Astral Codex Ten

Beijing

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Beijing

Article

Beijing is a recurring place in the Astral Codex Ten archive, appearing 8 times across 8 issues between April 06, 2022 and April 01, 2026. The archive places it in contexts such as “Tsinghua and Peking Universities, both in Beijing”; “BEIJING, CHINA”; “Putin closer to Beijing”. It most often appears alongside China, Europe, Shanghai.

Metadata

  • Category: Places
  • Mention count: 8
  • Issue count: 8
  • First seen: April 06, 2022
  • Last seen: April 01, 2026

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Dictator Book Club: Xi Jinping
April 06, 2022 · Original source
Tsinghua and Peking Universities, both in Beijing, are the Harvard and Yale of China. In 2002, Tsinghua administrators were sad that their graduates didn’t get as many top posts in government as Peking grads did. They started a program where Tsinghua grads interned with, networked with, and supported other Tsinghua grads. It was a really good program. In a country where everything works on informal patron-client relationships anyway, it provided a helpful nucleus of “have you considered making your patron-client relationships based around going to the same university?”
If the paper is to be believed, all of this really worked. Before Tsinghua started its push, it was usually equal to or behind Peking in officials produced. Now in one supposedly-representative set of 38 high officials, the score is Tsinghua 11, Peking 1.
Spring Meetups In Seventy Cities
April 10, 2022 · Original source
BEIJING, CHINA Contact: Karen (caoyy19@mails.tsinghua.edu.cn) Date: April 24 Time: 3:00 PM Coordinates: https://plus.codes/8PFRWCH5+46 Location: 大酉·M Coffee，美术馆后街77号文创美术馆内
Inline links: https://plus.codes/8PFRWCH5+46
Your Book Review: Public Choice Theory And The Illusion Of Grand Strategy
June 24, 2022 · Original source
It’s all a competition to see who can signal “I hate Putin” the most, but Germany was still shutting down all its nuclear power plants to rely on Russian gas despite warnings from every other EU state (Russia accounts for 40% of Europe’s gas imports) — so much for grand strategy. That is not to excuse Putin’s invasion (he is, after all, the aggressor) and no, Ukraine is not “the West’s fault” as Mearsheimer has claimed in his viral lecture, but “NATO’s door remains open” for me and “we're going to start WW3 because you're in my sphere of influence” for thee is no grand strategy at all. Indeed, the irrational Western response is not predictable by the unitary actor model, but by the public choice model. Hanania writes: If you were going to cut Russia off from SWIFT, for example, why wouldn’t you announce it beforehand? The whole point of a punishment like that is supposed to be its deterrent effect, but if you don’t communicate that a specific action will happen, then it can’t influence behaviour. The answer here seems to be a lack of grand strategy, with leaders responding to events according to emotion and public relations more than anything. Cutting off SWIFT, or even threatening to do so, seems extreme before an invasion occurs, but not after it has begun. The West cannot rely on sanctions to make Russia abandon its core national security interests, which at the very least include a no-NATO commitment, the acceptance of the secession of Donetsk and Luhansk, and the recognition of the annexation of Crimea. Sanctions will also push Putin closer to Beijing, and the US will continue down the self-defeating path of alienating both of the other two superpowers — so much for American grand strategy. Hanania writes: Even if Putin has maximalist aims at this point, that doesn’t mean sanctions are worth doing. Their costs are high and they may have major consequences for the global economy. One has to consider the possibility that they make Russia more repressive at home and more brutal in its persecution of the war. Putin is getting sanctioned, but ordinary Russians are getting cancelled. The Metropolitan Opera of New York has announced it will no longer stage performers who have supported Russian President Vladimir Putin. Carnegie Hall has done the same, and the Royal Opera House in London is cancelling a planned Bolshoi Ballet residency (one of the oldest and most prestigious ballet companies in the world). Eurovision banned Russia. Tchaikovsky is cancelled. As Tyler Cowen writes, cancel culture against Russians is the new McCarthyism. The culture war has morphed into a hyperreal form on the Internet. Just as COVID is the first pandemic in the Age of Twitter, so the Ukraine invasion is, in some sense, the first war in the Age of Twitter. As it unfolds, we are seeing many disturbing parallels to the events of early 2020. People are rapidly normalising once-fringe ideas like a NATO-enforced no-fly zone (while completely oblivious to the fact that it means shooting down Russian planes and causing WW3), direct US conflict with Russia, regime change in Moscow, and even, incredibly, the use of nuclear weapons. The overnight flips on German defence spending and SWIFT are like the overturning of conventional public health policies on masking and lockdowns. We have entered the age of shitpost diplomacy, as coined by Tanner Green, in which the official Twitter account of the US Embassy in Kiev literally posts memes to spite Putin: A Russian sixth-grader could explain why celebrating the glories of Kievan Rus does not subvert Putin’s claims about the history of the Russian nation so much as reinforce them. Just like Hong Kong’s protests, Ukraine has won the meme war with utterly lopsided propaganda and unanimous international support on the Internet. As Yoshimi writes: Floating ghostlike above it is our war, the myth of the ‘Ghost of Kyiv’, ace MIG-29 pilot who has apparently shot down six Russian planes, or the legend of the Ukrainian soldiers defending an island outpost who replied “Russian warship go fuck yourselves” to a surrender offer and may or may not have died heroically, or two Russian II-76 transport aircraft that maybe were shot down near Kiev, or videos of air strikes or dead bodies which variously are Russian or Ukrainian until they turn out to be from Gaza six years ago, or the viral video of an old Ukrainian woman telling off a Russian soldier by offering him sunflower seeds so when he dies, sunflowers (Ukraine’s national flowers) will sprout from the soil. We’re raising funds for the Ukrainian army on crowdfunding apps and giving advice to the civilians being handed assault weapons about how to disable tanks, sharing weird homophobic pictures of Putin as a gay icon and spamming Russian government posts. Ukrainian president Volodymyr Zelensky has made the decision to stay and fight rather than flee like most would-be leaders who go all in for American foreign policy, and now is being deified by us as “badass”, “a true leader”, etc. etc., alongside his people, whose resistance to authoritarianism we are told is unparalleled in the modern world. After all, so it goes, who could be next? And like in Hong Kong, despite winning the culture war in hyperreality, the actual war in reality is won by the side with overwhelming military might, not morality. The real war is where Ukrainians are experiencing the genuine life-shattering effects of military conflict. It matters because this is the first time Western response is driven by Twitter outcry, and it will not be the last. A New EA Cause? Besides Hanania’s recommendations in the last section (which he admits are more or less impossible in an excellent interview with Caplan), a worthy EA priority might be to somehow turn the public tide on sanctions, which literally kill more people than Putin. Americans should be appalled by the atrocity committed in their names. The banality of the incompetence of foreign policy elites does not excuse their evil. With how entrenched the special interests are, I have no idea if it’s even worth trying, but at the very least the sheer amount of suffering and death from sanctions should be made common knowledge. Nuclear security is one of the top priorities in Effective Altruism, per 80,000 Hours, Future of Life Institute, and Our World In Data. Toby Orb, who wrote the definitive book on existential risk, The Precipice, estimates x-risk from nuclear war to be ~1 in 1000 in the next century. Luisa Rodriguez estimates a 1.1% chance of nuclear war each year and that the chances of a US-Russia nuclear war may be in the ballpark of 0.38% per year; summarised by Max Roser as: Nuclear risk is neglected by the public because of Pax Americana since the collapse of the USSR, and is not discussed as often in EA as it’s thought to be relatively well-funded and mainstream, but in fact major donors like the MacArthur Foundation have been withdrawing funding. As Joan Rohling details in an 80,000 Hours podcast there is much to be done, especially when Ukraine gave up their nuclear arsenal in 1994 in exchange for Russia’s promise to never threaten or use military force against them. A worthwhile adjacent cause area might be de-escalation of public outcry to reduce x-risk from nuclear war beyond just regular anti-proliferation efforts — even a Russian specialist from the RAND Corporation is surprised by how much public outrage is driving policy: Even just the pace of the sanctions: we went to 11 out of 10 in like two days — farther than many expected we’d ever get in short order. And I think the same is true about these military assistance initiatives. We’re just trying to do something because there’s a public demand for action. So that’s what worries me, that the sort of public outrage that’s being channeled in Western democracies through political systems could result in decisions that prove ultimately unwise. Despite how odd it is that some wars are “legal” while others aren’t, we should be glad UNSC exists as much as everyone laughs at how useless the rest of the UN is. All is fair in love and war, but international norms is all that stands between us and nuclear annihilation. It is hard to emphasise just how delusional it is for the public to fixate on no-fly zones — I, like Scott, am surprised we’re still capable of jingoism. 80,000 Hours has updated their top career recommendations to include China specialist to improve China-Western coordination on global catastrophic risk, which seems more important after reading how irrational and captured the American foreign policy apparatus is. As Hanania writes, “great power competition” is an anachronism. If Ukraine is the first war warped by hyperreality, it won’t be the last. Now that US foreign policy elites have driven Putin into the arms of China, let’s hope IR specialists can imbibe the public choice model instead of antagonising yet another nuclear rival. Public Choice Theory and the Illusion of Grand Strategy is an important work because it raises the sanity waterline, which at the least should make us stop killing millions for no reason, and at the most should make the human race more knowledgeable of how to prevent total extinction from nuclear armageddon. Pax Americana is dead, but a multipolar world will be more humane. Endnotes In the fiscal year 2018, the top five government contractors were all weapons manufacturers, with Lockheed Martin in first place at $40.6 billion. The Department of Defence spent $358 billion on contracting, ten times higher than second place Department of Energy. Collective action problems that stop a bunch of smaller companies from effectively influencing policy are no hindrance for companies like Lockheed Martin.
Inline links: not, viral lecture, NATO’s door remains open, we're going to start WW3 because you're in my sphere of influence, writes, writes, cancelled, banned, cancelled, writes,, German defence spending, shitpost diplomacy, posts, https://substackcdn.com/image/fetch/$s_!j4gR!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F3c5db00c-e420-4b54-b826-b85d9ddf186d_900x647.png, writes, interview, 80,000 Hours, Future of Life Institute, Our World In Data, The Precipice, ~1 in 1000, estimates, summarised, https://substackcdn.com/image/fetch/$s_!PRc2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fe90b1bb5-d84b-45dc-b9fa-2edddbf6b9fa_1600x1006.png, Pax Americana, MacArthur Foundation, details, gave up, surprised, Scott, jingoism, China specialist, captured, writes, the sanity waterline, a multipolar world will be more humane
Your Book Review: 1587, A Year Of No Significance
August 19, 2022 · Original source
It’s challenging to represent Chinese words in alphabetical form; the Wade-Giles and Pinyin approaches are the two main methods. This book, from 1981, uses the older Wade-Giles system. On the other hand, a nice benefit of this book's age is that it remains blessedly uncontaminated by any current “culture war” toxicity. Many of the main characters have Wikipedia pages under the newer Pinyin versions of their names, which I'll link. I’ll also include the Pinyin version in parentheses where the spelling of the Wade-Giles version is significantly different, like this: Peking (Beijing).
For about 500 years the Forbidden City, surrounded by a moat and located inside the much larger city of Peking (Beijing), was the nucleus of the Chinese Empire. It had been created several generations earlier at the behest of the Ming dynasty’s third emperor (Wan-li was the 14th). This giant, rambling palace outlived the dynasty which created it, and went on to serve as the headquarters of the subsequent and final Ching (Qing) dynasty as well.
“block printing was in wide public use… anonymous pamphlets and pseudonymous literature appeared in Peking to stir up and intensify the controversy”.
Links For April 2023
April 20, 2023 · Original source
3: The planned Beijing → Taipei high-speed rail corridor. I can spot at least two problems with this idea.
Inline links: The planned Beijing → Taipei high-speed rail corridor.
Practically-A-Book Review: Rootclaim $100,000 Lab Leak Debate
March 28, 2024 · Original source
Lineage A (left) was used by the Minoan Cretans, but has never been deciphered. Lineage B (right) was used by the Mycaeneans for lists of palace goods. This matches Saar’s story above. The lab leaked to somewhere else in Wuhan, not the wet market. The virus spread undetected in the population for a while. During this time, it mutated to Lineage B. Then one of the people with Lineage B went to the wet market and started a superspreader event. The authorities sampled the patients, found Lineage B, then started looking elsewhere. Later they detected some of the earlier Lineage A cases. The market is unlikely to be the origin of the pandemic, because the original Lineage A strain wasn’t found there. Peter: Although Lineage A is evolutionarily older, Lineage B started spreading in humans first. We know this because Lineage B is more common. Throughout the early pandemic, until the D614G variant drove all other strains extinct, a consistent 2/3 of the cases were B, compared to 1/3 A. Both strains spread at the same rate, so the best explanation is that B started earlier than A. Since COVID doubles every 3-4 days, probably Lineage B started 3-4 days earlier than Lineage A, which explains why it’s always been twice as many cases. But also, Lineage B also has more internal genetic diversity than Lineage A. In general, older viruses have more genetic diversity (the “molecular clock”). This is further evidence that B started spreading first. Pekar 2022 and Pipes 2021 do analyses with known parameters for spread rate and diversity, and find 90%+ odds that Lineage B was the first one in humans. Why did the older strain start spreading later? Probably the virus crossed from bats into raccoon-dogs on some raccoon-dog farm out in the country. It spread in the raccoon-dogs for a while, racking up mutations, including the (less mutated) Lineage A strain and the (slightly more mutated) Lineage B strain. Then several raccoon-dogs were taken to Wuhan for sale, including one with Lineage A and another with Lineage B. The one with Lineage B passed its virus to humans earlier. Then 3-4 days later, the Lineage A one passed its virus to humans. Lineage A was first found in a Wuhan neighborhood right next to the wet market (closer to the wet market than 97% of Wuhan’s population). Again, it would be a bizarre coincidence if a lab leak pandemic was first detected at a wet market. But it would be an even more bizarre coincidence if a lab leak pandemic separated into two strains, and both were first detected at a wet market! Although no known wet market cases were Lineage A, a positive Lineage A environmental sample was found at the wet market, and everyone agrees most cases went undetected. So maybe the Lineage B raccoon-dog spread its virus to a vendor, and that sub-strain mostly stayed in the market. But the Lineage A raccoon-dog spread its virus to a customer, who went back to his house nearby, and that strain spread in the neighborhoods next to the market. This is the only story that explains the evolutionary precedence of A, the greater spread and older molecular clock of B, and the fact that both strains were first found very close to the wet market. Yuri/Saar: Lineage B could be more common and diverse because it got the advantage of a super-spreader event in the wet market. There are a few scattered cases of intermediates between A and B, and a few other scattered cases of lineages that seem even more ancestral (ie closer to the bat virus) than either. This doesn’t make sense in a double spillover hypothesis. But it does make sense if the lineages separated in human transmission somewhere between the lab and the first super-spreader event at the wet market. Peter: Again, the wet market wasn’t a super-spreader event. COVID spread in the wet market at exactly its normal spread rate, doubling about once every 3.5 days. Stop calling the wet market a super-spreader event. The scattered cases of “intermediates” are sequencing errors. They were all found by the same computer software, which “autofills” unsequenced bases in a genome to the most plausible guess. Because Lineage B was already in the software, depending on which part of a Lineage A virus you sequenced, you might get one half or the other autofilled as Lineage B, which looked like an “intermediate”. We know this because all the supposed “intermediates” were partial cases sequenced by this particular software. We can confirm this by noting that there are too many intermediates! That is, where Lineage A is (T/C) and Lineage B is (C/T), the software found both (T/T) “intermediates” and (C/C) “intermediates”. But obviously there can only be one real intermediate form, and we have to dismiss one or the other. But in fact we can dismiss both, because they were both caused by the same software bug. The scattered “progenitor” cases - those closer to the ancestral bat virus than either A or B - are reversions, ie cases where a new mutation in the virus happened to hit an already-mutated base and shift it back towards the ancestral virus. We know this because all of these “progenitors” were scattered cases found months after the pandemic started, often in entirely different countries from Wuhan. If these were real progenitor viruses, they would have either fizzled out or exploded into a substantial portion of all cases, not be found one time in one guy in Malaysia. Given the number of mutations the virus developed over the course of the pandemic, it’s inevitable that some of them would be mutations that bring it closer to the original bat virus, and in fact we find the number of “progenitors” found very nicely matches the number of progenitor-appearing viruses we would expect by chance. And in many cases, we know the “progenitors” are newer than the original lineages, because they also have some of the later mutations that Lineage A or B picked up along the way, alongside their apparent ancestral-bat-virus-like mutations. Session 2: Viral Genetics Yuri: Two years before COVID, scientists at the Wuhan Institute of Virology, together with colleagues at the University of North Carolina, sent in a grant proposal for the DEFUSE program. This program, intended to locate and better understand potential future pandemic viruses, involved going into bat caves and collecting new coronaviruses. Once they had them, they would do gain-of-function: specifically, they would add a furin cleavage site to make them more infectious and see what happened. (quick interlude: COVID’s spike protein has two sections: one binds to human cells through the ACE2 receptor, the other helps fuse with the cell after binding. In order to avoid the immune system, it hides both of these into one spike. But when it reaches a cell, it needs to separate them again. It takes advantage of a human respiratory enzyme, furin, to do the separation - this also ensures that it only infects its primary target, human respiratory cells. The part of COVID that lets it get separated by furin is called the “furin cleavage site”. COVID’s bat-virus ancestors were gastrointestinal viruses; the addition of a furin cleavage site was what made them respiratory viruses.) We’ve found two close relatives of COVID: bat viruses called RATG-13 and BANAL-52. In particular, COVID looks more or less like BANAL-52 plus a furin cleavage site. There are 1500 sarbecoviruses, members of the family of viruses that includes SARS and SARS2/COVID. None of them except COVID have furin cleavage sites. BANAL-52, COVID’s closest ancestor, doesn’t even have anything resembling one that could mutate into a functional furin cleavage site like COVID’s. Instead, COVID - which mostly just resembles BANAL-52 with a few scattered single-point mutations - has twelve completely new nucleotides in a row - a fully formed furin cleavage site that came out of nowhere. There is nowhere else in the genome that COVID differs from BANAL-52 in such a profound way. It’s just BANAL-52 plus a little bit of random mutation plus a fully-formed furin cleavage site that came out of nowhere. Further, the furin cleavage site is weird. It uses the protein arginine twice. But instead of the nucleotides coding for arginine in the usual viral way, both times it uses the codons CGG - the way that higher animals code for arginine. This works fine - it’s just not how viruses do it. So the obvious conclusion is that WIV, which said in 2018 that it was going to find viruses and add furin cleavage sites to them, found a close relative of BANAL-52 and added a furin cleavage site. Since they were humans, and most familiar with the human way of encoding arginine, they added it as CGG both times. COVID seemed surprisingly optimized for infecting humans. Of fifty animals it was tested in, including the usual coronavirus intermediate hosts (pangolins, raccoon-dogs, etc), it was best at infecting human cells. Further, a virus that enters a new species will usually show a burst of mutations as it “figures out” the best way to adapt to that species’ unique biology. But COVID has had a pretty constant mutation rate in humans, from the beginning of the pandemic to the end. That suggests it was already adapted to humans. This could be because the lab screened for viruses with existing adaptations, because they passed it through humanized mice in the lab, or because it adapted in the hundreds of undetected cases that happened between the lab and detection in the wet market. Usually, research with potentially dangerous coronaviruses is done in BSL-3 or 4, ie high to very-high security. But WIV was irresponsibly doing it in BSL-2, ie medium security. The researchers weren’t even required to wear masks. In general, about 1/500 labs will leak any given pathogen they’re working on (?!). But because WIV was researching such an infectious virus in such an irresponsible way, the odds of a leak were much higher. The most likely explanation for all these facts is that WIV went ahead and did the gain-of-function research they said they were going to do (the particular DEFUSE grant proposal we know about got rejected, but it proves that Wuhan wanted to do this, and they could easily have gotten funding somewhere else, or done it out of their regular budget). They found a close relative of BANAL-52 and added a furin cleavage site as a simple twelve-nucleotide insertion, using the human method of encoding arginine that their genetic engineers were familiar with. Then it leaked, spread for a while in the general Wuhan population, and eventually made it to the wet market where it got detected. Peter: As mentioned earlier, the DEFUSE grant was rejected. Further, the grant said that the Wuhan Institute of Virology was responsible for finding the viruses, and the University of North Carolina would do all the gain-of-function research. This was a reasonable division of labor, since UNC was actually good at gain-of-function research, and WIV mostly wasn’t. They had done a few very simple gain-of-function projects before, but weren’t really set up for this particular proposal and were happy to leave it for their American colleagues. Even if WIV did try to create COVID, they couldn’t have. As Yuri said, COVID looks like BANAL-52 plus a furin cleavage site. But WIV didn’t have BANAL-52. It wasn’t discovered until after the COVID pandemic started, when scientists scoured the area for potential COVID relatives. WIV had a more distant COVID relative, RATG-13. But you can’t create COVID from RATG-13; they’re too different. You would need BANAL-52, or some as-yet-undiscovered extremely close relative. WIV had neither. Are we sure they had neither? Yes. Remember, WIV’s whole job was looking for new coronaviruses. They published lists of which ones they had found pretty regularly. They published their last list in mid-2019, just a few months before the pandemic. Although lab leak proponents claimed these lists showed weird discrepancies, this was just their inability to keep names consistent, and all the lists showed basically the same viruses (plus a few extra on the later ones, as they kept discovering more). The lists didn’t include BANAL-52 or any other suitable COVID relatives - only RATG-13, which isn’t close enough to work. Could they have been keeping their discovery of BANAL-52 secret? No. Pre-pandemic, there was nothing interesting about it; our understanding of virology wasn’t good enough to point this out as a potential pandemic candidate. WIV did its gain-of-function research openly and proudly (before the pandemic, gain-of-function wasn’t as unpopular as it is now) so it’s not like they wanted to keep it secret because they might gain-of-function it later. Their lists very clearly showed they had no virus they could create COVID from, and they had no reason to hide it if they did. COVID’s furin cleavage site is admittedly unusual. But it’s unusual in a way that looks natural rather than man-made. Labs don’t usually add furin cleavage sites through nucleotide insertions (they usually mutate what’s already there). On the other hand, viruses get weird insertions of 12+ nucleotides in nature. For example, HKU1 is another emergent Chinese coronavirus that caused a small outbreak of pneumonia in 2004. It had a 15 nucleotide insertion right next to its furin cleavage site. Later strains of COVID got further 12 - 15 nucleotide insertions. Plenty of flus have 12 to 15 nucleotide insertions compared to other earlier flu strains. Sometimes insertions happen because of a mistake in viral replication. Other times the virus gets confused between its own RNA and its host’s, and splices a bit of the host RNA into the virus. This would neatly explain why the insertion used the unusual coding CGG for arginine, which is common in animals but rare in viruses. On the other hand, it’s not that rare in viruses - COVID uses CGG for arginine about 3% of the time. And human engineers don’t necessarily use it any more than that - Peter was able to find one example of humans adding arginine to a virus, and 0 out of the 5 arginines added were CGG. COVID’s furin cleavage site is a mess. When humans are inserting furin cleavage sites into viruses for gain-of-function, the standard practice is RRKR, a very nice and simple furin cleavage site which works well. COVID uses PRRAR, a bizarre furin cleavage site which no human has ever used before, and which virologists expected to work poorly. They later found that an adjacent part of COVID’s genome twisted the protein in an unusual way that allowed PRRAR to be a viable furin cleavage site, but this discovery took a lot of computer power, and was only made after COVID became important. The Wuhan virologists supposedly doing gain-of-function research on COVID shouldn’t have known this would work. Why didn’t they just use the standard RRKR site, which would have worked better? Everyone thinks it works better! Even the virus eventually decided it worked better - sometime during the course of the pandemic, it mutated away from its weird PRRAR furin cleavage site towards a more normal form. Further, COVID’s furin cleavage site was inserted via what seems to be a frameshift mutation - it wasn’t a clean insertion of the amino acids that formed the site, it was an insertion of a sequence which changed the context of the surrounding nucleotides into the amino acids that formed the site. This is a pointless too-clever-by-half “flourish” that there would be no reason for a human engineer to do. But it’s exactly the kind of weird thing that happens in the random chance of evolution. COVID is hard to culture. If you culture it in most standard media or animals, it will quickly develop characteristic mutations. But the original Wuhan strains didn’t have these mutations. The only ways to culture it without mutations are in human airway cells, or (apparently) in live raccoon-dogs. Getting human airway cells requires a donor (ie someone who donates their body to science), and Wuhan had never done this before (it was one of the technologies only used at the superior North Carolina site). As for raccoon-dogs, it sure does seems suspicious that the virus is already suited to them. The claim that COVID is uniquely adapted to humans is false. The paper that claimed that defined how well COVID was adapted to different animals by those animals’ difference (on the relevant cell receptors) from humans. So in its methodology, humans came out #1 by default. If you don’t do that, COVID is better-adapted to many other animals. It’s not necessarily true that viruses see a burst of mutations when they enter a new host. COVID spread to deer and mink, and in neither case was there a burst of mutations. COVID has a pretty simple job of infecting respiratory cells and is already very good at it, regardless of species. In Yuri’s model, Wuhan Institute of Virology picked up a discarded grant and decided to do the gain-of-function half allotted to a different university, despite their relative inexperience. They skipped over all the SARS-like viruses they were supposed to work on, and all the standard gain-of-function model backbones, in favor of BANAL-52, a virus which would not be discovered for another two years, but which they somehow had samples of, which they had for some reason decided to keep secret despite its total lack of interestingness. Then they would have had to eschew all usual gain-of-function practices in favor of inserting a weird furin cleavage site that shouldn’t have worked according to the theory they had at the time, via a frameshift mutation. Then they would have had to culture it, a technique beyond their limited capabilities. Then it would have had to leak, and magically show up again in front of the raccoon-dog stall at a wet market. Yuri: WIV wouldn’t have needed to keep BANAL-52 “secret” in some kind of sinister way. Plenty of researchers have backlogs of work they haven’t published yet. Probably they a found BANAL relative in one of their normal sampling trips, did some preliminary studies on it, and planned to publish it later once they cleaned up their data. Everyone works like this. The part of DEFUSE saying that they would only work on viruses that were 95% similar to SARS is unclear and might mean something else. It looks more like they say they’ll start with those viruses, but also do some work on novel viruses. BANAL-52 could have been one of the novel viruses. The furin cleavage site is weird, but the researchers might have done that on purpose, to make the virus easier to keep track of, or to test different furin cleavage sites. Depending on the exact BANAL-52 relative they used, it might not even be a frameshift; there’s a particular way to spell serine that would make the insertion more natural. The claims that COVID can’t be cultured in normal media are based on speculative original research by Peter and might not hold up. Peter: WIV did most of its virus-gathering in a trip to a Yunnan cave between 2010 and 2015. All those viruses have long since been processed and added to the database. There’s no sign that they made more trips to Yunnan caves, and no reason for them to keep that secret. So the idea that they might just have some new viruses they didn’t publish doesn’t hold up. But suppose they did make more trips. Given the amount of time between the DEFUSE proposal and COVID, if they kept to their normal virus-collection rate, they would have gotten about thirty new viruses. What’s the chance that one of those was BANAL-52? There are thousands of bat viruses, and BANAL-52 is so rare that it wasn’t found until well after the pandemic started and people were looking for it very hard. So the chance that one of their 30 would be BANAL-52 is low. Also, they said in DEFUSE that they planned to go back to the same Yunnan cave. But BANAL-52 was found far away from that cave, so unless it ranged over a wide area, they probably couldn’t have found it even if they got very lucky. Session 3: Closing Arguments This third debate was supposed to be about “inference”, ie how much Bayesian evidence was provided by each of the facts given so far, and how to fit them into the Rootclaim probabilistic model. I’m going to relegate my summary of the more probabilistic half to the next section of this post, and just include the closing arguments here. Saar: Peter’s case hinges on the idea that it’s very improbable that a lab leak pandemic would first show up at a wet market. But this isn’t necessarily improbable. The Huanan Seafood Market had several factors that made it a likely location for a superspreader event. It was busy, with over 10,000 visitors a day. Many of the people there (eg the 1,000 vendors) came back daily, letting them reinfect each other. It had poor ventilation, especially in the high-positivity area near the raccoon-dog stall. It had cold wet surfaces on which the virus could survive for long periods. It was indoors, which prevented UV light from killing the virus. Given a small amount of sporadic COVID going around Wuhan, it’s not surprising for the first place it started spreading en masse to be a wet market. In fact, we have several examples of this. When China was COVID Zero, there would occasionally be small outbreaks that the authorities would have to contain. Most of these were at wet markets. For example, the big COVID outbreak in Beijing started at Xinfadi Market, their local seafood market. This couldn’t be an animal spillover, because there were no raccoon-dogs or other weird wildlife there. So it must be that wet markets are natural places for superspreader events. There are several other examples, which make up about half of the total outbreaks in Zero COVID era China, plus others in Singapore and Thailand. Since COVID clusters concentrate in wet markets even when there is no animal spillover, we should accept this as a property of the virus, and not attribute any significance to the fact that this happened in Wuhan too. Peter: About 1/10,000 citizens of Wuhan was a wet market vendor. So there’s a 1/10,000 chance that the first known COVID case should be a wet market vendor by chance alone. Weibo lists the most popular places for people to check in to their network on their phones, and the wet market was the 1600th most popular place in Wuhan, meaning that if you weight locations by busy-ness, there’s a less than 1/1600 chance that the first cases would be in the wet market. Yes, the wet market is indoors, has mediocre ventilation, has repeat visitors, etc. So do thousands of other places in Wuhan, like schools, hospitals, workplaces, places of worship. The wet market isn’t special in any way. And again, it wasn’t a superspreader event! COVID spread at the same rate in the wet market as it does everywhere else: doubling once per 3.5 days. It doesn’t matter what kinds of arguments you can come up with for why the wet market should have been the perfect superspreader event location, we can look at it and see that it wasn’t. It’s an environment that spreads COVID at exactly the normal rate. Zero COVID era Chinese outbreaks were concentrated in wet markets because they received infected animal products. We know why there was an outbreak in the Xinfadi Market in Beijing: it was because the seafood stall got frozen fish from some non-Zero-COVID country, the fish had COVID particles on it, and the vendor got infected and spread it to everyone else. Something like this is true for the other Chinese wet market based outbreaks we know about it. So this makes the opposite point you think it does: wet markets start outbreaks because there are infected goods being sold there. Then the virus spreads through the wet market at a completely normal rate. Saar: The Weibo list of 1600 places bigger than the wet market is likely inaccurate, because it's based on check-in data and people don't check in to seafood markets. Most of those 1600 places aren't amenable to superspread. The 70 markets supposedly bigger than Huanan are irrelevant, because they're supermarkets, open air markets, etc. Huanan is the largest seafood market in central China, and a more likely place for the first cluster of cases to be noticed. Markets weren't a common spillover location in SARS1, so the zoonosis hypothesis hasn't "called" this event in a way that should give them a high Bayes factor. And there’s still plenty of evidence for isolated (though not super-spreading) pre-market cases. A British expatriate in Wuhan, Connor Reed, says he got sick in November, three weeks before the first wet market case. Later the hospital tested his samples and said it was COVID. Another paper reports 90 cases before the first wet market one. Peter: Connor Reed was lying. The case wasn’t reported in any peer-reviewed paper. It was reported in the tabloid The Daily Mail, months after it supposedly happened. He also told the Mail that his cat died of coronavirus too, which is rare-to-impossible. Also, to get a positive hospital test, he would have had to go to the hospital, but he was 25 years old and almost no 25-year-olds go to the hospital for coronavirus. His only evidence that it was COVID was that two months later, the hospital supposedly “notified” him that it was. The hospital never informed anyone else of this extremely surprising fact which would be the biggest scientific story of the year if true. So probably he was lying. Incidentally, he died of a drug overdose shortly after giving the Mail that story; while not all drug addicts are liars, given all the other implausibilities in his story, this certainly doesn’t make him seem more credible. And in any case, he claimed he got his case at a market “like in the media” The other 90 cases are also fake. A lab leak guy found a paper that mentioned 90 more cases than other papers, and made up a conspiracy theory where the author was trying to secretly communicate that there had been 90 secret cases before any of the confirmed cases, even though there was nothing about this in the text of the paper. But actually that paper just counted cases differently than other papers, and they were referring to normal cases after the pandemic officially started. Again, I’ll come back to the discussion about inference later, but for now, here’s a table of both sides’ reasoning. This exact presentation comparing both analyses is mine3, but you can see Saar’s version here, and Peter’s starting at 45:33 of this video. Slightly made up; the two sides didn’t express their probabilities in the same way and I had to make editorial decisions to match them. Note that these aren't entirely comparable because Peter is being laxer about out-of-model probability than Saar. Although Saar's final odds here are 533-to-1, this just the central estimate. Rootclaim’s real final probability is 94% lab leak. You can see their analysis here. And The Winner Is . . . … … … … … Peter and the zoonosis hypothesis. This was a decisive victory. There were two judges, who each gave separate verdicts (or were allowed to declare a draw). Both judges decided in favor of Peter. You can see the judges’ own summary of their reasoning here (Will, Eric) Manifold agreed with the judges. There was a prediction market on who would win. It started out 70-30 in favor of lab leak. As the videos came out, zoonosis started doing better and better. I don’t want to take the exact final numbers too seriously, since I think some of the later price increases involved hints from the participants’ behavior. But it’s clear which way viewers thought the wind was blowing4. Around the same time, the Good Judgment Project - Philip Tetlock’s group studying superforecasters - put out a report on the lab leak hypothesis. After studying it in depth, his forecasters ended up 75-25 in favor of zoonosis. The Rootclaim debate was one of ten sources they said they found especially interesting. And also around the same time, and unrelated to any of this, the Global Catastrophic Risks Institute surveyed experts (“168 virologists, infectious disease epidemiologists, and other scientists from 47 countries”) and found the same thing (though see here for some potential problems with the survey): For what it’s worth, I was close to 50-50 before the debate, and now I’m 90-10 in favor of zoonosis. III. The Math And The Aftermath The third debate session was about “inference”, how to put evidence together. I put this part off until after disclosing the winner, because I wanted to talk about some of these issues at more length. The Math: Judges Both judges included a probabilistic analysis in their written decision. Here’s the same table as above, expanded to add the judges: I shoehorned the judges’ factors into the categories I already had; some of them were actually subtly different from Peter’s, Saar’s, and each other’s. The “priors” category is especially a mess here. We’ll go over these later, but I get the impression that they both thought of probabilistic analyses as an afterthought. For example, Judge Eric wrote 30,000 words about which considerations moved him, and only then includes the analysis, saying: I am not convinced that this Bayesian calculation is even an appropriate way to estimate the relative posterior probability of Z and LL; it just seemed fair that after criticizing Rootclaim’s calculations at length I should make an attempt at it myself. Judge Will’s decision ran to 10,000 words. He said he independently tried both reasoning it out intuitively, and running the Bayesian analysis, and was relieved when these two methods returned the same result. He said: I am skeptical that the Bayesian decision making/evaluation methods are any more "objective" than [intuitive reasoning]. I think they maximize legibility, not objectivity, and tend to hide the intuitive/heuristic portion in the data inclusion step and values, where it’s harder to see . . . I am not skilled in the Bayesian method, and I am sure I made significant mistakes. More time and practice would improve and refine my estimates. At the fundamental rules of the universe level, Bayesian analysis must be the best way to evaluate evidence. However, I am unsure that it’s a good strategy for a human given our cognitive limitations, and doubly unsure it’s truly being used (in the dispassionate sense) where the outcome is social desirability/fame/Twitter likes. I’m focusing on this because Saar’s opinion is that the debate went wrong (for his side) because he didn’t realize the judges were going to use Bayesian math, they did the math wrong (because Saar hadn’t done enough work explaining how to do it right), and so they got the wrong answer. I want to discuss the math errors he thinks the judges made, but this discussion would be incomplete without mentioning that the judges themselves say the numbers were only a supplement for their intuitive reasoning. That having been said, let’s look deeper into some of Saar’s concerns. The Math: Extreme Odds Saar complained that Peter’s odds were too extreme. For example, Peter said there was only a 1/10,000 chance that a lab leak pandemic would first show up at a wet market. Peter’s argument went something like: obviously a zoonotic pandemic would start at a site selling weird animals. But a lab leak pandemic - if it didn’t start at the lab - could show up anywhere. 1/10,000 Wuhan citizens work at the wet market. So if a lab leak was going to show up somewhere random, the wet market was a 1/10,000 chance. Saar had specific arguments against this, but he also had a more general argument: you should rarely see odds like 1/10,000 outside of well-understood domains. In his blog post, he gave this example: A prosecutor shows the court a statistical analysis of which DNA markers matched the defendant and their prevalence, arriving at a 1E-9 probability they would all match a random person, implying a Bayes factor near 1E9 for guilty. But if we try to estimate p(DNA|~guilty) by truly assuming innocence, it is immediately evident how ridiculous it is to claim only 1 out of a billion innocent suspects will have a DNA match to the crime scene. There are obviously far better explanations like a lab mistake, framing, an object of the suspect being brought by someone to the scene, etc. So the real p(wet market|lab leak) isn’t the 1/10,000 chance a pandemic arising in a random place hits the wet market, but the (higher?) probability that there’s something wrong with Peter’s argument. Then Saar tried to show specific things that might be wrong with Peter’s argument. I didn’t find his specific examples convincing. But maybe the question shouldn’t be whether I agreed with him. It should be whether I’m so confident he’s wrong that I would give it 10,000-to-1 odds. This makes total sense, it’s absolutely true, and I want to be really, really careful with it. If you take this kind of reasoning too far, you can convince yourself that the sun won’t rise tomorrow morning. All you have to do is propose 100 different reasons the sunrise might not happen. For example: The sun might go nova.
Inline links: Pekar 2022, Pipes 2021, says, 3, here, this video, https://substackcdn.com/image/fetch/$s_!8aU2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F815fe32d-d7ea-401b-b3a2-d8cd25b52ee8_490x780.png, https://substackcdn.com/image/fetch/$s_!0Tm_!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd0492f69-7b7e-4611-9d76-64ef8d7f59d5_511x511.png, Will, Eric, agreed, 4, put out a report on the lab leak hypothesis, https://substackcdn.com/image/fetch/$s_!g7k2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F37f1b493-b556-41ec-925e-03f9d8bc26cb_1456x849.webp, surveyed experts, see here, https://substackcdn.com/image/fetch/$s_!Zejl!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe9c88e87-b6ca-4c6d-840e-24da726f50b7_975x365.png, https://substackcdn.com/image/fetch/$s_!T5rV!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4983e2cd-4151-42de-9685-08037ef7a8e8_635x788.png
Okay, this one is just awful. It takes the risky gambit above - giving extreme odds to something - then doubles down on it by multiplying across twenty different stages to get a stupendously low probability of 1/5*10^25. If we believe this, it’s more likely that we win the lottery three times in a row than that we learn lab leak was true after all. Eliezer Yudkowsky calls this the Multiple Stage Fallacy. Even aside from the failure mode in the sunrise example above (where people are too reluctant to give strong probabilities), it fails because people don’t think enough about the correlations between stages. For example, maybe there’s only 1/10 odds that the Wuhan scientists would choose the suboptimal RRAR furin cleavage site. And maybe there’s only 1/20 odds that they would add a proline in front to make it PRRAR. But are these really two separate forms of weirdness, such that we can multiply them together and get 1/200? Or are scientists who do one weird thing with a furin cleavage site more likely to do another? Mightn’t they be pursuing some general strategy of testing weird furin cleavage sites? (For example, Yuri proposed that, because the scientists wanted to understand how pandemic coronaviruses originate in nature, they might deliberately pick more natural-looking features over more designed-looking ones, which would neatly explain many features seemingly inconsistent with lab leak. Is this a conspiracy theory? Rootclaim is able to successfully route around this question. If the probability of a feature happening in nature is X, then the probability of it happening in this variant of lab leak scenario is X * [chance that the scientists wanted to imitate nature). This gives it a (deserved) complexity penalty without ruling out this (non-zero and potentially important) possibility.) In any case, Peter didn’t care as much about probabilistic analysis as Saar, he didn’t make his case hinge on this slide, and he might have been kind of using it to troll Rootclaim (which definitely worked). He might not have been making any of the mistakes above. But anyone who took this slide seriously would end up dramatically miscalibrated. The Math: Big Pictures Another of Saar’s concerns with the verdict was that Peter was an extraordinary debater, to the point where it could have overwhelmed the signal from the evidence. It’s hard to watch the videos and not come away impressed. Peter seems to have a photographic memory for every detail of every study he’s ever read. He has some kind of 3D model in his brain of Wuhan, the wet market, and how all of its ventilation ducts and drains interacted with each other. Whenever someone challenged one of his points, he had a ten-slide PowerPoint presentation already made up to address that particular challenge, and would go over it with complete fluency, like he was reciting a memorized speech. I sometimes get accused of overdoing things, but I can’t imagine how many mutations it would take to make me even a fraction as competent as Peter was. Saar’s closing argument included the admission: Peter, I think everyone can agree, has much more knowledge on [COVID] origins than we do. He's invested much more time. He may be a much more talented researcher. He's much more into the details. He probably knows the best in the world on origins at this point. Once you’ve described your opponent that way in your closing argument, what’s left of your case? Saar thought a lot was left. Throughout the debate, he tried to make a point about how getting the inference right was more important than winning sub-sub-sub-debates about individual lines of evidence. Although Peter won most specific points of contention, Saar thought that if the judges could just keep their mind on the big picture, they would realize a lab leak was more likely. I’m potentially sympathetic to arguments like Saar’s. Imagine a debate about UFOs. Imaginary-Saar says “UFOs can’t be real, because it doesn’t make sense for aliens to come to Earth, circle around a few fields in Kansas, then leave without providing any other evidence of their existence.” Imaginary-Peter says “John Smith of Topeka saw a UFO at 4:52 PM on 6/12/2010, and everyone agrees he’s an honorable person who wouldn’t lie, so what’s your explanation of that?” Saar says “I don’t know, maybe he was drunk or something?” Peter says “Ha, I’ve hacked his cell phone records and geolocated him to coordinates XYZ, which is a mosque. My analysis finds that he’s there on 99.5% of Islamic holy days, which proves he’s a very religious Muslim. And religious Muslims don’t drink! Your argument is invalid!” On the one hand, imaginary-Peter is very impressive and sure did shoot down Saar’s point. On the other, imaginary-Saar never really claimed to have a great explanation for this particular UFO sighting, and his argument doesn’t depend on it. Instead of debating whether Smith could or couldn’t have been drunk, we need to zoom out and realize that the aliens explanation makes no sense. The problem was, Saar couldn’t effectively communicate what his big picture was. Neither deployed some kind of amazingly elegant prior. They both used the same kind of evidence. The only difference was that Peter’s evidence hung together, and Saar’s evidence fell apart on cross-examination. I think - not because Saar really explained it, but just reading between the lines - Saar thought the un-ignorable big picture evidence was the origin in a city with a coronavirus gain-of-function lab, and the twelve-nucleotide insertion in the furin cleavage site. To some degree, Peter just ate the loss on those questions. No matter how you slice it, it really is a weird coincidence that the epidemic started so close to Asia’s biggest coronavirus laboratory. Peter tried to deflect this - he pointed out there were other BSL-3 and BSL-4 laboratories in Beijing, Shanghai, Shenzhen, etc. But this was a rare question where he unambiguously came out looking worse - the other cities’ labs had much less coronavirus-specific research. Wuhan really was unique (aside from the other big coronavirus lab in North Carolina). Peter did better when he tried to control the damage: there are a couple hundred million people in the South Asian areas where people eat weird animals exposed to virus-infected bats, Wuhan has a population of about 12 million, so maybe 1.5% of all potential zoonotic pandemics should start in Wuhan. Peter tried to argue that Wuhan was a local trade center, so maybe we should up that to 5 - 10%. 5 - 10% coincidences aren’t that rare. Even 1.5% coincidences happen sometimes. Likewise, the furin cleavage site really does stand on a genetic map. I didn’t feel like either side did much math to quantify how weird it was. Naively, I might think of this as “30,000 bases in COVID, only one insertion, it’s in what’s obviously the most interesting place - sounds like 30,000-to-one odds against”. Against that, a virus with a boring insertion would never have become a pandemic, so maybe you need to multiply this by however much viral evolution is going on in weird caves in Laos, and then you would get the odds that at least one virus would have an insertion interesting enough to go global. Neither participant calculated this in a way that satisfied me (though see here for related discussion). Instead, Peter tried to undermine the furin argument by showing that, as surprising as the site was under a natural origin, it would be an even more surprising choice for human engineers. Saar argued it wasn’t - but because of his policy of giving adjusted-for-model-error odds, he only gave this a factor of 30 in his analysis. Since Peter gave it a higher factor of 50 in his analysis, it looked from the outside like Saar had already conceded this point, and the judges were mostly happy to go with Saar’s artificially-low estimate. The Math: Double Coincidences Saar brought up an interesting point halfway through the debate: you should rarely see high Bayes factors on both sides of an argument. That is, suppose you accept that there’s only a 1-in-10,000 chance that the pandemic starts at a wet market under lab leak. And suppose you accept there’s only a 1-in-10,000 chance that COVID’s furin cleavage site could evolve naturally. If lab leak is true, then you might find 1-in-10,000 evidence for lab leak. But it’s a freak coincidence that there was 1-in-10,000 evidence for zoonosis5. Likewise, if zoonosis is true, you might find 1-in-10,000 evidence for this true thing. But it’s a freak coincidence that there was 1-in-10,000 evidence for lab leak. Either way, you’re accepting that a 1-in-10,000 freak coincidence happened. Isn’t it more likely you’ve bungled your analysis? I was following along at home, and I definitely bungled this point; I had some high Bayes factors on both sides. I adjusted some of them downward based on Saar’s good point, but how far should we take it? Here I remember The Pyramid And The Garden: you can get very strong coincidences if you have many degrees of freedom, ie buy a lot of lottery tickets. So for example, suppose there are fifty things about a virus. You should expect at least one of those to have a one-in-fifty coincidence by pure chance. What about more than that? You might be able to get away with this by saying there are an infinite number of possible conspiracy theories, and some from that infinite set are brought into existence when a strong enough coincidence makes them plausible. For example, it’s really weird that John Adams and Thomas Jefferson both died on the 50th anniversary of the Declaration of Independence. If I wanted, I could form a conspiracy theory about a group of weird assassins obsessed with killing Founding Fathers on important dates, and then Jefferson and Adams’ deaths would be 1/10,000 evidence for that theory. But this is the Texas Sharpshooter Fallacy, which Saar warned against several times. I don’t know if “the virus started in Wuhan, which is where they’re doing this research” gets a Texas Sharpshooter penalty, or how high that penalty should be. But the furin cleavage site doesn’t - people were talking about lab leak before anyone noticed it. The Aftermath: Peter Peter seemed satisfied with the result, in an understated sort of way: It seemed like an interesting experiment in monetizing the debunking of a conspiracy theory. I think there's usually a big asymmetry where it's easy to get rich spreading bullshit (like, the top anti-vaxxers during the pandemic all made a million dollars a year on substack), but it's almost impossible to make money on debunking it. The Rootclaim challenge seemed like one rare case where the opposite was true. Beyond that, I don't know what it's good for. It does seem like there could be a positive social impact from more people understanding that the lab leak hypothesis is (almost certainly) false. The Aftermath: Saar Saar says the debate didn’t change his mind. In fact, by the end of the debate, Rootclaim released an updated analysis that placed an even higher probability on lab leak than when they started. In his blog post, he discussed the issues above, and said the judges had erred in not considering them. He respects the judges, he appreciates their efforts, he just thinks they got it wrong. Although he respected their decision, he wanted the judges to correct what he saw as mistakes in their published statements, which delayed the public verdict and which which Viewers Like You did not appreciate: I ran an early draft of this post by him. There was some miscommunication about the exact publication date, so he hasn’t had time to write up a full response, but he has some quick thoughts (and I’ll link the full response when he writes it). He says: We will provide a full response to this post soon, but the main problem with it is fairly simple: There is general agreement that the main evidence for zoonosis is HSM (Huanan Seafood Market) forming an early cluster of cases. The contention is whether it is amazing 10,000x evidence, or is it negligible. All other evidence points to a lab leak, and if HSM is shown to be weak, lab leak is a clear winner. We provided an analysis of why it is negligible that is as close to mathematical proof as such things can be. Read it here. Scott and I exchanged a few emails on this issue and Scott preferred to discuss more intuitive analyses of HSM, using rules of thumb that likely served him well in the past. While I believe I managed to mostly explain where these failed, and Scott understands HSM is far weaker evidence than he initially thought6, he still has a very strong intuitive feeling (based on years of dealing with probabilities) that this is some exceptional coincidence, and that prevents him from properly updating his posterior. At the end of the day, this cannot be settled without going through our semi-formal derivation, understanding it, and either identifying the problem with it or accepting it (and thereby accepting lab-leak to be more likely). Here is a quick summary of the mistakes made by those claiming HSM is strong evidence: The first mistake is conflating Bayes factors with conditional probabilities. 1/10000 is the supposed conditional probability p(HSM|Lab Leak), That should be divided by the conditional probability of HSM under Zoonosis. Markets were not identified as a high-risk location prior to this outbreak (This will be elaborated in the full response), and in SARS1 the spillovers were mostly at restaurants and other food handlers that deal more closely with wildlife. While it's cool to point to the raccoon dog photo, that was a result of a retrospective search (we don't know what other photos they took which in retrospect would be brought up as premonition). Unbiased data shows markets are not a likely spillover location for zoonosis. We originally estimated p(HSM|Zoonosis)<0.1. Following more research we did to answer Scott's questions, this is more likely <0.03.
Inline links: Multiple Stage Fallacy, see here, 5, The Pyramid And The Garden, Texas Sharpshooter Fallacy, blog post, 6
Links For April 2025
April 22, 2025 · Original source
14: Did you know that China has mostly solved the problem of smog in Beijing? (X)
Inline links: China has mostly solved the problem of smog in Beijing
Meetups Everywhere Spring 2026: Times & Places
April 01, 2026 · Original source
Contact: Eliot Contact Info: Redeliot[@]gmail[.]com Time: Thursday, April 16th, 6:00 PM Location: Skyview Hall, 14 Frances St Randwick, walk to the end of the car park to the green gate and through to the back building, upstairs in the hall. Call Eliot if lost 0438481143 Coordinates: https://plus.codes/4RRH36QQ+QR Group Link: https://www.meetup.com/rationalists_of_sydney Notes: Usual attendance is 10-30 people. Bring a friend! China BEIJING Contact: Anonymous Contact Info: 13021231532 Time: Friday, April 3rd, 5:00 PM Location: Top floor of PAGEONE cafe in Haidian, on the table with a paper aeroplane Coordinates: Coordinates not specified Notes: This is my first time organising - text me before April 1st ideally if you’re interested, I have zero idea if there are nonzero ACX readers in Beijing.
Inline links: https://plus.codes/4RRH36QQ+QR, https://www.meetup.com/rationalists_of_sydney
Contact: Anonymous Contact Info: 13021231532 Time: Friday, April 3rd, 5:00 PM Location: Top floor of PAGEONE cafe in Haidian, on the table with a paper aeroplane Coordinates: Coordinates not specified Notes: This is my first time organising - text me before April 1st ideally if you’re interested, I have zero idea if there are nonzero ACX readers in Beijing.