Icosian Reflections

…a tendency to systematize and a keen sense

that we live in a broken world.

IN  WHICH Ross Rheingans-Yoo—a sometime economist, trader, artist, expat, poet, EA, and programmer—writes on things of int­erest.

Poker is a bad game for teaching epistemics. Figgie is a better one.

Some trading firms are driven by good decisions made by humans. (Some aren't, but we can set those aside. This post is about the ones that are.) Humans don't make better-than-average-quality decisions by default, so the better class of intellectually-driven quantitative trading firm realizes that they are in the business of training humans to make better decisions. (The second-best class of firm contents themselves with merely selecting talent.) Some firms, famously, use poker to teach traders about decision making under uncertainty.

First, the case for poker-as-educational-tool: You have to make decisions. (Goodbye, Candy Land.) You have to make them under uncertainty. (Goodbye, chess.) If you want to win against smart competition, you have to reverse-engineer the state of your competitors' uncertainty from their decisions, in order to make better decisions yourself. (Goodbye, blackjack.)

It's the last of these that is the rarest among games. In Camel Up -- which is a great game for sharpening certain skills -- you place bets and make trades on the outcome of a

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Asimov on building robots without the First Law

From Caves of Steel, pp. 160-161 in my version.

“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

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“Liquidity” vs “solvency” in bank runs

and some notes on Silicon Valley Bank

Originally posted to LessWrong.

epistemic status: Reference post, then some evidenced speculation about emerging current events (as of 2023-03-12 morning).

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
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(Naïve) microeconomics of bundling goods

Originally posted to LessWrong.

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

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Metaculus and medians

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:

metaculus_mpx_infections_1

metaculus_mpx_deaths_1

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

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The FDA on Fluvoxamine, round 1

Yesterday, the US FDA responded to a submission for Emergency Use Authorization (that was submitted 146 days earlier) for use of fluvoxamine "for the outpatient treatment of adults 24 years and older...to prevent progression to severe COVID-19 and/or hospitalization" (p. 2).

The FDA's conclusion is:

Due to limitations in the available clinical study results for fluvoxamine in the proposed patient population, lack of compelling in vitro and in vivo data to support the proposed [mechanism of action] of fluvoxamine for the treatment of mild COVID-19 disease, and context of increasingly available therapies with well-characterized [mechanisms of action] and consistent efficacy results in nonhospitalized patients, the FDA cannot reasonably conclude that fluvoxamine may be effective for the treatment of COVID-19. As such, FDA has determined that the criteria for issuance of an EUA are not met at this time.

While the FDA has concluded that the existing clinical data are insufficient to support the issuance of an EUA, these data suggest that further clinical investigation may

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