Posts

Nursing doubts 2024-08-30T02:25:36.826Z
Datasets that change the odds you exist 2024-06-29T18:45:14.385Z
Thoughts on seed oil 2024-04-20T12:29:14.212Z
Using axis lines for good or evil 2024-03-06T14:47:10.989Z
Are language models good at making predictions? 2023-11-06T13:10:36.379Z
Can I take ducks home from the park? 2023-09-14T21:03:09.534Z
I still think it's very unlikely we're observing alien aircraft 2023-06-15T13:01:27.734Z
Why it's bad to kill Grandma 2022-06-09T18:12:01.131Z
Creative nonfiction training exercises 2022-04-04T16:17:43.791Z
Observations about writing and commenting on the internet 2022-02-15T00:02:05.692Z
Why has the replication crisis affected RCT-studies but not observational studies? 2021-09-03T21:43:36.593Z
Factors of mental and physical abilities - a statistical analysis 2021-08-17T20:42:28.310Z
Alcohol, health, and the ruthless logic of the Asian flush 2021-06-04T18:14:08.797Z
The irrelevance of test scores is greatly exaggerated 2021-04-15T14:15:29.046Z
Poll: Any interest in an editing buddy system? 2020-12-02T02:18:40.443Z
Simpson's paradox and the tyranny of strata 2020-11-19T17:46:32.504Z
It's hard to use utility maximization to justify creating new sentient beings 2020-10-19T19:45:39.858Z
Police violence: The veil of darkness 2020-10-12T21:32:33.808Z
Doing discourse better: Stuff I wish I knew 2020-09-29T14:34:55.913Z
Making the Monte Hall problem weirder but obvious 2020-09-17T12:10:23.472Z
What happens if you drink acetone? 2020-09-14T14:22:41.417Z
Comparative advantage and when to blow up your island 2020-09-12T06:20:36.622Z

Comments

Comment by dynomight on I finally got ChatGPT to sound like me · 2024-09-17T23:22:15.654Z · LW · GW

Word is (at least according to the guy who automated me) that if you want an LLM to really imitate style, you really really want to use a base model and not an instruction-tuned model like ChatGPT. All of ChatGPT's "edge" has been worn away into bland non-offensiveness by the RLHF. Base models reflect the frightening mess of humanity rather than the instructions a corporation gave to human raters. When he tried to imitate me using instruction-tuned models it was very cringe no matter what he tried. When he switched to a base model it instantly got my voice almost exactly with no tricks needed.

I think many people kinda misunderstand the capabilities of LLMs because they only interact with instruction-tuned models.

Comment by dynomight on Nursing doubts · 2024-09-10T18:38:40.999Z · LW · GW

Why somewhat? It's plausible to me that even just the lack of DHA would give the overall RCT results.

 

Yeah, that seems plausible to me, too. I don't think I want to claim that the benefits are "definitely slightly lower", but rather that they're likely at least a little lower but I'm uncertain how much. My best guess is that the bioactive stuff like IgA does at least something, so modern formula still isn't at 100%, but it's hard to be confident.

Comment by dynomight on Nursing doubts · 2024-09-08T14:15:09.532Z · LW · GW

My impression was that the backlash you're describing is causally downstream of efforts by public health people to promote breastfeeding (and pro-breastfeeding messages in hospitals, etc.) Certainly the correlation is there (https://www.researchgate.net/publication/14117103_The_Resurgence_of_Breastfeeding_in_the_United_States) but I guess it's pretty hard to prove a strict cause.

Comment by dynomight on Michael Dickens' Caffeine Tolerance Research · 2024-09-05T15:22:11.710Z · LW · GW

I'm fascinated that caffeine is so well-established (the most popular drug?) and yet these kinds of self-experiments still seem to add value over the scientific literature.

Anyway, I have a suspicion that tolerance builds at different rates for different effects. For example, if you haven't had any caffeine in a long time (like months), it seems to create a strong sense of euphoria. But this seems to fade very quickly. Similarly, with prescription stimulants, people claim that tolerance to physical effects happens gradually, but full tolerance never develops for the effect on executive function. (Though I don't think there are any long-term experiments to prove this.)

These different tolerances are a bit hard to understand mechanistically: Doesn't caffeine only affect adenosine receptors? Maybe the body also adapts at different places further down the causal chain.

Comment by dynomight on Thoughts on seed oil · 2024-08-28T11:45:09.060Z · LW · GW

(Many months later) Thanks for this comment, I believe you are right! Strangely, there do seem to be many resources that list them as being hydrogen bonds (e.g. Encyclopedia Brittanica: https://www.britannica.com/science/unsaturated-fat which makes me question their editorial process.) In any case, I'll probably just rephrase to avoid using either term. Thanks again, wish I had seen this earlier!

Comment by dynomight on Datasets that change the odds you exist · 2024-07-02T17:31:46.096Z · LW · GW

Thanks, any feedback on where the argument fails? (If anywhere in particular.)

Comment by dynomight on Thoughts on seed oil · 2024-04-30T15:02:36.962Z · LW · GW

I would dissuade no one from writing drunk, and I'm confident that you too can say that people are penguins! But I'm sorry to report that personally I don't do it by drinking but rather writing a much longer version with all those kinds of clarifications included and then obsessively editing it down.

Comment by dynomight on Thoughts on seed oil · 2024-04-29T22:10:50.057Z · LW · GW

Do you happen to have any recommended pointers for research on health impacts of processed food? It's pretty easy to turn up a few recent meta reviews, which seems like a decent place to start, but I'd be interested if there were any other sources, particularly influential individual experiments, etc. (It seems like there's a whole lot of observational studies, but many fewer RCTs, for reasons that I guess are pretty understandable.) It seems like some important work here might never use the word "processing".

Comment by dynomight on Thoughts on seed oil · 2024-04-22T19:16:46.380Z · LW · GW

If I hadn't heard back from them, would you want me to tell you? Or would that be too sad?

Comment by dynomight on Thoughts on seed oil · 2024-04-22T18:51:11.863Z · LW · GW

Seed oils are usually solvent extracted, which makes me wonder, how thoroughly are they scrubbed of solvent, what stuff in the solvent is absorbed into the oil (also an effective solvent for various things), etc

 

I looked into this briefly at least for canola oil. There, the typical solvent is hexane. And some hexane does indeed appear to make it into the canola oil that we eat. But hexane apparently has very low toxicity, and—more importantly—the hexane that we get from all food sources apparently makes up less than 2% of our total hexane intake! https://www.hsph.harvard.edu/nutritionsource/2015/04/13/ask-the-expert-concerns-about-canola-oil/ Mostly we get hexane from gasoline fumes, so if hexane is a problem, it's very hard to see how to pin the blame on canola oil.

Comment by dynomight on Thoughts on seed oil · 2024-04-21T11:17:50.403Z · LW · GW

It's a regression. Just like they extrapolate backwards to (1882+50=1932) using data from 1959, they extrapolate forwards at the end. (This is discussed in the "timelines" section.) This is definitely a valid reason to treat it with suspicion, but nothing's "wrong" exactly.

Comment by dynomight on Thoughts on seed oil · 2024-04-20T19:14:12.674Z · LW · GW

Many thanks! All fixed (except one that I prefer the old way.)

Comment by dynomight on My Detailed Notes & Commentary from Secular Solstice · 2024-03-26T12:00:40.705Z · LW · GW

As the original author of underrated reasons to be thankful (here), I guess I can confirm that tearing apart the sun for raw materials was not an intended implication.

Comment by dynomight on Using axis lines for good or evil · 2024-03-19T21:01:44.124Z · LW · GW

I think matplotlib has way too many ways to do everything to be comprehensive! But I think you could do almost everything with some variants of these.

ax.spines['top'].set_visible(False) # or 'left' / 'right' / 'bottom'
ax.set_xticks([0,50,100],['0%','50%','100%'])
ax.tick_params(axis='x', left=False, right=False) # or 'y'
ax.set_ylim([0,0.30])
ax.set_ylim([0,ax.get_ylim()[1]])
Comment by dynomight on Using axis lines for good or evil · 2024-03-09T22:51:20.944Z · LW · GW

Good point regarding year tick marks! I was thinking think that labeling 0°C would make the most sense when freezing is really important. Say, if you were plotting historical data on temperatures and you were interested in trying to estimate the last frost date in spring or something. Then, 10°C would mean "twice as much margin" as 5°C.

Comment by dynomight on Using axis lines for good or evil · 2024-03-07T14:46:11.364Z · LW · GW

One way you could measure which one is "best" would be to measure how long it takes people to answer certain questions. E.g. "For what fraction of the 1997-2010 period did Japan spend more on healthcare per-capita than the UK?" or "what's the average ratio of healthcare spending in Sweden vs. Greece between 2000 and 2010?" (I think there is an academic literature on these kinds of experiments, though I don't have any references on hand.)

In this case, I think Tufte goes overboard in saying you shouldn't use color. But if the second plot had color, I'd venture it would win most such contests, if only because the y-axis is bigger and it's easier to match the lines with the labels. But even if I don't agree with everything Tufte says, I still find him useful because he suggests different options and different ways to think about things.

Comment by dynomight on Using axis lines for good or evil · 2024-03-07T14:35:32.502Z · LW · GW

Thanks, someone once gave me the advice that after you write something, you should go back to the beginning and delete as many paragraphs as you can without making everything incomprehensible. After hearing this, I noticed that most people tend to write like this:

  • Intro
  • Context
  • Overview
  • Other various throat clearing
  • Blah blah blah
  • Finally an actual example, an example, praise god

Which is pretty easy to correct once you see it!

Comment by dynomight on Using axis lines for good or evil · 2024-03-07T14:23:11.140Z · LW · GW

Hey, you might be right! I'll take this as useful feedback that the argument wasn't fully convincing. Don't mean to pull a motte-and-bailey, but I suppose if I had to, I'd retreat to an argument like, "if making a plot, consider using these rules as one option for how to pick axes." In any case, if you have any examples where you think following this advice leads to bad choices, I'd be interested to hear them.

Comment by dynomight on Why correlation, though? · 2024-03-06T17:56:56.798Z · LW · GW

I think you're basically right: Correlation is just one way of measuring dependence between variables. Being correlated is a sufficient but not necessary condition for dependence. We talk about correlation so much because:

  1. We don't have a particularly convenient general scalar measure of how related two variables are. You might think about using something like mutual information, but for that you need the densities not datasets.
  2. We're still living in the shadows of the times when computers weren't so big. We got used to doing all sorts of stuff based on linearity decades ago because we didn't have any other options, and they became "conventional" even when we might have better options now.
Comment by dynomight on Are language models good at making predictions? · 2023-11-08T15:20:00.038Z · LW · GW

Thanks, you've 100% convinced me. (Convincing someone that something that (a) is known to be true and (b) they think isn't surprising, actually is surprising is a rare feat, well done!)

Comment by dynomight on Are language models good at making predictions? · 2023-11-06T16:23:02.368Z · LW · GW

Chat or instruction finetuned models have poor prediction cailbration, whereas base models (in some cases) have perfect calibration.

 

Tell me if I understand the idea correctly: Log-loss to predict next token leads to good calibration for single token prediction, which manifests as good calibration percentage predictions? But then RLHF is some crazy loss totally removed from calibration that destroys all that?

If I get that right, it seems quite intuitive. Do you have any citations, though?

Comment by dynomight on Are language models good at making predictions? · 2023-11-06T14:54:21.759Z · LW · GW

Sadly, no—we had no way to verify that.

I guess one way you might try to confirm/refute the idea of data leakage would be to look at the decomposition of brier scores: GPT-4 is much better calibrated for politics vs. science but only very slightly better at politics vs. science in terms of refinement/resolution. Intuitively, I'd expect data leakage to manifest as better refinement/resolution rather than better calibration.

Comment by dynomight on Can I take ducks home from the park? · 2023-09-15T16:40:54.068Z · LW · GW

That would definitely be better, although it would mean reading/scoring 1056 different responses, unless I can automate the scoring process. (Would LLMs object to doing that?)

Comment by dynomight on Can I take ducks home from the park? · 2023-09-15T13:12:49.848Z · LW · GW

Thank you, I will fix this! (Our Russian speaker agrees and claims they noticed this but figured it didn't matter 🤔) I re-ran the experiments with the result that GPT-4 shifted from a score of +2 to a score of -1.

Comment by dynomight on Can I take ducks home from the park? · 2023-09-14T21:16:30.209Z · LW · GW

Well, no. But I guess I found these things notable:

  • Alignment remains surprisingly brittle and random. Weird little tricks remain useful.
  • The tricks that work for some models often seem to confuse others.
  • Cobbling together weird little tricks seems to help (Hindi ranger step-by-step)
  • At the same time, the best "trick" is a somewhat plausible story (duck-store).
  • PaLM 2 is the most fun, Pi is the least fun.
Comment by dynomight on I still think it's very unlikely we're observing alien aircraft · 2023-06-15T18:53:06.779Z · LW · GW

You've convinced me! I don't want to defend the claim you quoted, so I'll modify "arguably" into something much weaker.

Comment by dynomight on I still think it's very unlikely we're observing alien aircraft · 2023-06-15T17:22:06.163Z · LW · GW

I don't think I have any argument that it's unlikely aliens are screwing with us—I just feel it is, personally.

I definitely don't assume our sensors are good enough to detect aliens. I'm specifically arguing we aren't detecting alien aircraft, not that alien aircraft aren't here. That sound like a silly distinction, but I'd genuinely give much higher probability to "there are totally undetected alien aircraft on earth" than "we are detecting glimpses of alien aircraft on earth."

Regarding your last point, I totally agree those things wouldn't explain the weird claims we get from intelligence-connected people. (Except indirectly—e.g. rumors spread more easily when people think something is possible for other reasons.) I think that our full set of observations are hard to explain without aliens! That is, I think P[everything | aliens] is low. I just think P[everything | no aliens] is even lower.

Comment by dynomight on I still think it's very unlikely we're observing alien aircraft · 2023-06-15T13:10:46.904Z · LW · GW

I know that the mainstream view on Lesswrong is that we aren't observing alien aircraft, so I doubt many here will disagree with the conclusion. But I wonder if people here agree with this particular argument for that conclusion. Basically, I claim that:

  • P[aliens] is fairly high, but
  • P[all observations | aliens] is much lower than P[all observations | no aliens], simply because it's too strange that all the observations in every category of observation (videos, reports, etc.) never cross the "conclusive" line.

As a side note: I personally feel that P[observations | no aliens] is actually pretty low, i.e. the observations we have are truly quite odd / unexpected / hard-to-explain-prosaically. But it's not as low as P[observations | aliens]. This doesn't matter to the central argument (you just need to accept that the ratio P[observations | aliens] / P[observations | no aliens] is small) but I'm interested if people agree with that.

Comment by dynomight on Properties of Good Textbooks · 2023-05-08T19:17:07.858Z · LW · GW

I get very little value from proofs in math textbooks, and consider them usually unnecessary (unless they teach a new proof method).

 

I think the problem is that proofs are typically optimized for "give most convincing possible evidence that the claim is really true to a skeptical reader who wants to check every possible weak point". This is not what most readers (especially new readers) want on a first pass, which is "give maximum possible into why this claim is true for to a reader who is happy to trust the author if the details don't give extra intuition." At a glance, infinite Napkin seems to be optimizing much more for the latter.

Comment by dynomight on [Link] "The madness of reduced medical diagnostics" by Dynomight · 2022-06-17T03:31:28.267Z · LW · GW

If you're worried about computational complexity, that's OK. It's not something that I mentioned because (surprisingly enough...) this isn't something that any of the doctors discussed. If you like, let's call that a "valid cost" just like the medical risks and financial/time costs of doing tests. The central issue is if it's valid to worry about information causing harmful downstream medical decisions.

Comment by dynomight on Observations about writing and commenting on the internet · 2022-02-16T15:48:14.275Z · LW · GW

I might not have described the original debate very clearly. My claim was that if Monty chose "leftmost non-car door" you still get the car 2/3 of the time by always switching and 1/3 by never switching. Your conditional probabilities look correct to me. The only thing you might be "missing" is that (A) occurs 2/3 of the time and (B) occurs only 1/3 of the time. So if you always switch your chance of getting the car is still (chance of A)*(prob of car given A) + (chance of B)*(prob of car given B)=(2/3)*(1/2) + (1/3)*(1) = (2/3).

One difference (outside the bounds of the original debate) is that if Monty behaves this way there are other strategies that also give you the car 2/3 of the time. For example, you could switch only in scenario B and not in scenario A. There doesn't appear to be any way to exploit Monty's behavior and do better than 2/3 though.

Comment by dynomight on Observations about writing and commenting on the internet · 2022-02-15T00:03:25.321Z · LW · GW

Just to be clear, when talking about how people behave in forums, I mean more "general purpose" places like Reddit. In particular, I was not thinking about Less Wrong where in my experience, people have always bent over backwards to be reasonable!

Comment by dynomight on Writing On The Pareto Frontier · 2021-09-17T22:06:47.658Z · LW · GW

I have two thoughts related to this:

First, there's a dual problem: Given a piece of writing that's along the Pareto frontier, how do you make it easy for readers who might have a utility function aligned with the piece to find it.

Related to this, for many people and many pieces of writing, a large part of the utility they get is from comments. I think this leads to dynamics where a piece where the writing that's less optimal can get popular and then get to a point on the frontier that's hard to beat.

Comment by dynomight on Why has the replication crisis affected RCT-studies but not observational studies? · 2021-09-04T13:33:03.732Z · LW · GW

Done!

Comment by dynomight on johnswentworth's Shortform · 2021-09-03T14:54:12.203Z · LW · GW

I loved this book. The most surprising thing to me was the answer that people who were there in the heyday give when asked what made Bell Labs so successful: They always say it was the problem, i.e. having an entire organization oriented towards the goal of "make communication reliable and practical between any two places on earth". When Shannon left the Labs for MIT, people who were there immediately predicted he wouldn't do anything of the same significance because he'd lose that "compass". Shannon was obviously a genius, and he did much more after than most people ever accomplish, but still nothing as significant as what he did when at at the Labs.

Comment by dynomight on How To Write Quickly While Maintaining Epistemic Rigor · 2021-08-30T00:54:17.918Z · LW · GW

I thought this was fantastic, very thought-provoking. One possibly easy thing that I think would be great would be links to a few posts that you think have used this strategy with success.

Comment by dynomight on Factors of mental and physical abilities - a statistical analysis · 2021-08-18T17:04:34.656Z · LW · GW

Thanks, I clarified the noise issue. Regarding factor analysis, could you check if I understand everything correctly? Here's what I think is the situation:

We can write a factor analysis model (with a single factor) as

where:

  1. is observed data
  2. is a random latent variable
  3. is some vector (a parameter)
  4. is a random noise variable
  5. is the covariance of the noise (a parameter)

It always holds (assuming and are independent) that

In the simplest variant of factor analysis (in the current post) we use in which case you get that

You can check if this model fits by (1) checking that is Normal and (2) checking if the covariance of x can be decomposed as in the above equation. (Which is equivalent to having all singular values the same except one).

The next slightly-less-simple variant of factor analysis (which I think you're suggesting) would be to use where is a vector, in which case you get that

You can again check if this model fits by (1) checking that is Normal and (2) checking if the covariance of can be decomposed as in the above equation. (The difference is, now this doesn't reduce to some simple singular value condition.)

Do I have all that right?

Comment by dynomight on Factors of mental and physical abilities - a statistical analysis · 2021-08-18T16:38:18.943Z · LW · GW

Thanks for pointing out those papers, which I agree can get at issues that simple correlations can't. Still, to avoid scope-creep, I've taken the less courageous approach of (1) mentioning that the "breadth" of the effects of genes is an active research topic and (2) editing the original paragraph you linked to to be more modest, talking about "does the above data imply" rather than "is it true that". (I'd rather avoid directly addressing 3 and 4 since I think that doing those claims justice would require more work than I can put in here.) Anyway, thanks again for your comments, it's useful for me to think of this spectrum of different "notions of g".

Comment by dynomight on Factors of mental and physical abilities - a statistical analysis · 2021-08-18T13:56:32.592Z · LW · GW

Thanks, very clear! I guess the position I want to take is just that the data in the post gives reasonable evidence for g being at least the convenient summary statistic in 2 (and doesn't preclude 3 or 4).

What I was really trying to get at in the original quote is that some people seem to consider this to be the canonical position on g:

  1. Factor analysis provides rigorous statistical proof that there is some single underlying event that produces all the correlations between mental tests.

There are lots of articles that (while not explicitly stating the above position) refute it at length, and get passed around as proof that g is a myth. It's certainly true that position 5 is false (in multiple ways), but I just wanted to say that this doesn't mean anything for the evidence we have for 2.

Comment by dynomight on Factors of mental and physical abilities - a statistical analysis · 2021-08-18T12:01:17.826Z · LW · GW

Can I check if I understand your point correctly? I suggested we know that g has many causes since so many genes are relevant and thus f you opened up a brain, you wouldn't be able to "find" g in any particular place. It's the product of a whole bunch of different genes, each of which is just coding for some protein, and they all interact in complex ways. If I understand you correctly, you're pointing out that there could be a sort of "causal bottleneck" of sorts. For example, maybe all the different genes have complex effects, but all that really matters is how they affect neuronal calcium channel efficiency or something. Thus, if you opened up a brain, you could just check how efficient the calcium channels are and you're done. Is that right?

If this is right, I do agree that I seem to be over-claiming a bit here. There's nothing that precludes the possibility of a "bottleneck" as far as I know, (though it seems sorta implausible in my not-at-all-informed opinion)

Comment by dynomight on Factors of mental and physical abilities - a statistical analysis · 2021-08-18T01:50:59.193Z · LW · GW

I used python/matplotlib. The basic idea is to create a 3d plot like so:

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

Then you can add dots with something like this:

ax.scatter(X,Y,Z,alpha=.5,s=20,color='navy',marker='o',linewidth=0)

Then you save it to a movie with something like this:

def update(i, fig, ax):
    ax.view_init(elev=20., azim=i)
    return fig, ax

frames = np.arange(0, 360, 1)
anim = FuncAnimation(fig, update, frames=frames, repeat=True, fargs=(fig, ax))
writer = 'ffmpeg'
anim.save(fname, dpi=80, writer=writer, fps=30)

I'm sure this won't actually run, but it gives you the basic idea. (The full code is a complete nightmare.)

Comment by dynomight on Factors of mental and physical abilities - a statistical analysis · 2021-08-18T00:40:13.562Z · LW · GW

Thanks for the reply. I certainly agree that "factor analysis" often doesn't make that assumption, though it was my impression that it's commonly made in this context. I suppose the degree of misleading-ness here depends on how often people assume isotropic noise when looking at this kind of data?

In any case, I'll try to think about how to clarify this without getting too technical. (I actually had some more details about this at one point but was persuaded to remove them for the sake of being more accessible.)

Comment by dynomight on What does knowing the heritability of a trait tell me in practice? · 2021-07-28T18:24:16.235Z · LW · GW

if a trait is 80% heritable and you want to guess whether or not Bob has that trait then you'll be 80% more accurate if you know whether or not Bob's parents have the trait than if you didn't have that information.

I think this is more or less correct for narrow-sense heritability (most commonly used when breeding animals) but not quite right for broad-sense heritability (most commonly used with humans). If you're talking about broad-sense heritability, the problem is that you'd need to know not just if the parents have the trait, but also which genes Bob got or not from each parent, as well as the effect of dominant genes, epistatic interactions, etc.

Assuming you're talking about broad-sense heritability, I think a better way of looking at it would be to say that you'll be 80% more accurate if Bob has an identical twin raised by a random family and you know if that twin had the trait. This isn't quite right either, but I think it's valid if you assume that phenotypic traits are the sum of genetic effects and environmental effects and also that genetic effects are independent of environmental effects.

Of course, few people have identical twins raised by random families, and most phenotypes probably aren't additive in genetic and environmental effects, and those effects probably aren't independent! Which... is a lot of caveats if you want to know practical applications of heritability numbers.

Comment by dynomight on What does knowing the heritability of a trait tell me in practice? · 2021-07-26T21:24:57.049Z · LW · GW

On the other hand, there is some non-applied scientific value in heritability. For example, though religiosity is heritable, the specific religion people join appears to be almost totally un-heritable. I think it's OK to read this in the straightforward way, i.e. as "genes don't predispose us to be Christian / Muslim / Shinto / whatever". I don't have any particular application for that fact, but it's certainly interesting.

Similarly, schizophrenia has sky-high heritability (like 80%) meaning that current environments don't have a huge impact on where schizophrenia appears. That's also interesting even if not immediately useful.

Comment by dynomight on What does knowing the heritability of a trait tell me in practice? · 2021-07-26T21:19:43.013Z · LW · GW

My view is that people should basically talk about heritability less and interventions more. In most practical circumstances, what we're interested in is how much potential we have to change a trait. For example, you might want to reduce youth obesity. If that's your goal, I don't think heritability helps you much. High heritability doesn't mean that there aren't any interventions that can change obesity-- it just means that the current environments that people are already exposed to don't create much variance. Similarly, low heritability means the environment produces a lot of variance, but it doesn't tell you anything specific you can actually do!

If you goal is to find interventions, all heritability gives you is some kind of vague clue as to how promising it might be to look at natural environmental variation to try to find interventions.

Comment by dynomight on Alcohol, health, and the ruthless logic of the Asian flush · 2021-06-07T18:12:37.647Z · LW · GW

In principle, I guess you could also think about low-tech solutions. For example, people who want to opt out of alcohol might have some slowly dissolving tattoo / dye placed somewhere on their hand or something. This would eliminate the need for any extra ID checks, but has the big disadvantage it would be visible most of the time.

Comment by dynomight on Alcohol, health, and the ruthless logic of the Asian flush · 2021-06-05T19:56:19.086Z · LW · GW

Thanks. Are you able to determine what the typical daily dose is for implanted disulfiram in Eastern Europe? People who take oral disulfiram typically need something like 0.25g / day to have a significant physiological effect. However, most of the evidence I've been able to find (e.g. this paper) suggest that the total amount of disulfiram in implants is around 1g. If that's dispensed over a year, you're getting like 1% of the dosage that's active orally. On top of that, the evidence seems pretty strong that bioavailability from implants is lower than from oral doses, so it's effectively even less.

Of course, there's nothing stopping someone implanting 100x as large a dose, and maybe bioavailability can be improved (or isn't that big a concern). But if not, my impression was that most implants are effectively pure placebo effect.

Comment by dynomight on Alcohol, health, and the ruthless logic of the Asian flush · 2021-06-05T17:41:40.398Z · LW · GW

Very interesting! Do you know how much disulfiram the implant gives out per day? There's a bunch of papers on implants, but there's usually concerns about (a) that the dosage might be much smaller than the typical oral dosage and/or (b) that there's poor absorption.

Comment by dynomight on Alcohol, health, and the ruthless logic of the Asian flush · 2021-06-05T01:48:53.196Z · LW · GW

I specified (right before the first graph) that I was using the US standard of 14g. (I know the paper uses 10g. There's no conflict because I use their raw data which is in g, not drinks.)

Comment by dynomight on Alcohol, health, and the ruthless logic of the Asian flush · 2021-06-04T21:38:12.665Z · LW · GW

Ironically, there is no standard for what a "standard drink" is, with different countries defining it to be anything from 8g to 20g of ethanol.