I started writing a comment, but it got too long, so I wrote it up here.
Simon_M
Karma: 1,159
I summarised a little bit how various organisations in the EA space aggregate QALY’s over time here.
What I’ve been unable to find anywhere in the literature is how many QALYs a typical human life equates to? If I save a newborn from dying, is that worth 70 QALYs (~global life expectancy), 50 QALYs (not all of life is lived in good health), or some other value?
I think this post by Open Phil is probably related to what you’re asking for and I would also recommend the GiveWell post on the same topic
I think this is still generally seen as a bit of an open question in the space
How do you square:
The order was: I learned about one situation from a third party, then learned the situation described in TIME, then learned of another situation because I asked the woman on a hunch, then learned the last case from Owen.
with
No other women raised complaints about him to me, but I learned (in some cases from him) of a couple of other situations where his interactions with women in EA were questionable.
Emphasis mine. (Highlighting your first statement implies he informed you of multiple cases and this statement implies he only informed you of one)
Thanks—I’ve already commented. I’m pretty disappointed that Owen resigned 3 days before my comment and I was filibustered. (I’ve already commented there about the timeline, very curious to know what can possibly have been going on during that period other than getting together a PR strategy).
Please would someone be able to put together a slightly more fleshed out timeline of who knew what and when. Best I can tell is:
3rd February 2023 - TIME article published
3rd February 2023 - People start questioning this specific case in the forum
3rd February 2023 - Julia and Owen discuss who should find out about this
3rd February 2023 - Julia informed Nicole that the person was Owen
3rdFebruary 2023 - Julia informs EV US and EV UK boards
4th February 2023 - Julia informed Chana that the person was Owen
11th February 2023 - Owen resigns from the board
20th February 2023 - Owen’s resignation is made public
I know I’m probably being dense here, but would it be possible for you to share what the other possibilities are?
Edit: I guess there’s “The person doesn’t have the role, but we are bound by some kind of confidentiality we agreed when removing them from post”
Just bumping this in case you’ve forgotten. At the moment there only seem to be two possibities: 1/ you forgot about this comment or 2/ the person does still have a role “picking out promising students” as Peter asked. I’m currently assuming it’s 2, and I imagine other people are too.
iirc, there is access to the histogram, which tells you how many people predicted each %age. I then sampled k predictors from that distribution.
“k predictors” is the number of samples I was looking at
”>N predictors” was the total number of people who predicted on a given question
what does SD stand for? Usually I would expect standard deviation?
Yes, that’s exactly right. The HLI methodology consists of polling together a bunch of different studies effect-sizes (measured in standard deviations) and then converting those standard deviations into WELLBYs. (By mulitplying by a number ~2).
No bet from me on the Ozler tria
Fair enough—I’m open to betting on this with anyone* fwiw. * anyone who hasn’t already seen results / involved in the trial ofc
Any intervention is extremely sensitive to implementation details, whether deworming or nets or psychotherapy.
Yes, I’m sorry if my comment appeared to dismiss this fact as I do strongly agree with this.
Maybe some interventions are easier to implement than others, and there might be more variance in the effectiveness of psychotherapy compared with net distribution (although I doubt that, I would guess less variance than nets) but all are very sensitive to implementation details.
This is pretty much my point
I’d be interested in you backing up this comment with a bit explanation if you have time (all good if not!). I know this isn’t your job and you don’t have the time that Joel has, but what is it that has led you to conclude that the numbers are “on the order (or lower) than cash transfers”? Is this comment based on intuition or have you done some maths?
I haven’t done a bottom up analyses, more I have made my own adjustments to the HLI numbers which get me to about that level:
You use 0.88 as the effect-size for StrongMinds whereas I think it’s more appropriate to use something closer to the 0.4/0.5 you use here. (And in fact I actually skew this number even lower than you do)
You convert SDs of depression-scores directly to SDs of well-being, which I strongly object to. I don’t have exact numbers of how I would discount this, but my guess is there are two reasons I want to discount this:
Non-linearity in severity of depression
No perfect correlation between the measures (when I spoke to Joel we discussed this, and I do think your reasoning is reasonable, but I still disagree with it)
I think the fairest way to resolve this would be to bet on the effect-size of the Ozler trial. Where would you make me 50⁄50 odds in $5k?
My analysis of StrongMinds is based on a meta-analysis of 39 RCTS of group psychotherapy in low-income countries. I didn’t rely solely on StrongMinds’ own evidence alone, I incorporated the broader evidence base from other similar interventions too. This strikes me, in a Bayesian sense, as the sensible thing to do.
I agree, but as we have already discussed offline, I disagree with some of the steps in your meta-analyses, and think we should be using effect sizes smaller than the ones you have arrived at. I certainly didn’t mean to claim in my post that StrongMinds has no effect, just that it has an effect which is small enough that we are looking at numbers on the order (or lower) than cash-transfers and therefore it doesn’t meet the bar of “Top-Charity”.
I think Simon would define “strong evidence” as recent, high-quality, and charity-specific. If that’s the case, I think that’s too stringent. That standard would imply that GiveWell should not recommend bednets, deworming, or vitamin-A supplementation.
I agree with this, although I think the difference here is I wouldn’t expect those interventions to be as sensitive to the implementation details. (Mostly I think this is a reason to reduce the effect-size from the meta-analysis, whereas HLI thinks it’s a reason to increase the effect size).
As a community, I think that we should put some weight on a recommendation if it fits the two standards I listed above, according to a plausible worldview (i.e., GiveWell’s moral weights or HLI’s subjective wellbeing approach). All that being said, we’re still developing our charity evaluation methodology, and I expect our views to evolve in the future.
I agree with almost all of this. I don’t think we should use HLI’s subjective wellbeing approach until it is better understood by the wider community. I doubt most donors appreciate some of the assumptions the well-being approach makes or the conclusions that it draws.
I had seen both of those, but I didn’t read either of them as commitments that HLI thinks that the neutral point is between 0 and 5.
I agree, although I’ll give you good odds the 20y moves more.
I would guess some combination of:
Increasing longevity (which note the authors say has an effect in the FOOM scenario, but not in the aligned scenario...)
Decreasing credit risk (what was ‘risk free’ in the 1400s is very different to what is ‘risk free’ today)
Consumption preferences being correlated to growth
I don’t really have a strong opinion on any of these—macro is really hard and really uncertain. To quote a friend of mine:
one thing is that if AGI looks something like robin hanson’s EM scenario, you really don’t want to owe money to anyone
or if otherwise your labor is going to become worthless
because then all you have is whatever capital you happen to own
and if you borrowed to consume more today then that’s probably not going to be goodBorrowing and consuming because AGI is coming seems an incredibly risky proposition.
I’m really confused where any of those numbers have come from for using futures? (But yes, the expected return with low leverage is not spectacular for 2% move in rates).
I don’t think that makes much sense tbh.
I want to suggest a bunch of caution against shorting bonds (or tips).
The 30yr yield is 3.5%, so you make −3.5% per year from that.
You earn the cash rate on the capital freed up from the shorts, which is 3.8% in interactive brokers.
If you’re right that the real interest rate will rise 2% over 20 years, and average duration is 20 years, then you make +40% over 20 years – roughly 2% per year.
If you buy an ETF, maybe you lose 0.4% in fees.
So you end up with a +1.9% expected return per year.
I think the calculation you’ve done here is −3.5% + 3.8% + 2% − 0.4%
This doesn’t quite make sense. The first rate you are talking about is the yield on a 30y bond. The second rate (should be) the overnight repo. What you should actually look at is the average overnight repo over 30y. The 30y SOFR swap is ~2.9% which would be a more relevant comparison to your 30y.
A simpler way to think about all of this would be to have some number for losses on fees (“shorting fees” ie your repo costs + ETF fees if you execute via an ETF) and some number for return from being right (change in real rates * duration).
I would agree (roughly) with your calculations if this happens gradually over 20y. If the market is about to realise this overnight, then you wil make 40% overnight. This is what they are advocating for. (Maybe not overnight, but over a shorter time horizon than you are implying).
(Either way I agree with you that shorting bonds is a terrible strategy to implement just based on this post)
A 60:40 portfolio has an effective duration of ~40 years, where most of that duration comes from equities.
I’m not really sure how you get that? The duration on the bond portion is going to be ~7-10y which would imply 60y duration for equities, which I think is wrong.
My understanding is that an important part of the reasoning for a focus on avoiding bonds is that an increase in GDP growth driven by AI is clearly negative for bonds, but has an ambiguous effect on equities (plus commodities and real estate), so overall you should hold more equities (/growth assets) and less bonds. Is that right?
That is their claim, but as I pointed out here the evidence isn’t so clear.
Yes—you are correct.
There’s a 3rd reason, which I expect is the biggest contributor. Number of readers of the post/comment.