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.

Age and Covid-19 IFR in Africa

nb: This analysis has not been updated to reflect the realized results of the pandemic after publication.


I replicated my estimation of population-average IFR for Africa-ex-South-Africa (henceforth "Africa"), using the same methodology as my India calculations. Africa is significantly demographically younger than either India or the US -- the top quintile of age in Africa starts at 39, India at 49, and the US at 61.

I estimate that the age effect creates an Africa population-average IFR 20% that of the US rate (i.e., a US rate 4.94× greater), assuming age-uniform infection rates and no difference in medical care. This effect is driven by the reduced population share of age>70 in Africa (just 18% that of the US).

My work is here, and here's the primary chart:

In my India analysis, I wrote:

The effect of medical care differences on IFR-by-age curves is of first-order importance to this analysis; as an example, if lack of care were

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Age and Covid-19 IFR in India

nb: This analysis has not been updated to reflect the realized results of the pandemic after publication.


Abstract:

India's demographic average age is younger than that of the US. This implies that the strongly age-varying Covid-19 infection fatality rate (IFR) could cause a lower population-average IFR in India than in an older nation such as the US, all other factors being equal (spoiler: they're not).

I estimate that the age effect creates an India population-average IFR 39% that of the US rate (i.e., a US rate \(2.58\times\) greater), assuming age-uniform infection rates and no difference in medical care. This effect is driven by the reduced population share of age>70 in India (just 35% that of the US).

I do not attempt to model age-varying infection rates (which I expect would slightly decrease India fatality rates relative to the US), do not attempt to model selection pressure on patients' immune systems (which I expect would make India fatality rates modestly lower), and do not model environmental

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Which vaccine?

I wrote in January about vaccines and public health, and I wanted to retract my bottom-line recommendation about which vaccine to get -- if you have a choice -- in Hong Kong. Appointments opened to residents 16+ yesterday, so this post is coming a bit late, but oh well. Here we are.

If you're in Hong Kong and have choices, my personal recommandation is that you get an appointment for the BioNTech (Pfizer) vaccine as soon as possible. (If you are in Hong Kong and have a HKID, the link to book a vaccine in English is here -- click the red "Book Vaccination" box at the left.)

In the rest of this post, I'll describe how my thinking has changed on the argument I expressed in my January post.


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When I wrote in January, I was looking at a massive shortfall in vaccine demand in the US and assuming that it couldn't happen here in Hong Kong. In hindsight, I was extremely wrong.

In the first

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How proper scoring rules are like order books

Intended audience: my own later reference. Might be useful for traders thinking about general scoring rules and their constraints


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The following proposition is paraphrased from Savage 1971 (pdf), the original paper that introduced proper scoring rules way back when. Savage characterized the space of all possible scoring rules two ways -- in the now-common form using a general convex function and its subgradient, and in the equivalent "schedule of demands" form that has a natural interpretation as being given the opportunity to trade into a limit order book.

Proposition 1. Any (strictly) proper scoring rule \(S(p,q)\) can be written as \(S(p,q):=\alpha+S^*(p,q)\), where \(\alpha\in\mathbb R\) is a constant and \(S^*(p,q)\) is the profits-or-losses from trading into a limit order book (with nonzero size available at each price) when your initial position is some \(\beta\), your fair value is \(p\), and the contract value resolves to \(q\).

Proof. Let \(\phi:[0,1]\to\mathbb R\) be a

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The Times on EU Vaccines, 2021-03-01

Zvi Mowshowitz's new policy is not to link to the New York Times, and he's willing to entertain the policy of not linking to NYT reporters' Twitters (though hasn't pulled the trigger yet). I understand where he's coming from -- Cade Metz's piece on Scott Alexander was really, really not good.

Scott Aaronson has a numbered list of 14 theses issues and won't talk with Cade Metz, even to explain quantum complexity, without a full explanation on how the piece on Slate Star Codex happened. Also understandable; the article really was quite bad.

Then there's social pressure going around not to read the Times. I think this is a mistake. It is important to understand what rhetoric the paper chooses to use, for the same reason that it's important to occasionally look at what's happening on the other side of a chessboard. I wouldn't claim it's in the top-5 most important things to read to understand the world (or even the top 10), but I believe it's part of

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Metaculus has some issues

In Zvi's 2/11 Covid update, he turned to Metaculus for help. He looked at the numbers. Becase the man is an inveterate trader, he saw odds that were Wrong On The Internet and just couldn't stop himself from creating an account to bet against it. And then he saw the payout structure and decided he was done after making a single prediction.

I spent some time with the Metaculus site and figured out how they borked this one up enough to drive away Zvi Mowshowitz. I'll try to explain it here.


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Here's a presumably current description of the scoring function that I found on the FAQ, slightly abridged:

Your score \(S(T;f)\) at any given time \(T\) is the sum of an "absolute" component and a "relative" component: \[S(T;f)=a(N)\times L(p;f)+b(N)\times B(p;f),\] where \(N\) is the number of predictors on the question.

If we define \(f=1\) for a positive resolution of the question and \(f=0\) for a negative resolution, then \(L(p;f)=\log_2(p/0.5)\) for \(f=1\) and \(L(p;f)=\log_2((1−p)/0.5)\) for

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