Daniel V


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To get Robin worried about AI doom, I'd need to convince him that there's a different metric he needs to be tracking

That, or explain the factors/why the Robin should update his timeline for AI/computer automation taking "most" of the jobs.

AI Doom Scenario

Robin's take here strikes me both as an uncooperative thought-experiment participant and as a decently considered position. It's like he hasn't actually skimmed the top doom scenarios discussed in this space (and that's coming from me...someone who has probably thought less about this space than Robin) (also see his equating corporations with superintelligence - he's not keyed into the doomer use of the term and not paying attention to the range of values it could take).

On the other hand, I find there is some affinity with my skepticism of AI doom, with my vibe being it's in the notion that authorization lines will be important.

On the other other hand, once the authorization bailey is under siege by the superhuman intelligence aspect of the scenario, Robin retreats to the motte that there will be billions of AIs and (I guess unlike humans?) they can't coordinate. Sure, corporations haven't taken over the government and there isn't one world government, but in many cases, tens of millions of people coordinate to form a polity, so why would we assume all AI agents will counteract each other?

It was definitely a fun section and I appreciate Robin making these points, but I'm finding myself about as unassuaged by Robin's thoughts here as I am by my own.

Robin: We have this abstract conception of what it might eventually become, but we can't use that abstract conception to do very much now about the problems that might arise. We'll need to wait until they are realized more.

When talking about doom, I think a pretty natural comparison is nuclear weapon development. And I believe that analogy highlights how much more right Robin is here than doomers might give him credit for. Obviously a lot of abstract thinking and scenario consideration went into developing the atomic bomb, but also a lot of safeguards were developed as they built prototypes and encountered snags. If Robin is so correct that no prototype or abstraction will allow us address safety concerns, so we need to be dealing with the real thing to understand it, then I think a biosafety analogy still helps his point. If you're dealing with GPT-10 before public release, train it, give it no authorization lines, and train people (plural) studying it to not follow its directions. In line with Robin's competition views, use GPT-9 agents to help out on assessments if need be. But again, Robin's perspective here falls flat and is of little assurance if it just devolves into "let it into the wild, then deal with it."

A great debate and post, thanks!

Paper from the Federal Reserve Bank of Dallas estimates 150%-300% returns to government nondefense R&D over the postwar period on business sector productivity growth. They say this implies underfunding of nondefense R&D, but that is not right. One should assume decreasing marginal returns, so this is entirely compatible with the level of spending being too high. I also would not assume conditions are unchanged and spending remains similarly effective.


At low returns, you might question whether it's good enough to invest more compared to other options (e.g., at 5%, maybe simply not incurring the added deficit to be financed at 5% is arguably preferable; at 7%, maybe your value function is such that simply not incurring the added deficit to be financed at 5% is arguably preferable), but at such high returns, unless you think the private sector is achieving a ballpark level of marginal returns, invest, baby, invest! The marginal returns would have to be insanely diminishing for it not to make sense to invest more, which implies we're investing at just about the optimal level (if the marginal return of the next $1 were 0%, we shouldn't invest more, but we shouldn't invest less either because our current marginal return is 150%). Holding skepticism about the estimated return itself would be a different story.

That is an additional 15% of kids not sleeping seven hours

I was not aware of the concomitant huge drop in sleep (though it's obvious in retrospect). Maybe it's more important to limit screen time at night, when you're alone in your room not sleeping. Being constantly lethargic as a result may also contribute to (and be a) depressive symptoms. It will be very important to figure out the mechanism(s) by which smartphone use hurts kids.

I agree, I was thinking more generally this isn't a "poker" theory specifically, just one about rules and buy-in. But it's about poker night, so I'll let it slide. The main game rules, though, remain extraneous. Loved the post still!

Mira: You should be able to buy anything with a limit order.

“I don’t feel like paying $250 for an anime figurine, but I left an order up for $50”

If they saw 10,000 orders at a lower price rung ...

As usual the answer is transaction costs

Agree and also perceptions. The idea here is to facilitate price discovery and price discrimination. If only we knew people's WTP and could serve them lower prices acceptable to us when volume isn't moving at the current price! We can adjust prices ad hoc, but maybe a little upfront market research would be better and an exchange might be smoother (subject to TCs). The flipside of this has the problem that consumers hate it [Reuters]. Also, hedging (see: futures markets) does happen in B2B, but with more sophisticated owners and larger businesses. The supply chain is constantly to optimize inventory management (again, not mom-and-pops you see on save-my-business shows).

Why is turbulence worse on planes? The headlines blame it on ‘climate change.’ The actual answer is the FAA told airlines to prioritize saving fuel over passenger comfort, despite passengers having a strong revealed preference for spending the extra cost of fuel to have a more pleasant flight. This then became ‘because climate change.’ This kind of thing damages public trust in all such claims, making solving climate change (and everything else) that much harder.

There are benefits to optimized profile descents (fuel, time, reduced air traffic controller instructions, reduced noise over populated areas), which they did studies on to confirm since in high traffic airspace the stepwise approach can be easier for ATC. This change could conceivably increase turbulence on approach but would not explain the increase that "the narrative" is attributing to increased wind shear at higher altitudes. 

I agree with Neil here: if you identify with your flaws, that is bad. By definition. If you are highly analytical and you identify with it, great, regardless of if other people see it as a flaw. Like you said and Neil's reply in the footnote, if it's a goal, then it is not a flaw. But if you say it is a personal flaw, then either you shouldn't be adopting it into your identity (you don't even have to try to fix it as noble as that would be, but you don't get to say "I'm the bad-at-math-person, it's so funny and quirky, and I just led my small business and partners into financial ruin with an arithmetic mistake," life is not a sit-com) or maybe you don't really see it as a flaw after all. Either way, something is wrong, either in your priorities or the reliability of your self-reports. And, yeah, this topic involves value judgments. If nothing has valence, then the notion of a flaw would not exist. 

I quite appreciate the post's laying things out, but it's not convincing regarding Scott's post (it's not bad either, just not convincing!) because it doesn't offer much more than "no, you're wrong." The crux of the argument presented here is taking the word disability, which to most speakers means X and implies Y, and breaking it into an impairment, which means X, and a disability, which is Y. Scott says this is wrong and explains why he thinks so. DirectedEvolution says Scott is wrong "because the definitions say..." but that's exactly what Scott is complaining about.

For example, if you're short-sighted, normally we'd say "you have a disability (or impairment or handicap, etc., they're interchangeable) of your vision so that means you will struggle with reading road signs." Instead, the social model entails saying "you have an impairment of your vision so that means, because of society, you will be disabled when it comes to reading road signs."

We can debate which view is more useful (and for what purposes). Scott thinks the social model is useful to promote accommodations since it separates the physical condition from the consequences (whether it produces negative consequences depends on society). He thinks the Szaz-Caplan model is useful to deny accommodations since it separates the mental condition (i.e., preferences, in that model) from the consequences (whether it produces negative consequences depends on will). More importantly, he thinks the social model is "slightly wrong about some empirical facts" (what empirical facts? DirectedEvolution is correct that Scott's argumentation is a bit soft...he benefits greatly from arguing the layperson side) in that in some cases it feels absurd to pin blame on society for the consequences of some impairments (e.g., Mt. Everest). And on that your layperson (and I) would agree with him. DirectedEvolution offers no counterpoint on that (which is the primary argument), but the post DOES provide a key benefit:

Adopting separate definitions for impairment and disability IS NOT strictly equivalent to adopting the social model. One could restate short-sightedness: "you have an impairment of your vision so that means you will be disabled when it comes to reading road signs." This drops the blame game and allows for impairments to disable people outside of societies. In fact, Scott accidentally endorsed it [added by me]: "the blind person’s inability to drive [disability] remains due to their blindness [impairment], not society." So perhaps the crux of Scott's argument is not about using two definitions but about whether disability ought to be defined as stemming from society! And in fact that's evident in Scott's post. However, Scott's post DID also, at times, imply that one definition would suffice.

This post made me update toward two definitions potentially being useful, but it did not make me update away from endorsing Scott's main point, that disability ought not be defined as stemming from society.

As an aside: the two definitions are still debatable though. Suppose someone has an impairment that has not nor ever will generate a disability. How is this not the same as "there exists variability"? If someone has perfect vision and I am short-sighted but we live in a dome with a 5 foot diameter such that I can see just fine, and no one tells me my lived experience could be better, how could you even call that an impairment? Is it an impairment if I realize that my vision could be better? Is that other person impaired if they realize their vision could be improved above "normal"? "Impairment" could just refer to being low on the spectrum of natural human variability in some capability, but how low is low enough? "So low that it starts to interfere..." is bringing disability into the mix. What capabilities count? Certainly not "reading road signs" as that would be in the realm of disability, but what level of specificity is appropriate? Short-sightedness is not an impairment of seeing near objects, it's an impairment of seeing far objects, so that is to say, not vision generally. But once you get specific enough, it's back to sounding like a disability - "your far object vision is impaired so you are disabled at seeing far objects."

It's very interesting to see the intuitive approach here and there is a lot to like about how you identified something you didn't like in some personality tests (though there are some concrete ones out there), probed content domains for item generation, and settled upon correlations to assess hanging-togetherness.

But you need to incorporate your knowledge from reading about scale development and factor analysis. Obviously you've read in that space. You know you want to test item-total correlations (trait impact), multi-dimensionality (factor model loss), and criterion validity (correlation with lexical notion). Are you trying to ease us in with a primer (with different vocabulary!) or reinvent the wheel?

Let's start with the easy-goingness scale:

  • (+) In the evening I tend to relax and watch some videos/TV
  • (+) I don’t feel the need to arrange any elaborate events to go to in my free time
  • (+) I think it is best to take it easy about exams and interviews, rather than worrying a bunch about doing it right
  • (+) I think you’ve got to have low expectations of others, as otherwise they will let you down
  • (-) I get angry about politics
  • (-) I have a stressful job
  • (-) I don’t feel like I should have breaks at work unless I’ve “earned” them by finishing something productive
  • (-) I spent a lot of effort on parenting

The breadth of it is either a strength or a weakness. It'd be nice to have a construct definition or at least some gesturing at what easy-goingness actually is to gauge the face-validity of these items. Concrete items necessarily will have some domain-dependence, resulting in deficiency (e.g., someone who likes to relax and read a book will score low on item 1) or contamination (e.g., having low expectations of others might also be trait pessimism), but item 8 is really specific. It hampers the ability of this scale to capture easy-goingness among non-parents. The breadth would be good if it captured variations on easy-goingness, but instead it'd be bad if it just captures different things that don't really relate to each other. That's especially problematic because then the inference from low inter-correlations might not be that the construct is bad, but that the items just don't tap into it. You can see where I'm going with this because...

This suggests to me that Easy-Goingness is not very “real”. While it might make sense to describe a person as doing something Easy-Going, for instance when they are watching TV, it is kind of arbitrary to talk about people as being more or less Easy-Going, because it depends a lot on context/what you mean.

...indeed, the items are mainly just capturing different things, not reflecting on easy-goingness in any way. From a scale-assessment standpoint, it's great to see the results confirm my unease about the items based on simply reading them.

The fact that this is weak means that even the most Easy-Going people cannot necessarily be expected to be particularly Easy-Going in all contexts.

This statement presumes your measure reflects a higher-order easy-goingness and that context-specific easy-goingnesses are also being adequately measured.

With conservatism, on the other hand, you can see there is some context-specificity (e.g., dress vs. general social views vs. issue-based ideology), but the measure is facially better. And it hangs together better. Alternately, you might explore those contours and say you've come up with a multi-dimensional conservatism scale, just like you have a multi-dimensional creativity scale. 

the “Correlation with lexical notion” was consistently close to 1, showing that the concrete and the abstract descriptors were getting at the same thing.

There's an implicit "when the concrete descriptors actually had face validity" hidden here; low correlation with the lexical notion could indicate a problem with the lexical scale or a problem with the concrete scale, or both. 

Overall, I am very impressed that you presented a scary chart to start, promised you'd explain it, and successfully did so. The general takeaway from it is that the lexical hypothesis could be pretty sound and a few of these might be multidimensional in nature (or could be that some items are good and some a bad). For the low trait impact scales, it's a question of whether the items are good and the construct isn't "real," or whether the items are just a bad measurement approach.

Who has an alternative hypothesis that explains this data? Anyone? Ooh ooh, pick me, pick me. Perhaps being depressed has something to do with your life being depressing, due to things like lack of human capital or job opportunities, life and career setbacks or alienation from one’s work. Income increases life satisfaction, as I assume does the prospect of future income.

It is amazing to see the ‘depression is purely a chemical imbalance unrelated to one’s physical circumstances’ attitude in this brazen a form. Mistaking correlation for causation here seems like a difficult mistake for a reasonable and reflecting person to make.


They measured depression at ages 27-35 in 1992 and outcomes at age 50. They control for "age, gender, race, for level of education by age 26, parental education, r marital status in 1992 survey, years of work experience accumulated by 1992 survey, the average percentage of weeks the person’s work history data is unaccounted for by 1992 survey, health status during childhood, a dummy for number of cigarettes consumed by 1992 survey, year indicators, local unemployment rate in 1992, 1998, 2004, and the year the person’s outcome variable is collected."

So it's not like they just correlated depression and wages from a cross-sectional survey and claimed causation. They did some work here.

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