Last time, I found that the healthiest BMI range for all-cause mortality is 20–22. But BMI doesn’t tell the whole story. Most obviously, it doesn’t account for body fat vs. lean mass. All else equal, you’d rather have more muscle¹ and less fat.

So what’s the healthiest combination of lean mass + fat mass?

I’m not going to answer that question because I can’t. Instead, I will explain why I can’t, and then give a rough guess at the answer.

Scientists have been measuring and collecting data on BMI for decades. You can find plenty of giant BMI studies with three million participants in various countries.

We have much sparser data on body fat. Scientists didn’t start collecting data on body fat until the last few decades. And body fat is harder to measure—we have various methods for estimating body fat, but they’re all more complicated than calculating BMI.

I managed to scrounge together some studies on body fat and mortality. My best guess: the average woman should aim for a BMI of 21 with 20% body fat, and the average man a BMI of 21 with 10% body fat. (Subject to individual variation due to genetics and whatnot.)

Trans men should probably target the same body fat % as cis men, and likewise for trans women and cis women, because hormone therapy alters body fat distribution (Spanos et al. (2020)²).

The evidence weakly suggests that there is no lower bound on healthy fat mass, and no upper bound on healthy lean mass. We have so little mortality data on extremely lean + muscular people that we can’t say how healthy they are.

A more in-depth analysis would look at a variety of health indicators (blood pressure, HDL cholesterol, etc.) and use that to predict mortality. I didn’t do that, I just looked at mortality data.

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TLDR: 20 to 22.

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Last updated 2024-07-26 to clarify wording.

Confidence: Likely.

I conducted an experiment on myself to see if I would develop a tolerance to caffeine from taking it three days a week. The results suggest that I didn’t. Caffeine had just as big an effect at the end of my four-week trial as it did at the beginning.

This outcome is statistically significant (p = 0.016), but the data show a weird pattern: caffeine’s effectiveness went up over time instead of staying flat. I don’t know how to explain that, which makes me suspicious of the experiment’s findings.

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In 2012, Scott Alexander defended social sciences against the claim that they can’t figure anything out. He gave a long list of well-established findings across a variety of social science disciplines.

12 years later, how well did that list hold up?

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Recently, Saar Wilf, creator of Rootclaim, had a high-profile debate against Peter Miller on whether COVID originated from a lab. Peter won and Saar lost.

Rootclaim’s mission is to “overcome the flaws of human reasoning with our probabilistic inference methodology.” Rootclaim assigns odds to each piece of evidence and perfoms Bayesian updates to get a posterior probability. When Saar lost the lab leak debate, some people considered this a defeat not just for the lab leak hypothesis, but for Rootclaim’s whole approach.

In Scott Alexander’s coverage of the debate, he wrote:

While everyone else tries “pop Bayesianism” and “Bayes-inspired toolboxes”, Rootclaim asks: what if you just directly apply Bayes to the world’s hardest problems? There’s something pure about that, in a way nobody else is trying.

Unfortunately, the reason nobody else is trying this is because it doesn’t work. There’s too much evidence, and it’s too hard to figure out how to quantify it.

Don’t give up so easily! We as a society have spent approximately 0% of our collective decision-making resources on explicit Bayesian reasoning. Just because Rootclaim used Bayesian methods and then lost a debate doesn’t mean those methods will never work. That would be like saying, “randomized controlled trials were a great idea, but they keep finding that ESP exists. Oh well, I guess we should give up on RCTs and just form beliefs using common sense.”

(And it’s not even like the problems with RCTs were easy to fix. Scott wrote about 10 known problems with RCTs and 10 ways to fix them, and then wrote about an RCT that fixed all 10¹ of those problems and still found that ESP exists. If we’re going to give RCTs more than 10 tries, we should extend the same courtesy to Bayesian reasoning.)

I’m optimistic that we can make explicit Bayesian analysis work better. And I can already think of ways to improve on two problems with it.

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Last updated 2024-09-02.

Confidence: Likely.

If you take caffeine every day, does it stop working? If it keeps working, how much of its effect does it retain?

There are many studies on this question, but most of them have severe methodological limitations. I read all the good studies (on humans) I could find. Here’s my interpretation of the literature:

• Caffeine almost certainly loses some but not all of its effect when you take it every day.
• In expectation, caffeine retains 1/2 of its benefit, but this figure has a wide credence interval.
• The studies on cognitive benefits all have some methodological issues so they might not generalize.
• There are two studies on exercise benefits with strong methodology, but they have small sample sizes.

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Summary

Caffeine improves cognition¹ and exercise performance². But if you take caffeine every day, over time it becomes less effective.

What if instead of taking caffeine every day, you only take it intermittently—say, once every 3 days? How often can most people take caffeine without developing a tolerance?

The scientific literature on this question is sparse. Here’s what I found:

1. Experiments on rats found that rats who took caffeine every other day did not develop a tolerance. There are no experiments on humans. There are no experiments that use other intermittent dosing frequencies (such as once every 3 days).

2. Internet forum users report that they can take caffeine on average once every 3 days without developing a tolerance. But there’s a lot of variation between individuals.

This post will cover:

1. The motivation for intermittent dosing

2. A review of the experimental research on the effect of taking caffeine intermittently (TLDR: there’s almost no experimental research)

3. A review of self-reports from the online nootropics community

4. Intermittent dosing vs. taking caffeine every day

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In the eyes of popular culture (and in the eyes of many philosophy professors), the essence of utilitarianism is “it’s okay to do bad things for the greater good.” In my mind, that’s not the essence of utilitarianism. The essence is, “doing more good is better than doing less good.”

Utilitarianism is about doing the most good. You don’t do the most good by fretting over weird edge cases where you can harm someone to help other people. You do the most good by picking up massive free wins like donating to effective charities where money does 100x more good than it would if you spent it on yourself.

(Richard Y. Chappell might call this beneficentrism: “the view that promoting the general welfare is deeply important, and should be amongst one’s central life projects.” You can be a beneficentrist without being a utilitarian, but if you’re a utilitarian, you have to be a beneficentrist, and as a utilitarian, being a beneficentrist is much more important than being a “do bad things for the greater good”-ist.)

Posted on Aug 15, 2023

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Mistakes?

The United States is exceptional along many dimensions. Sometimes, people pick two of these dimensions and try to argue that one causes the other. And that’s probably true sometimes. But “the USA is #1 in the world on dimension X, and #1 on dimension Y” isn’t much evidence that X causes Y.

The United States has:

1. the 3rd largest population, and the largest population of any first-world country (by far)
2. the 3rd or 4th largest land area (depending on how you measure USA’s and China’s land)
3. the highest GDP of any country
4. the highest median income, the 8th highest GDP per capita (PPP), and the highest GDP per capita of any large country (the top 7 countries combined have a lower population than California)
5. unusually high income inequality for a developed country
6. the highest healthcare expenditure per capita
7. the highest gun ownership per capita (with double the gun ownership of the #2 country)
8. an unusually high homicide rate for a developed country
9. the most Nobel Prize winners winners (by a huge margin) and most Fields Medalists (narrowly beating France)
10. the highest obesity rate of any large country, and the 11th highest overall
11. the most top universities (whatever that means) by a wide margin
12. unusually low life expectancy for a developed country
13. an unusually high fertility rate for a developed country
14. the 2nd most exports (after China) and the most imports (China is #2)
15. the most military expenditures (by a factor of 3) and the 2nd most nuclear weapons

(Those were just the examples I could come up with in an hour of research.)

(I also looked at a few stats where I thought the USA might be exceptional, but it turned out not to be: IQ, educational attainment, infant mortality¹, and net immigration.)

A lot of these facts are clearly intertwined—the fact that the US has the highest GDP is related to the facts that it has the highest military expenditures, the highest healthcare expenditures, and the 2nd highest exports. (But they’re not fully intertwined, because the US still has high military and healthcare expenditures relative to GDP.)

For other facts, you can come up with narratives as to why they’re related—maybe the high obesity rate causes the low life expectancy, maybe high gun ownership causes the (relatively) high homicide rate. But maybe not. The United States is weird and I don’t have a great handle on why it’s weird (and, as far as I know, nobody else does either). Until someone comes up with a Grand Theory of National Weirdness, I’m reluctant to pick two ways in which the USA is weird and claim one causes the other.

Posted on Aug 15, 2023

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Mistakes?

Notes

TLDR: No.

Confidence: Somewhat likely.

Summary

Some philanthropists discount the future much less than normal people. For philanthropists with low discount rates, does this change how they invest their money? Can they do anything to take advantage of other investors’ high time discounting?

We can answer this question in two different ways.

Should low-discount philanthropists invest differently in theory? No. [More]

Should low-discount philanthropists invest differently in practice? The real world differs from the standard theoretical approach in a few ways. These differences suggest that low-discount philanthropists should favor risky and illiquid investments slightly more than high-discount investors do. But the difference is too small to matter in practice. [More]

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