Econ PhD focusing on global priorities research, ex-McKinsey Global Institute fellow. Founder of McKinsey Effective Altruism community and board member of EA Norway. Follow me on Twitter at @jgraabak
Agree with many of the considerations above - the bar should probably rise somewhat after such a funding shortfall. One way to solve it in practice could be to sit down in the room with the old FTX FF team and ask "which XX% of your grants are you most enthusiastic about and why", and then (at least as an initial hypothesis; possibly requiring some further vetting) plan to fund that. The generalized point I'm trying to make is twofold: 1) that quite a bit of judgement already went into assessing these projects and it should be possible to use that to decide how many of them are above the bar, and 2) because all the other input factors (talent, project idea, vetting) are unchanged, and assuming a standard shape of the EA production function, the marginal returns to funding should now be unusually high.
And David is right that (at least under some reasonable models) if you can predict that your bar will fall in the future, you should probably lower it already. I'm not exactly sure what the requirements would be for the funding bar to have a Martingale property (e.g., does it require some version of risk neutrality, or specific assumptions about the shape of the impact distribution across projects or time), but it seems reasonable to start with something close to that assumption, at least. However that still implies that when you experience a large, unexpected funding shortfall, the bar does need to rise somewhat.
Thank you for a good and swift response, and in particular, for stating so clearly that fraud cannot be justified on altruistic grounds.
I have only one quibble with the post: IMO you should probably increase your longtermist spending quite significantly over the next ~year or so, for the following reasons (which I'm sure you've already considered, but I'm stating them so others can also weigh in)
Thank you for your good work over the last months, and thank you for your commitment to integrity in these hard times. I'm sure this must also be hard for you on a personal level, so I hope you're able to find consolation in all the good that will be created from the projects you helped off the ground, and that you still find a home in the EA community.
Hi Adam! Thanks for the detailed reply. From a brief look at your model, it seems you've understood my reasoning in this post correctly. I had indeed overlooked that their numbers were already discounted.
However, since they use a 3% discount rate and you use a 4% discount rate, you would still need to adjust for the difference. If we still assume that the economic impacts hit throughout your entire career, from 15 to 60 years into the future (note: 15 years into the future is not the average, but the initial year of impacts!), then you get to around $0.7 of NPV for each $1 today - much better than the $0.28 in my analysis, but still less than the $1 without discounting. Using this number, the result would be very close to GiveWell's 20% estimate. How curious!
Thank you for this post, this is excellent work! Are you aware of ongoing efforts for any of your proposed topics? I'm asking because I'd consider starting a project on some of the above.
Thank you, Miranda!
I agree that it is a decision to be made on a project-by-project basis, but you can still have some prior about what’s roughly the right thing to do in aggregate, and use that prior to assess if you’re clearly missing the mark. This may feel like an artificial or useless exercise, but in general it is how high-level strategy decisions are made. Perhaps we’re just talking around each other because we are on different abstraction levels - you’re perhaps imagining a grant maker asking “how should I achieve this outcome” while I’m imagining “what’s the right strategy for EA as a whole”?
Side note: In this case, 100% prizes would clearly be the wrong percentage. 0% prizes is likely too low, and the realistic range is maybe 1-20%, but I don’t know with higher precision than that. However the movement looks very different with 1% vs. 20%, and getting it right could matter quite a bit.
The flip side of this is that people with less existing “reputation stock” may see the potential status upside as the main compensation from a prize contest, and not the monetary benefit
I think the “get lots of input in a short time from a crowd with different semi-informed opinions” feature of prizes are hard to replace through other mechanisms. Some companies have built up extensive expert networks that they can call on-demand to do this, but that still doesn’t have quite the same agility. However, in those cases you may often want to compensate more than just the best entry (in line with the OP)
One interesting debate would be: what’s the optimal % of funding that should go to prizes? Which parameters would allow us to determine this? One can imagine that the % should be higher in communities that are struggling more to hire enough, or where research agendas are unclear so more coordination is needed, but should be lower in communities with people with low savings, or where the funders have capacity to diversify risks.
One additional consideration is that the coordination benefits from prizes (in raising the salience of memes or the status of the winners) comes at an attention cost, so a large number of prizes may cannibalize on our “common knowledge budget” (if there is a limit to how much common knowledge we can generate)