This post describes my thoughts, at the end of 2024, about using money to make the universe a better place. I remain committed to using at least 10% of what income I earn to do so, and am excited to do more than that when I have the opportunity.

This year marks the tenth anniversary of my first $4,000 donation to GiveWell's top charities! (That donation was 10%

New drugs being developed can be "easy" drugs or "difficult" drugs.

In order to know whether your drug candidate is safe and effective, you're going to test it in a series of clinical trials. In each trial, you'll recruit some number of patients, give each patient the treatment, placebo, or a comparator drug, wait some time, and test them for pre-specified endpoints.

Within that framework, however the trials for different drugs will differ greatly. (And the different phases of a single drug's trials may differ by even more!) Typically, the greatest axes of variation will be:

• Who are your patients?
  • How common is the indication that you're treating? How often do people go to your trial site to get treatment for it? How many of them want to be in a trial?
  • Is your trial taking anyone with the disease? / Is it only

“Why can’t a robot be built without the First Law? What’s so sacred about it?”

Dr. Gerrigel looked startled, then tittered, “Oh, Mr. Baley.”

“Well, what’s the answer?”

“Surely, Mr. Baley, if you even know a little about robotics, you must know the gigantic task involved, both mathematically and electronically, in building a positronic brain.”

“I have an idea,” said Baley. He remembered well his visit to a robot factory once in the way of business. He had seen their library of book-films, long ones, each of which contained the mathematical analysis of a single type of positronic brain. [...] Oh, it was a job, all right. Baley wouldn’t deny that.

Dr. Gerrigel said, “Well, then, you must understand that a design for a new type of positronic brain, even one where only minor innovations are involved, is not the matter of a night’s work. It usually involves the entire research staff of a moderately sized

and some notes on Silicon Valley Bank

A "liquidity" crisis

There's one kind of "bank run" where the story, in stylized terms, starts like this:

• A bank opens up and offers 4%/ann interest on customer deposits.
• 100 people each deposit $75 to the bank.
• The bank uses $7,500 to buy government debt that will pay back $10,000 in five years. Let's call this "$10,000-par of Treasury notes", and call that a 5%/ann interest rate for simplicity. (Normally, government debt pays off a bit every month and then a large amount at the end, but that's just the same thing as having a portfolio of single-payout (or "zero coupon") notes with different sizes and maturity dates, and the single-payout notes are easier to think about, so I'm going to use them

Junk fees are in the news from the 2023 State of the Union address, get picked up by Matt Yglesias, and Zvi responds in Junk Fees, Bundling and Unbundling. Matt and my former colleague propose an economic framing of "bundling versus unbundling", and Zvi identifies four win-win advantages of bundling, and four 'advantages' of unbundling (two win-wins, one (company win)-(customer lose), and one mixed win-lose).

I think Zvi is broadly right on the points he makes, but he and Matt both skip over the basic, conventional econ-101 analysis of bundling goods on prices and customer welfare. I think that, for a broader audience, it's worth covering the "naïve microeconomics" perspective as background for the customer-behavioral story (which, admittedly, is more juicy and fun). Rather than responding to the whole conversation, this post will restrict its focus to the econ-101 microeconomics story of bundling, and ignore the behavioral / political / moral dimensions that Zvi, Matt, and others are discussing.

I'm going

or, Scope-sensitive snafus in summing speculations

(1)

Should I expect monkeypox to be a big deal for the world? Well, fortunately, Metaculus has a pair of questions that ask users to predict how many infections and deaths there will be in 2022:

203 users(!) made 817 predictions of infections, and Metaculus helpfully aggregates those into a "community prediction" of ~248k infections. 77 users made 180 predictions of deaths, with a community prediction of 541.

The y-axis is on a log scale (as are the predictors' distributions). This is a good choice! Whatever you expect the most-likely case to be, there's definitely a chance with things like this that one a misestimation or shift in one factor can make it bigger or smaller by a multiple, not just an additive amount.

What's not a good choice is to report the median outcome of the aggregate position as the "community prediction". This causes a headline