Exotic animal farming

I remember when people were muttering about Covid-19 being all the fault of the weird Chinese and their weird obsession with eating weird animals like pangolins.

So now we have a second version of Covid, that may start a completely novel pandemic, and it comes from the weird Europeans and their weird obsession with wearing the fur of weird animals like minks. Apparently, it was well known that Covid was spreading widely among the minks, but the animals were too valuable to give up on, so they tried to get away with just culling the obviously sick ones. And now we can just hope that they can get the new plague out of Denmark under control before it becomes a second pandemic.

But the people who advocate just giving up on eating and wearing animals are still treated as something between dreamy mystics and lunatics…

Less than zero, part 2

In a long-ago post I wrote about how huge debts don’t make you poor, and illustrated this with the story of real-estate mogul Donald Trump. Negative large fortunes are closer to positive large fortunes than either is to zero. (I later had to correct my interpretation later, on discovering that the counterintuitive behaviour of Trump’s creditors was largely a reflection of their involvement in money laundering.)

Now we learn from the N Y Times that Trump has been paying $750 in federal income tax each year as president. Presumably that’s just an arbitrary number that he made up so that he could say it wasn’t zero. (Apparently even Trump has some limits to his his explicit lying.)

But here’s the thing: $750 is probably worse than $0. People have been assuming he wasn’t paying taxes. It sounds like a general insult. $750 is too specific (as well as being too small). The number becomes a shorthand for his tax-dodging, as well as inviting people to compare their own tax bills to Trump’s.

This demonstrates again how absurdly miserly Donald Trump, above and beyond his criminality. He had to choose an amount to pay purely for the symbolism of possibly needing to tell average Americans how much he had paid. He could certainly have afforded not to choose an amount large enough that even Americans of modest means would find risible. At least four figures…

The opposite of a superficial lie

“The opposite of a fact is a falsehood. But the opposite of a profound truth may very well be another profound truth.”

Niels Bohr

The news media have gotten themselves tangled up, from the beginning of the Trump era, in the epistemological question of whether any statement can objectively be called a lie. Yes, Trump says things that are untrue, that contradict objectively known facts, but are they lies? Does he have the appropriate mens rea to lie, the intention to deceive, or is that just a partisan insult?

The opposite problem has gotten too little attention. Just because Donald Trump says something that corresponds to objective facts, one cannot infer that he is speaking the truth. (We don’t really have a word in English to correspond to the opposite of lie, in this dichotomy.) A good example is the controversy over Trump’s private and public comments on the incipient Coronavirus pandemic in February and March of this year. On February 7, 2020, Trump told Woodward

You just breathe the air and that’s how it’s passed. And so that’s a very tricky one. That’s a very delicate one. It’s also more deadly than even your strenuous flus.

This is quite an accurate statement, and also very different than what he was saying publicly. On February 10 he said, in a campaign speech,

I think the virus is going to be — it’s going to be fine.

And February 26 in an official White House pandemic task force briefing:

The 15 [case count in the U.S.] within a couple of days is going to be down to close to zero. … This is a flu. This is like a flu.

When you see that someone has been saying one thing in public and something completely different in private, it’s natural to interpret the former as lying and the latter as the secret truth — particularly when, as in this case, the private statement is known to be, in fact, true, and the public statement false. And particularly when the speaker later says

I wanted to always play it down. I still like playing it down, because I don’t want to create a panic.

With Trump, though, this interpretation is likely false.

The thing is, while his statement of February 7 was true, he could not have known it was true. No one knew it was true. We can see any number of statements by responsible public-health officials making similar statements at the time. For example, Anthony Fauci on February 19:

Fauci doesn’t want people to worry about coronavirus, the danger of which is “just minuscule.” But he does want them to take precautions against the “influenza outbreak, which is having its second wave.”

“We have more kids dying of flu this year at this time than in the last decade or more,” he said. “At the same time people are worrying about going to a Chinese restaurant. The threat is (we have) a pretty bad influenza season, particularly dangerous for our children.”

And it’s not just Americans under the thumb of Trump. February 6, the day before Trump’s remark to Woodward, the head of the infectious disease clinic at a major Munich hospital, where some of the first German Covid-19 patients were being treated, told the press that “Corona is definitely not more dangerous than influenza,” and criticised the panic that was coming from exaggerated estimates of mortality rates.

Researchers were posting their data and models in real time, but there just wasn’t enough understanding possible then. This is the kind of issue where the secret information that a government has access to is of particularly limited value.

So how are we to interpret Trump’s statements? I think the key is that Trump is not a liar per se, he is a conman and a bullshitter, someone to whom the truth of his statements is completely irrelevant.

In early February he probably did receive a briefing where the possibility that the novel coronavirus was highly lethal and airborne was raised as one possibility, as well as the possibility that it was mild and would disappear on its own. .In talking to elite journalist Bob Woodward he delivered up the most frightening version, not because he believed it was true, but because it seemed most impressive, making him seem like the mighty keeper of dangerous secrets. When talking to the public he said something different, because he had other motives. It’s purely coincidence that what he said in private turned out to be true.

It would be poetic justice of Trump were to be damaged by the bad luck of one time accidentally having told the truth.

Jack and the Beehive

It suddenly struck me that the English word beanstalk and the German word Bienenstock (beehive) sound powerfully like cognates, even though they are not. There are quite a lot of faux amis between English and German, and they are usually cognate, even when the meanings are radically different — as between the English fabric and the German Fabrik (factory), or the English stuff and the German Stoff (fabric). They have a common root, from which they have evolved differently. Even the bizarre Gift meaning “poison” started out as something given, a dose of medicine (dosis also from the Latin root for “given”).

But beanstalk and Bienenstock are both compound words made up of parts that both seem like they could be cognates, but actually are unrelated. That beans and bees are unrelated is unsurprising. It took me a bit of work to convince myself that stalk is etymologically unrelated to Stock, which is indeed cognate to the English stick. The roots are quite different: Stalk from Old English stale, meaning a handle or part of a ladder; Stock originally a branch or a treestump, presumably then a stump that houses bees, either naturally or agriculturally.

The model didn’t fail us, we failed the model

THE ALGORITHM. It’s all anyone can talk about, when they’re talking about universities these days. Illustrative of the unique ability of the current UK government to take a challenging societal problem in hand, and transform it into a flaming chaos that simultaneously exacerbates divisions and satisfies no one.

In this case, it’s about the assignment of marks in A-levels (18 year-olds) released last week, and GCSEs (16-year-olds) still ahead this week. Scotland had its own small version of the fiasco that played out earlier in the week for their own Scottish Higher exams, but the UK government, responsible for English A-levels, managed not only not to learn from the Scottish situation and change course early, it managed to parlay the political challenge into a systemic disaster for higher education that will now roll on for at least the next year or two.

Like any great governing disaster, this one has been years in the making. Pupils doing A levels used to have intermediate exams — AS levels — after the first year of their two-year course, as well as significant amounts of coursework that counted for a substantial portion of their final marks. AS levels were progressively eliminated and coursework reduced over the past decade in England (but not Wales), as the Conservatives seem to have believed that todays pupils were being inappropriately coddled by having too little stress and uncontrollable randomness in their lives, leaving several weeks of exams right at the end of their course as the only determinant of the marks that would decide high-stakes competition for university places. Then they cancelled the exams, in a panicked response to the first wave of Covid-19. Leaving them with nothing.

Weirdly, it’s not as though they don’t have frequent exams during their school (and university) time. But these exams are called “mock exams”, and don’t count for anything in the end.

Which brings us to THE ALGORITHM. How do you assign marks to students when you don’t have any exams? Teachers have quite a lot of information, even if it doesn’t formally count for anything in the regular process. (Weirdly, teachers are regularly expected to produce “predicted grades” based on mock exams, coursework, and general impressions, because the official marks arrive too late for university admissions.) But on average they tend to be overly optimistic — or, one might also say, either generous or strategic, since the university admission offer that results from an overpredicted A-level grade is not necessarily withdrawn when the exam result exceeds it, whereas the university place that is lost from an underprediction is almost impossible to make good.

If you were a mindless machine-learning bot trying to optimise the accuracy of prediction of missing marks in an overall minimum-mean-error way, you would take data about each student’s family income, ethnicity, sex, parents’ occupations, and region, all of which are likely to be correlated to exam scores. But that would seem outrageously biased: Why should the young person with wealthy parents get higher A-level marks than the one with poor parents, after they had the same mock exam grades? The machine-learning answer is, because that’s what’s happened with real grades in the past. The wealthy family is likely to provide more support, maybe tutors, a more stable environment for studying for the exams. The child of the poor family may have been working hard since year 12, but there’s a much higher chance that the family would have had a crisis — maybe a parent losing a job, illness, homelessness — that would have distracted from exam preparation and led to underperformance at the exam. And since that might have happened in reality, that needs to be reflected in our optimal prediction algorithm.

But that looks bad, so the Ofqual boffins used past school performance as a proxy. Effectively, they said that each school gets the same marks this year that they got last year. Teacher evaluations were used to rank the students in each subject, to decide which students get the school’s quota of A*’s, etc. If you made the bad life choice to go to a low-performing school where no one in living memory has scored better than B in chemistry, then B is the ceiling for your marks, no matter what scores you may personally have been achieving on your mockeries.

Averaged over the whole population of English students your misfortune is just a small blemish on an overall excellent prediction.

It’s a good illustration of the problems of ethical machine learning. People say, if you don’t want your algorithm to be biased based on gender, don’t include gender information in the dataset. But if you instead include height information, say, the algorithm will learn all the gender bias in the training set and assign it to the height variable.

Just to rub salt in the wounds, there was an extra fillip for students in small — heavily private — schools: Since average performance fluctuates more in small groups, courses with 15 or fewer students had their (generally higher) teacher predictions more heavily weighted in their final marks, and those with 5 or fewer received their teacher predictions unfiltered.

Now, this way of using past school performance seems… surprising, to those of us who have been involved in UK university admissions in the past, given the extent of government and public outrage every year when the elite universities once again draw their intake from a very small sliver of UK secondary schools, predominantly private schools. You might think that this outrage reflects a belief that the differences in average exam performance, that drive most of the differential in university admissions, are unfair, that they do not accurately represent student ability, performance, and potential. If you believed that, you might propose a very different way of using school performance to assign marks, namely: Every school gets the same proportion of A*, A, B, etc., to be allocated within each subject according to the teacher rankings. I’m not advocating this method, but it is no more extreme, in its own way, than the application of past school performance that was actually implemented.

To the extent that A-level marks are primarily a tool for sorting graduates for university admissions, this would function somewhat similarly to the practice of some US states, of guaranteeing admission to their state universities to a certain percentile of every high school in the state. This leverages housing and school segregation to benefit equality, as opposed to the opposite.

The fact that my algorithm seems obviously unfair to individuals, while the other algorithm was seen as not only credible but actually self-evident, reflects nothing but naked ideology about the nature of class.

Education minister (a position whose relationship to that of education secretary confuses me) Nick Gibb responded to the fiasco thus:

So the model itself was fair, it was very popular, it was widely consulted upon. The problem arose in the way in which the three phases of the application of that model – the historic data of the school, the prior attainment of the cohort of pupils at the school, and then the national standard correction – it’s that element of the application of the model that I think there is a concern.”

The minister went on: “The application of the model is a regulatory approach and it’s the development of that that emerged on the Thursday when the algorithm was published. And at that stage it became clear that there were some results that were being published on Thursday and Friday that were just not right and they were not what the model had intended.”

The poor misunderstood beast. It meant well…

Early Trumpist medical treatments

And then I see the disinfectant, where it knocks it out in a minute. One minute! And is there a way we can do something like that, by injection inside or almost a cleaning. Because you see it gets in the lungs and it does a tremendous number on the lungs. So it would be interesting to check that.

When Donald Trump used a Covid-19 press briefing to recommend injecting disinfectants to kill viruses within the human body, people reacted as though this were entirely unprecedented. But it wasn’t, entirely. From Frank Snowden’s Epidemics and Society:

Of all nineteenth-century treatments for epidemic cholera, however, perhaps the most painful was the acid enema, which physicians administered in the 1880s in a burst of excessive optimism after Robert Koch’s discovery of V. cholerae. Optimistic doctors reasoned that since they at last knew what the enemy was and where it was lodged in the body, and since they also understood that bacteria are vulnerable to acid, as Lister had demonstrated, all they needed to destroy the invader and restore patients’ health was to suffuse their bowels with carbolic acid. Even though neither Koch nor Lister ever sanctioned such a procedure, some of their Italian followers nevertheless attempted this treatment during the epidemic of 1884–1885. The acid enema was an experimental intervention that, in their view, followed the logic of Koch’s discoveries and Lister’s practice. The results, however, were maximally discouraging…

Apparently it’s a not uncommon response on someone first learning of the germ theory of disease.

Count no statue happy…

Count no man happy till he dies.

Sophocles, Oedipus the King (trans. Robert Fagles)

Must no one at all, then, be called happy while he lives; must we, as Solon says, see the end? Even if we are to lay down this doctrine, is it also the case that a man is happy when he is dead? […] for both evil and good are thought to exist for a dead man, as much as for one who is alive but not aware of them; e.g. honours and dishonours and the good or bad fortunes of children and in general of descendants.

Aristotle, Nichomachean Ethics, Book 1 (trans. W D Ross)

In all of the discussion of racist statues one fundamental point is rarely mentioned: Above all, public statues represent the unwillingness of “great men” to simply go away. Those who bestrode their narrow world like a Colossus are loath to let death remove them from the scene, so like the stuffed dodo in a diorama they have their effigies propped up in the public square.

While they lived they received the adulation of the crowds, and the opprobrium of their opponents. If the great one’s supporters need a public icon as a focus for their devotions, the icon will have to continue to participate in the hurly-burly of public life, including the scrutiny of their lives and deeds brought on by shifting ethical standards. If Winston Churchill were alive today he would rightly have paint and rotten tomatoes flung at him by those appalled at his racist ideas and actions. Reasonable can believe that his near-genocidal actions in Bengal, among others places inhabited by darker-skinned people, are more significant than a few well-crafted speeches that bucked up the spirits of the Island Race. Reasonable people did think so during his life. The place where one is beyond praise or blame is called the grave, and no one is suggesting disinterring WC’s bones — though an earlier generation of Tories did exactly that with Oliver Cromwell, after the tide of history turned against him.

His supporters are welcome to hide his statues away in private shrines, or public museums. If you put them up in public you have to accept that people are going to continue to engage with them. Sometimes angrily. Sometimes disorderly.

Vaccine probabilities

From an article on the vaccine being developed by Robin Shattock’s group at Imperial College:

The success rate of vaccines at this stage of development is 10%, Shattock says, and there are already probably 10 vaccines in clinical trials, “so that means we will definitely have one”

It could be an exercise for a probability course:

  1. Suppose there are exactly 10 vaccines in this stage of development. What is the probability that one will succeed?
  2. Interpret “probably 10 vaccines” to mean that the number of vaccines in clinical trials is Poisson distributed with parameter 10. What is the probability that one will succeed?