Yep that sounds good, non-profits should aim to have fairly stable expenditure over the business cycle.

I think I was thrown off your true motivation by the name 'Keynesian altruism'. It might be wise to rename it 'countercyclical' so it doesn't carry the implication that you're looking for an economic multiplier.

The idea that charities should focus on spending money during recessions because of the extra benefit that provides seems wrong to me.

Using standard estimates of the fiscal multiplier during recessions — and ignoring any offsetting effects your actions have on fiscal or monetary policy — if a US charity spends an extra $1 during a recession it might raise US GDP by between $0 and $3.

If you're a charity spending $1, and just generally raising US GDP by $3 is a significant fraction of your total social impact, you must be a very ineffective organisation. I could not recommend giving to such a project.

I'd think such a gain would be swamped like other issues like investment returns, us learning about better charities in future, or the worst problems getting solved leaving us worse giving opportunities, and so on.

An exception might be if you independently thought something like GiveDirectly was the best option and wasn't going to be beaten by another option in future. Then giving money for dispersal during a recession in the recipient country might be, say, twice as good as giving it outside of recession.

There's a bunch of discussion of these issues in my interview with Phil Trammell.

Is there even 1 exclusively about people working at EA organisations?

If someone had taken a different job with the goal of having a big social impact, and we didn't think what they were doing was horribly misguided, I don't think we would count them as having 'dropped out of EA' in any of the 6 data sets.

"For example 80000 Hours have stopped cause prioritisation work to focus on their priority paths"

Hey Sam — being a small organisation 80,000 Hours has only ever had fairly limited staff time for cause priorities research.

But I wouldn't say we're doing less of it than before, and we haven't decided to cut it. For instance see Arden Koehler's recent posts about Ideas for high impact careers beyond our priority paths and Global issues beyond 80,000 Hours’ current priorities.

We aim to put ~10% of team time into underlying research, where one topic is trying to figure out which problems and paths go into each priority level. We also have podcast episodes on newer problems from time to time.

All that said, I am sympathetic to the idea that as a community we are underinvesting in cause priorities research.

It seems like lots of active AI safety researchers, even a majority, are aware of Yudkowsky and Bostrom's views but only agree with parts of what they have to say (e.g. Russell, Amodei, Christiano, the teams at DeepMind, OpenAI, etc).

There may still not be enough intellectual diversity, but having the same perspective as Bostrom or Yudkowsky isn't a filter to involvement.

As Michael says, common sense would indicate I must have been referring to the initial peak, or the peak in interest/panic/policy response, or the peak in the UK/Europe, or peak where our readers are located, or — this being a brief comment on an unrelated topic — just speaking loosely and not putting much thought into my wording.

FWIW it looks like globally the rate of new cases hasn't peaked yet. I don't expect the UK or Europe will return to a situation as bad as the one they went through in late March and early April. Unfortunately the US and Latin America are already doing worse than it was then.

I think you know what I mean — the initial peak in the UK, the country where we are located, in late March/April.

There's often a few months between recording and release and we've had a handful of episodes that took a frustratingly long time to get out the door, but never a year.

The time between the first recording and release for this one was actually 9 months. The main reason was Howie and Ben wanted to go back and re-record a number of parts they didn't think they got right the first time around, and it took them a while to both be free and in the same place so they could do that.

A few episodes were also pushed back so we could get out COVID-19 interviews during the peak of the epidemic.

Thanks for doing this research, nice work.

Could you make your figure a little larger, it's hard to read on a desktop. It might also be easier for the reader if each of the five arguments had a one-word name to keep track of the gist of their actual content.

"As you can see, the winner in Phase 2 was Argument 9 by a nose. Argument 9 was also the winner by a nose in Phase 1, and thus the winner overall."

I don't think this is quite right. Arguments 5 and 12 are very much within the confidence interval for Argument 9. Eyeballing it I would guess we can only be about 60% confident that argument 9 would do better again if you repeated the experiment.

I would summarise the results as follow:

• All five arguments substantially outperformed the control, on average increasing giving by around 45%.
• We also had some evidence that Arguments 5, 9 and 12 all outperformed Arguments 3 and 14, perhaps having about 30% more impact.

Hi Tobias — thanks for the ideas!

Invertebrate welfare is wrapped into 'Wild animal welfare', and reducing long-term risks from malevolent actors is partially captured under 'S-risks'. We'll discuss the other two.