are delivering something.
3. I think urbanism (and policy in general) can be a high impact area, but you need to compare it to other aspects and policy. I haven't run any numbers so I can't know.

Not an answer but Rational Reminder (nerdy evidence-based finance podcast by Ben Felix and colleagues) interviewed Elie Hassenfeld (from GiveWell). Super interesting:
https://rationalreminder.ca/podcast/372