Conventional wisdom says charity cost-effectiveness obeys a power law. To my knowledge, this hypothesis has never been properly tested.¹ So I tested it and it turns out to be true.

(Maybe. Cost-effectiveness might also be log-normally distributed.)

• Cost-effectiveness estimates for global health interventions (from DCP3) fit a power law (a.k.a. Pareto distribution) with \(\alpha = 1.11\). [More]
• Simulations indicate that the true underlying distribution has a thinner tail than the empirically observed distribution. [More]

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Leftist critics of effective altruism like to say this. Well, it’s not true, and I proved it by calculating (an estimate of) the expected utility of a communist revolution. It wasn’t even hard—it took me less than an hour.

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I have some observations and half-baked ideas about my recent donation process. They weren’t important enough to include in the main post, but I want to talk about them anyway.

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Summary

Last updated 2024-11-20.

It’s been a while since I last put serious thought into where to donate. Well I’m putting thought into it this year and I’m changing my mind on some things.

I now put more priority on existential risk (especially AI risk), and less on animal welfare and global priorities research. I believe I previously gave too little consideration to x-risk for emotional reasons, and I’ve managed to reason myself out of those emotions.

Within x-risk:

• AI is the most important source of risk.
• There is a disturbingly high probability that alignment research won’t solve alignment by the time superintelligent AI arrives. Policy work seems more promising.
• Specifically, I am most optimistic about policy advocacy for government regulation to pause/slow down AI development.

In the rest of this post, I will explain:

1. Why I prioritize x-risk over animal-focused longtermist work and global priorities research.
2. Why I prioritize AI policy over AI alignment research.
3. My beliefs about what kinds of policy work are best.

Then I provide a list of organizations working on AI policy and my evaluation of each of them, and where I plan to donate.

Cross-posted to the Effective Altruism Forum.

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Psychologists have done experiments that supposedly show how people behave irrationally. But in some of those experiments, people do behave rationally, and it’s the psychologists’ expectations that are irrational.

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Scott Alexander once wrote:

David Stove once ran a contest to find the Worst Argument In The World, but he awarded the prize to his own entry, and one that shored up his politics to boot. It hardly seems like an objective process.

If he can unilaterally declare a Worst Argument, then so can I.

If those guys can unilaterally declare a Worst Argument, then so can I. I declare the Worst Argument In The World to be this:

“A long time ago, not-A, and also, not-B. Now, A and B. Therefore, A caused B.”

Example: In 1820, pirates were everywhere. Now you hardly ever see pirates, and global temperatures are rising. Therefore, the lack of pirates caused global warming.

(This particular argument was originally made as a joke, but I will give some real examples later.)

Naming fallacies is hard. Maybe we could call this the “two distant points in time fallacy”. For now I’ll just call it the Worst Argument.

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Last updated 2024-10-23.

Outlive: The Science & Art of Longevity by Peter Attia (with Bill Gifford¹) gives Attia’s prescription on how to live longer and stay healthy into old age. In this post, I critically review some of the book’s scientific claims that stood out to me.

This is not a comprehensive review. I didn’t review assertions that I was pretty sure were true (ex: VO2 max improves longevity), or that were hard for me to evaluate (ex: the mechanics of how LDL cholesterol functions in the body), or that I didn’t care about (ex: sleep deprivation impairs one’s ability to identify facial expressions).

First, some general notes:

• I have no expertise on any of the subjects in this post. I evaluated claims by doing shallow readings of relevant scientific literature, especially meta-analyses.
• There is a spectrum between two ways of being wrong: “pop science book pushes a flashy attention-grabbing thesis with little regard for truth” to “careful truth-seeking author isn’t infallible”. Outlive makes it 75% of the way to the latter.
• If I wrote a book that covered this many entirely different scientific fields, I would get a lot more things wrong than Outlive did. (I probably get a lot of things wrong in this post.)
• When making my assessments, I give numeric credences and also use terms such as “true” and “likely true”. The numbers give my all-things-considered subjective credences, and the qualitative terms give my interpretation of the strength of the empirical evidence. For example, if the scientific evidence suggests that a claim is 75% likely and I understand the evidence well, then I rate the claim as “likely true”. If I only read the abstract of a single meta-analysis, and the abstract unequivocally supports the claim but I’m only 75% sure that the meta-analysis can be trusted, then I rate it as “true”. Both claims receive a 75% credence.

Now let’s have a look at some claims from Outlive, broken down into four categories: disease, exercise, nutrition, and sleep.

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Two personal stories:

A story about caffeine

When I first started working a full-time job, I started tracking my daily (subjective) productivity along with a number of variables that I thought might be relevant, like whether I exercised that morning or whether I took caffeine. I couldn’t perceive any differences in productivity based on any of the variables.

After collecting about a year of data, I ran a regression. I found that most variables had no noticeable effect, but caffeine had a huge effect—it increased my subjective productivity by about 20 percentage points, or an extra ~1.5 productive hours per day. Somehow I never noticed this enormous effect. Whatever the opposite of a placebo effect is, that’s what I had: caffeine had a large effect, but I thought it had no effect.

A story about sleep

People always say that exercise helps them sleep better. I thought it didn’t work for me. When I do cardio, even like two hours of cardio, I don’t feel more tired in the evening and I don’t fall asleep (noticeably) faster.

Yesterday, I decided to test this. I wrote a script to predict how long I slept based on how many calories my phone says I burned. The idea is that if I sleep less, that probably means I didn’t need as much because my sleep was higher quality. (I almost always wake up naturally without an alarm.)

Well, turns out exercise does help. For every 500 calories burned (which is about what I burn during a normal cardio session), I sleep 25 minutes less. Once again, exercise had a huge effect, and I thought it didn’t do anything.

I guess I’m not very observant.

Last updated 2024-09-06 to add more content.

You may know that complete proteins are good because they contain every essential amino acid. But you might not know that that’s not the full story.

Take wheat. Wheat is a complete protein—it contains all nine essential amino acids. But it has a problem. Wheat only contains 27mg of lysine (an essential amino acid) per gram of protein, whereas the Food and Agriculture Organization recommends 48mg of lysine per gram. To make full use of a gram of protein, your body needs to get those 48mg. It doesn’t matter that wheat has lots of other essential amino acids. Once your body uses up all the lysine, it can’t make good use of the other amino acids in wheat protein.

You can evaluate the protein quality of a food using the Digestible Indispensable Amino Acid Score (DIAAS). This score determines the quality of a source of protein based on which essential amino acid will run out first, adjusted for digestibility. A score of 100 means the protein has plenty of every essential amino acid.

Sometimes you can improve the protein quality of your food by mixing different ingredients. Wheat has a DIAAS of 57 because it only has 57% as much lysine per gram as your body needs. Peas have a score of 82 because they don’t have enough methionine + cysteine. But peas have 131% of the lysine requirement, and wheat has 149% of methionine + cysteine, so mix them together and they cover for each other’s weaknesses. A 50/50 mixture of wheat and pea protein has a DIAAS of 94.

With this calculator, you can determine the DIAAS for mixtures of different protein sources.

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Sometimes, people call a number a “rounding error” as if to say it doesn’t matter. But a rounding error can still be very important!

Say I’m tracking my weight. If I’ve put on 0.1 pounds since yesterday, that’s a rounding error—my weight fluctuates by 3 pounds on a day-to-day basis, so 0.1 pounds means nothing. But if I continue gaining 0.1 pounds per day, I’ll be obese after 18 months, and by the time I’m 70 I’ll be the fattest person who ever lived.

Or if the stock market moves 1% in a day, that’s a rounding error. If it moves up 1% every day for a year, every individual day of which is a rounding error, it will be up 3700%, which would be the craziest thing that’s ever happened in the history of the global economy.

This happens whenever the standard deviation is much larger than the mean. A large standard deviation means a “real” change gets obscured by random movement. But over enough iterations, the random movements even out and the real changes persist. For example, the stock market has an average daily return of 0.02% and a standard deviation of 0.8%. The standard deviation is 40x larger than the mean, so a real trend in prices gets totally washed out by noise. The market’s daily average return is a rounding error, but it’s still important.