Thanks, appreciate the empirical note and graph on trendlines!

Preventing an AI takeover is a great way for countries to help their own people!

Tbh, my honest if somewhat flippant response is that these trials should update us somewhat against marginal improvements in the welfare state in rich countries, and more towards investments in global health, animal welfare, and reductions in existential risk. 

I'm sure this analysis will go over well to The Argument subscribers!

It's funny (and I guess unsurprising) that Will's Gemini instance and your Claude instance both reflected what I would have previously expected both of your ex ante views to be! 

lmao when I commented 3 years ago I said 

As is often the case with social science research, we should be skeptical of out-of-country and out-of-distribution generalizability. 

and then I just did an out-of-country and out-of-distribution generalization with no caveats! I could be really silly sometimes lol.

Re the popular post on UBI by Kelsey going around, and related studies:

I think it helped less than I “thought” it would if I was just modeling this with words. But the observed effects (or lack thereof) in the trials appears consistent with standard theoretical models of welfare economics. So I’m skeptical of people using this as an update against cash transfers, in favor of a welfare state, or anything substantial like that.

If you previously modeled utility as linear or logarithmic with income (or somewhere in between), these studies should be a update against your worldview. But I don’t think those were ever extremely plausible to begin with.

See further discussions here:

https://x.com/LinchZhang/status/1958705316276969894

and here:

https://forum.effectivealtruism.org/posts/AAZqD2pvydH7Jmaek/lorenzo-buonanno-s-shortform?commentId=rXLQJLTH7ejJHcJdt

Tbc I have always been at least a little skeptical of UBI ever since I've heard of the idea. But I also buy the "poor people are poor because they don't have enough money" argument, at least in low-income countries. So I don't really have a dog in this fight.

(It’s mildly frustrating that The Argument doesn’t open up comments to people who aren’t paid subscribers, since I think this is an important point that most readers of that Kelsey post (and possibly the writer/editors) are not yet getting)

Hmm, I'd guess the s.d.s to be lower in the US than in Kenya, for what it's worth. Under five child mortality rates are about 10x higher in Kenya than in the US, and I expect stuff like that to bleed through to other places.

But even if we assume a smaller s.d. (check: is there a smaller s.d., empirically?), this might be a major problem. The top paper on OpenResearch says they can rule out health improvements greater than 0.023-0.028 standard deviations from giving $1000/month. I'm not sure how it compares to households incomes, but let's assume that household income is $2000-$3000/month for the US recipients now, so the transfer is 33-50% of household income.

From Googling around, the GiveDirectly studies show mental health effects around a quarter of a standard deviation from a doubling of income. 

In other words, the study can rule out effect sizes the size that theory would predict if theory predicts effect sizes >0.1x the effect size in GiveDirectly experiments. 

Does theory predict effect sizes >0.1x that of the GiveDirectly experiments? Well, it depends on ň, the risk-aversion constant^[1]! If risk aversion is between 0 (linear, aka insane) and 1 (logarithmic) we should predict changes >.41 x to >.58x that of the GD experiments. So we can rule out linera, logarithmic, and super-logarithmic utility!

But if ň=1.5, then theory will predict changes on the scale of 0.076x to 0.128x that of the GD experiments. Ie, exactly in the boundary of whether it's possible to detect an effect at all or not!

If ň =2, then theory will predict changes on the scale of 0.014x to 0.028x, or much smaller than the experiments are powered to detect.

For what it's worth, before the studies I would've guessed a risk-aversion constant across countries to be something in between 1.2 and 2, so this study updates me some but not a lot.

@Kelsey Piper and others, did you or the study authors pre-register your beliefs on what risk-aversion constant you expected? 

1. ^{^}

   rendering the greek constant economists use for risk aversion

   as ň since it otherwise doesn't render correctly on my laptop.

I expected low effects based on background assumptions like utility being sublogarithmic to income but I didn't expect the effect to be actually zero (at the level of power that the very big studies could detect). 

I'd be interested in replicating these trials in other developed countries. It could just be because the US is unusually wealthy. 

Of course, like you say, this is further evidence we should increase foreign aid, since money could do far more good in very poor countries than very rich ones.

Published a review of Ted Chiang, my favorite science fiction short story writer. 

Most relevant to EAs: he's one of the few living SF writers who portrays technology as potentially enhancing humanity rather than dystopian. I really like how he imagines what's possible and takes ideas seriously. But he completely misses societal-level responses to transformative tech. His worlds get universe-altering inventions and use them for personal therapy instead of solving coordination problems or running multiverse-wide RCTs.

In (attempted) blinded trials, my review is consistently ranked #1 by our AI overlords, so check out the one book review that all the LLMs are raving about!!!

I think as an individual reading and mathematical modeling is more conducive to learning true things about the world more than most other things on the list. Certainly I read much more often than I conduct RCTs! Even working scientists have reading the literature as a major component of their overall process.

I also believe this is true for civilization overall. If we imagine in an alternative civilization that is incapable of RCTs but can learn things from direct observation, natural experiments, engineering, etc, I expect substantial progress is still possible. However, if all information can only be relayed via the oral tradition, I think it'd be very hard to build up a substantial civilization. There's a similar argument for math as well, though less so.   

Likewise I think what thought experiments I'm influenced by is more important than the idea that thought experiments are (possibly) less trustworthy than at helping me make decisions than a full blown philosophical framework or more trustworthy than folk wisdown.

Sure, the article discusses this in some detail. Context and discernment definitely matters. I could've definitely spent more effort on it, but I was worried it was already too long, and am also unsure if I could provide anything novel that's relevant to specific people's situations anyway.

FWIW I think the infographic was fine and would suggest reinstating it (I don't think the argument is clearer without it, and it's certainly harder for people to suggest methods you might have missed if you don't show methods you included!)

I think the infographic probably makes it more likely for people to downvote the post without reading it. 

Your linkpost also strips most of the key parts from the article, which I suspect some of the downvoters missed

Yeah the linkpost is just an introduction + explanation of why the post is relevant to EA Forum + link. I strongly suspect, based on substack analytics (which admittedly might be inaccurate) most people who downvoted the post didn't read or even skim the post. I frankly find this extremely ^[1]rude. 

1. ^{^}

   (Less than 1% of my substack's views came from the EA Forum, so pretty much every single one of the clickers have to have downvoted; I think it's much more likely that people who didn't read the post downvoted. I personally only downvote posts I've read, or at least skimmed carefully enough that I'm confident I'd downvote upon a closer read. I can't imagine having the arrogance to do otherwise.)