Astral Codex Ten

Australia

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Australia

Article

Australia is a recurring place in the Astral Codex Ten archive, appearing 53 times across 53 issues between April 14, 2021 and April 01, 2026. The archive places it in contexts such as “It’s approved in Europe, the UK, Australia, Israel, etc”; “list of Best Practice Peer Countries including: Australia”; “A few will join the US as “masters of the chaos,” as they have favorable geographies”. It most often appears alongside Scott, Canada, California.

Metadata

  • Category: Places
  • Mention count: 53
  • Issue count: 53
  • First seen: April 14, 2021
  • 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.

Prospectus On Próspera
April 14, 2021 · Original source
Likewise, Próspera has 100% drug approval reciprocity. If a drug has been approved in an OECD country (eg by the FDA), it’s approved in Próspera. Again, close to my heart. Amisulpride is a great antipsychotic, probably better then most of the ones we use here. It’s approved in Europe, the UK, Australia, Israel, etc, where many studies have shown it’s safe and effective. Because none of those studies were done in the US, the FDA refuses to approve it here, and has demanded several hundred million dollars worth of more studies, which the company involved has chosen not to do (an injectable version was recently approved for nausea, but can’t be easily used for psychosis). Meanwhile, bupropion (“Wellbutrin”), the fourth-most prescribed antidepressant in the US, isn’t approved for depression in Britain; the subset of patients who respond to this medication and nothing else are out of luck. Próspera will be one of the only places in the world where patients will have access to amisulpride, bupropion, and all the other medications that one country or another is restricting because “it wasn’t invented here”.
Australia, Austria, Belgium, Canada, Chile, Denmark, Dubai, Estonia, Finland, France, Germany, Iceland, Ireland, Hong Kong, Israel, Italy, Japan, South Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Spain, Sweden, Singapore, Switzerland, United Kingdom, and United States of America
Your Book Review: The Accidental Superpower
May 21, 2021 · Original source
The second half of The Accidental Superpower is filled with Zeihan’s predictions about what happens if the big thesis is right. Some states will fail, as they don’t have what’s needed to survive (Syria, Greece, Libya). Some will decentralize, as they’re in the same boat, just not as hard up (Russia, China). Some will merely decline, as they have some capacity to address challenges (Brazil, India, Canada). Some will cope (UK, France, Peru, Philippines). A few will join the US as “masters of the chaos,” as they have favorable geographies and other advantages (Australia, Argentina, Angola, Turkey, Indonesia, Uzbekistan).
Instead Of Pledging To Change The World, Pledge To Change Prediction Markets
June 07, 2021 · Original source
This is nice, but I can't help but remember eg Australia's 2009 Copenhagen summit pledge to decrease emissions 5% by 2020 (in fact, they increased 17%). Or Brazil's pledge at the same summit to cut emissions 38% by 2020 (in fact, they increased 45%). Or Canada's pledge for -20% (they got +1%). I'm not cherry-picking bad actors here, I'm just going through the alphabet (pledges source, outcomes source) . For that matter, what about George W. Bush's pledge to return Americans to the moon by 2020?
Inline links: pledges source, outcomes source, pledge to return Americans to the moon
Your Book Review: Plagues And Peoples
June 17, 2021 · Original source
Australian rabbit populations provide a fascinating view of virus-host evolution in fast forward.
Inline links: Australian rabbit populations
Diamond responds that he is answering adequately broad questions like: Why was there civilization in Europe and not in Australia? Closing with “Historians’ failure to explain history’s broadest pattern leaves us with a huge moral gap. In the absence of convincing explanations, many (most?) people resort, consciously or unconsciously, to racist assumptions: the conquerors supposedly had superior IQ or culture.”
Inline links: Diamond responds
Links For June
June 23, 2021 · Original source
Still trying to figure out who the black bird and the frog are supposed to be, or why Australia seems to have replaced Germany in the G7.
Lockdown Effectiveness: Much More Than You Wanted To Know
July 07, 2021 · Original source
Australia and New Zealand managed to do very well by combining well-targeted border closures with very strict early lockdowns. This helped them get cases low enough to the point where their test-and-trace program could manage them, and helped them get through the pandemic relatively gently (so far). This was a great strategy for the countries that were quick-thinking, clear-thinking, and lucky enough to pull it off, ie very few of them.
Book Review Contest: Winners
July 10, 2021 · Original source
Misha is an investor in Sydney, Australia, and blogs here.
Inline links: here
(Outdoor, Careful) Meetups Everywhere 2021 - Seeking Organizers
August 10, 2021 · Original source
According to the recent surveys, 97% of ACX readers in the US are vaccinated. Other developed countries have roughly similar numbers (except for Australia, where I am recommending no meetups for now). I will request that only vaccinated people attend these meetups - but knowing that I can’t enforce this, it makes me reassured to learn that almost everyone is vaccinated anyway.
Long COVID: Much More Than You Wanted To Know
September 02, 2021 · Original source
This is terrible. Recovery rates in the single digit percentages over the space of years. You would think at least some patients would get placebo recoveries, or forget how it felt to be well, or otherwise Lizardman themselves into fake complacency, but no. This is f@#$ing awful. Maybe COVID won’t be this bad? One ray of hope comes from this Australian study, where doctors record the rates of recovery from postviral fatigue after various rare diseases they encounter (Epstein-Barr, Q fever, Ross River virus). They find that 35% of these patients have postviral fatigue after six weeks, but only 12% after six months, and 9% after twelve months. This sounds a lot better than chronic fatigue. In fact, these people do the kind of weird task of figuring out how bad different diagnostic labels for fatigue are, even though some might argue that all the labels refer to the same underlying reality. They find an official diagnosis of “CFS/ME” (chronic fatigue / myalgic encephalitis) is much worse than “postviral fatigue”. Using the weird measure of “days per year of followup with diagnosis” (I’m not sure I fully understand their reasoning for why this is good), they find a median length of 80 for CFS/ME vs. 0 for PVF (…huh?). Using the more comprehensible measure of percent who still complain of fatigue after 7-12 months, they find it’s 24% vs. 10% (which super contradicts the above study saying that basically nobody with a CFS/ME diagnosis ever recovers). My guess is that this study had much lower criteria for a CFS/ME diagnosis (some doctor diagnosed it and put it on the insurance records) compared to the ones above (some specialist confirmed it by official criteria). The conclusion I draw is that, while official CFS/ME is horrible and hopeless, there are a lot of things that unofficially look kind of chronic-fatigue-ish which have pretty good prognoses. Since there’s no good reason to think post-COVID fatigue is official CFS/ME as opposed to just some chronic-ish fatigue-ish thing, probably it will have a better prognosis, more like weird Australian viruses. …which we still don’t know, because AFAICT nobody has done any good studies on postviral fatigue lasting more than a year. 5. Psychosomatic symptoms probably aren’t the majority of long COVID. I mean, I’m not seeing too many people claiming that they are. There are a lot more people worried that someone else might be claiming that, than people actually making the claim. Still, the Wall Street Journal opinion section is always up for slathering itself in glue and rolling around in a haystack until it becomes the straw man everyone else warned you about, and they do have an article on The Dubious Origins Of Long COVID. They point out that long COVID was first thrust into the public consciousness in surveys run by Body Politic, who self-describe as “a queer feminist wellness collective merging the personal and the political”. I agree this is a weird source for something to come from, but Hans Asperger was a Nazi and I still use his diagnosis, so I probably have to accept these people’s as well. More relevantly, WSJ points out that many of the people complaining of Long COVID symptoms test negative for COVID, or at least never tested positive. This complaint conflates the fact that not everyone was able to get a COVID test at all, with the fact that sometimes you get the acute COVID test after you’ve recovered from acute COVID and it’s negative, with the fact that COVID tests don’t have a 100% success rate, with the fact that yeah, okay, some people who didn’t have COVID are probably imagining Long COVID symptoms. I feel like some of the case-control studies above, which clearly show that seropositive people have higher rates of Long COVID than seronegative people, are pretty convincing here. But also - the people with lung scarring clearly have lung scarring, and most of them have weird x-rays consistent with lung scarring. If you have lung scarring, then you have trouble breathing, you’re fatigued, and you probably have lots of other stuff downstream of that. The people with smell/taste disturbances clearly have smell/taste disturbances, testable with the stupidly named but scientifically venerable Sniffin Sticks test - and also, who even cares enough to make up olfactory problems? Fatigue and brain fog are the only symptoms here that can’t be easily objectively confirmed, and, well, do you think those Australians who got infected with Q fever and had twelve months of postviral fatigue are faking? What about all those post-Epstein Barr fatigue people? Lots of viruses cause postviral fatigue, it’s not really surprising that COVID should also. (WSJ also spends a while arguing that CFS/ME is just a psychiatric disorder, which I think is not really in keeping with the best recent evidence. Also, as a psychiatrist, I’m very against this conclusion, mostly because if it were true, then people would expect me to cure CFS/ME patients.) One point WSJ didn’t bring up but could have was that most Long COVID patients are women. Probably this is somewhere between 60 and 80% - I suspect on the lower end of this, because I think women are more likely to talk about these kinds of things than men, and much more likely to eg join Facebook groups. This is noteworthy, because women are traditionally more prone to psychosomatic illnesses - so much that the ancients attributed these to the uterus and called them hysteria (note shared root with eg “hysterectomy”). Women are about 2x as likely to get diagnosed with panic disorder, anxiety disorders, phobias, etc, about 2.5x as likely to get chronic Lyme disease, widely regarded as an entirely psychosomatic condition, and 3-5x more likely to be diagnosed with fibromyalgia. So the female preponderance is suspicious. But women are also somewhere between 2x and 4x more likely to get autoimmune disorders than men (it varies by disorder - the ratio for Sjogren’s is as high as 16x). There are some pretty crazy hypotheses for why this is - for example, maybe women’s immune systems are permanently upregulated to be prepared for attempts by the placenta to secrete immune-downregulating chemicals during pregnancy, as part of the creepy shadow war between mother and fetus to regulate the maternal environment. I don’t know, do you have a better idea? Anyway, women have more autoimmune issues and more upregulated immune systems, so if there was any good way to assess gender ratio in true postviral fatigue excluding all psychosomatic cases, that would probably be female-biased too. Probably some Long COVID cases are psychosomatic just like some cases of anything are psychosomatic, but I don’t see too many signs that this is too important in explaining the phenomenon. …and please allow me a moment of preachiness here. Chronic fatigue sounds really fake to anyone who doesn’t have it. I think this is because it’s related to willpower. Willpower itself would sound fake to anyone who didn’t have to worry about it. “Oh, so you can go partying with your friends whenever you want, but as soon as it comes time to write a ten page report, your ‘lack of willpower’ prevents you from doing it? A likely story!” Still, all of us (except Bryan Caplan) recognize how real and important willpower is - how having more of it is better than having less of it, and how some condition that caused you to have pathologically little of it would be a huge disaster. In the comments section to the rough draft of this post, CJ wrote: I will say - I was one of those types of men to scoff with skepticism at people claiming to have chronic fatigue and the like. I would have called those people lazy and would have been adamant they were faking it or feeling like crap because of unhealthy lifestyle choices. Unfortunately I have learned the hard way the severity of neurological conditions, what it feels like to have brain fog, what chronic fatigue feels like, and how difficult it can be to communicate neurological symptoms to others. I now start from a position of listening to people who are willing to open up about their symptoms and trust that they are being honest. There are millions of people suffering in silence with untreated and undiagnosed disorders - those people are not all faking it or just dealing with psychosomatic conditions. I would recommend Jennifer Brea's documentary, Unrest. Thank you for shedding some light on the subject. Heron added: I second the suggestion to watch 'Unrest,' and to consider the many unseen ill whose symptoms are deemed to be imagined. Until this last year, I had little patience with, and doubted, people who I saw as hypochondriacs. Then I became the thing I hated. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Long COVID do have similarities from what I've read, since becoming ill in August 2020. At that time, here in Northern Ireland, there was scant availability of COVID tests; after spending three days trying to get hold of one, (by which time I'd stopped teaching my post-grad online classes & I haven't worked since) I became too ill to do anything. I figured if this was COVID I'd gotten off lightly, mostly constant severe headache, inability to think, a new experience of fatigue, high temperature, insomnia, hypersomnia, paresthesia, no smell or taste etc Debilitated but not dead. Except for the fact that I still have the aforementioned symptoms a year on and whilst they fluctuate in type and severity, the fatigue, headaches and cognitive difficulties are real. A brain scan, an appointment for brain and spinal MRIs (waiting lists, even when going private [as NHS has 3-8 yr waiting lists here in NI] are lengthy), rare virtual doctors and neurologists suggest my ailments constitute a post-viral thing, maybe Long C, they can offer nothing but pills for pain. There is no test for ME/CFS yet, nor a Long C test, symptoms and presentation are so varied. Given a widespread lack of knowledge and resources regarding these ailments, you're on your own. Maybe I've developed ME, I certainly have post-exertional malaise which my very prominent neurologist hadn't heard of. Looking at the history of ME/CFS* and a dearth of research surrounding it, I hope that rather than dismiss the lives of sufferers of this or the long-lasting aftermath of COVID, that those experiencing such difficulties will be heard and learnt from. I only understood when I had no alternative. I don’t think I ever actively pooh-poohed CFS, but like everyone else who encountered it, I underestimated just how bad it was until I met some patients with the condition. It is real and really bad. For whatever reason it is hard to think about and take seriously, but it really is as bad as people say. </preachiness> 6. Long COVID is probably rare in children This matters a lot, because children are (currently) ineligible for the vaccine, and also likely to encounter the virus at school. But children usually have mild cases of COVID and don’t die from it, so it’s tempting to just not worry about them. But if they could get Long COVID, that would make it much less tempting. Preliminary Evidence On Long COVID In Children sounds like a good paper to draw conclusions from. It says 42.6% of children with COVID experience long-term follow-up symptoms, which would be higher than the rate for adults. But it has no control group, and most of the symptoms it finds don’t seem very COVID-related (eg rashes, constipation). The most common symptom (20%) is insomnia, which better studies in adults fail to associate with real Long COVID. The rate of known long COVID symptoms (eg taste and smell problems) is only about 3-4%, and no higher or lower than anything else. Probably these kids are just having problems at the usual rate and attributing them to their recent COVID. Blankenburg et al do the correct thing and ask a thousand children about potential symptoms, then compare the number who say yes vs. no among COVID-seropositive and seronegative subjects. They find no difference between the two groups. Both are reporting a lot of insomnia, etc. They reasonably attribute this to pandemics being a stressful event that it’s natural to lose sleep over. This is really reassuring, but it can’t rule out a somewhat rarer syndrome. The authors say that they might miss symptoms with a prevalence of less than 10%, and one of them gives his own personal guess that it’s 1%. An English team says there’s a Long COVID rate of 4.6% in kids. But there was a 1.7% rate of similar symptoms in the control group of kids who didn’t have COVID, so I think it would be fair to subtract that and end up with 2.9%. And even though the study started with 5000 children, so few of them got COVID, and so few of those got long COVID, that the 2.9% turns out to be about five kids. I don’t really want to update too much based on five kids, especially given the risk of recall bias (ie you might notice / care about your symptoms more if you know you had COVID before getting them). My overall conclusion here is that long COVID is rarer in children than adults, and may not exist at all. The studies tell us it’s probably somewhere less than 5% of kids, but so far we can’t conclude anything stronger than that. 7. Vaccination probably doesn’t change the per-symptomatic-case risk of Long COVID much Here’s a complicated Twitter thread about this. Of vaccinated people who got symptomatic COVID, about a third ended up with Long COVID symptoms, the same rate as in unvaccinated people. Of course, vaccinated people are much less likely to get symptomatic COVID. But even conditional on getting it, they’re still much less likely to go to the hospital, die, etc. It would have been nice if the same was true of getting Long COVID. But it doesn’t look that way. (all this information is from an online poll by a sketchy group of COVID “survivor” activists. But they wrote up their poll in the scientific paper font, as a PDF and everything, so I say we count it anyway) This NEJM study wasn’t exactly designed to look for Long COVID in vaccinated people. But they found it anyway, at a rate of 19% after 6 weeks. This also fits within the (wide) range reported for unvaccinated people. They don’t give a symptom breakdown beyond “prolonged loss of smell, persistent cough, fatigue, weakness, dyspnea, or myalgia”, which sounds like the usual set. These studies are pretty weak, and you could argue that given that vaccines decrease the average severity of COVID infection, and infection severity is linked to Long COVID risk, we should have a strong prior on vaccines decreasing Long COVID risk. And just before publishing this, someone sent me this study, which very preliminarily finds vaccines might decrease Long COVID risk by a factor of 2. I think a factor of 2-3 is believable; one of 10 or 20, less so. Weirdly, there are some claims that vaccines can help relieve symptoms of existing long COVID. Sounds kind of like sympathetic magic to me, but the researcher quoted in the linked article said it might “improve symptoms by eliminating any virus or viral remnants left in the body” or by “rebalancing the immune system”. So yeah, sympathetic magic. 8. Your risk of a terrible long COVID outcome conditional on COVID is probably between a few tenths of a percent and a few percent. My original calculation went like this: About 25% of people who get COVID report long COVID symptoms. About half of those go away after a few months, so 12.5% get persistent symptoms. Suppose that half of those cases (totally made-up number) are very mild and not worth worrying about. Then 6.25% of people who get COVID would have serious long-lasting Long COVID symptoms. After doing that calculation, I read this essay by Matt Bell, who tries to figure out the same thing. He is much more optimistic. He agrees that about half of long COVID cases go away after a few months, but adds another 50% decrease from “few months” to “lifelong”, kind of on priors, admitting there’s not too much positive evidence for this. Then he adds another factor-of-two decrease from vaccination, based on very preliminary studies from the UK. He estimates that someone with my demographics (vaccinated man in his 30s) has a 2% risk of Long COVID conditional on getting COVID at all. Then he divides by five for the true worst case scenario, based on studies showing that a fifth of people with Long COVID report that it affects their daily activities “a lot”. So by his final number, I have an 0.4% chance of getting really terrible long COVID, conditional on getting COVID at all. My friend AcesoUnderGlass also did a writeup of this, published after I did my first-draft calculation, which seems to be thinking of this very differently, based entirely on hospitalization rates (which of course are very low in vaccinated people our age). She accordingly concludes that risk is very low. I don’t really understand her reasoning here, but I trust her a lot and am working on trying to converge with her on this. What’s my yearly risk of getting COVID if I try to live a normal life? This site says only 0.1% of vaccinated Californians have gotten COVID after their vaccination. But vaccination was pretty new when that survey was done, so we might want to take this as a per one-to-two-months estimate. That would mean a risk of 0.5 - 1 percent per year. But not all these people are living normal lives, so my risk might be higher. MicroCOVID gives me a good sense of how careful I’d have to be to stay within a risk budget of 1% COVID risk per year. When I play around with it, I think I am about 5x - 10x less careful than that, which would mean a risk of about 5%/year. This tracker suggests my area has recently had about 1 new case per thousand people per week, which would imply 5% per year. But most of those people are probably unvaccinated, so my risk would be significantly lower than that. I’m going to round all of this off to about 1% - 10% per year of getting a breakthrough COVID case (though obviously this could change if the national picture got better or worse). Combined with the 0.4% to 6.25% risk of getting terrible long COVID conditional on getting COVID, that’s between a 1/150 - 1/25,000 chance of terrible long COVID per year. How does this compare to other risks? My ordinary risk of death per year, just from being a man in his 30s, is about 1/700 (though this includes drug abusers and stunt pilots, so my real risk might be lower, let’s say 1/1000). Here are some other risks, courtesy of the BMJ: In this context, I find the 1/150 risk pretty scary and the 1/25,000 risk not scary at all, so, darn, I guess there’s not yet enough data to have a strong sense of how concerned I should be. 9. This is hard to compare to other postviral syndromes Going into this, I wondered if we might be able to ignore Long COVID. The argument would go like this: all viral diseases have a risk of postviral syndromes. Colds, flus, mono, lots of stuff that’s going around all the time. Lots of people get those postviral syndromes, and either recover or don’t, but either way we don’t make a big deal out of it. Since COVID’s considered “newsworthy” in a way flu isn’t, we obsess over its postviral syndrome even though it’s no worse than anything else’s. This wouldn’t make Long COVID any less bad, and maybe we would be wrong to not panic more about colds and the flu, but it would at least give us some context and make things feel less scary. Unfortunately, I can’t find anything supporting or opposing this picture. The only relevant study is a meta-analysis by Poole-Wright et al, who (contra nominative determinism) don’t pool the studies by condition, which makes it hard to draw conclusions. I think all of their examples of postviral syndrome after flu are from severe hospitalized cases, so any comparison with COVID would be unfair. Although there do seem to be scattered reports of post-flu problems, they’ve never been formally studied or quantified. Mononucleosis is an infectious disease caused by the Epstein-Barr virus, affecting about 1/2000 people per year in developed countries. It has a famously nasty postviral syndrome, which this paper describes as “almost one-half of the group had substantial ongoing symptoms 2 months after onset and… ∼10% had disabling symptoms marked by fatigue lasting ≥ 6 months”. Flu is as common as COVID, but nobody really talks about it having a significant postviral syndrome so probably it’s not that bad. Mono has a worse postviral syndrome than COVID, but it’s rare enough that it doesn’t cause massive society-wide effects. COVID is right in the middle: more common than mono, and (probably) worse postviral syndrome than flu. I think it’s fair to say that we may not have encountered a condition with this exact combination of risk factors and can’t dismiss it as similar to conditions we currently ignore. One potential analogue might be the Spanish Flu of 1918. It was an equally widespread pandemic, and seemed to have some kind of postviral syndrome. From TIME: In what is now Tanzania, to the north, post-viral syndrome has been blamed for triggering the worst famine in a century—the so-called “famine of corms”—after debilitating lethargy prevented flu survivors from planting when the rains came at the end of 1918. “Agriculture suffered particular disruption because, not only did the epidemic coincide with the planting season in some parts of the country, but in others it came at the time for harvesting and sheep-shearing.” Kathleen Brant, who lived on a farm in Taranaki, New Zealand, told Rice, the historian, about the “legion” problems farmers in her district encountered following the pandemic, even though all patients survived: “The effects of loss of production were felt for a long time.” The 1918 flu seemed to have lots of psychiatric effects: “Norwegian demographer Svenn-Erik Mamelund provided such evidence when he combed the records of psychiatric institutions in his country to show that the average number of admissions showed a seven-fold increase in each of the six years following the pandemic, compared to earlier, non-pandemic years.” Coronavirus doesn’t - the excellent Amin-Chowdhury study above finds nothing. Still, this is the scale of thing I’m worried about. The worst case scenario here is really really bad. If a few percent of COVID patients get long-term unremitting genuine CFS/ME, that has the potential to overwhelm government welfare budgets and long-term depress the economy. I think there’s a 90% chance the real situation isn’t that bad, but it’s scary that we can’t entirely rule it out. Aside from the somewhat different 1918 case, I don’t think we have any historical experience of dealing with postviral syndromes at this scale. The medium case scenario is something more like “a few percent of infected people get moderate fatigue, which doesn’t really prevent them from working, and goes away after a few years”. I don’t know whether the level of media attention paid to this would converge on “boring and nobody notices” or “giant disaster”, and I think it would be compatible with either. 10. Conclusions 1. Long COVID is many different issues without a common mechanism. 2. Some of these are straightforward and not surprising, eg lung scarring and post-ICU syndrome from severe infection, and would happen in any disease of this severity. Others seem to be more like the poorly-understood postviral syndromes associated with several other diseases. While some symptoms may be psychosomatic, most are probably organic. 3 The three major categories of symptoms are straightforward cardiovascular-pulmonary issues, straightforward smell and taste issues, and more mysterious neurological issues. 4 Although these get better with time in some people, in a significant number (maybe ~50% of people who had them at six weeks) they persist for as long as anyone has been able to measure them (a few months in the case of COVID, a year or two in the case of comparable syndromes). 5. Post-COVID fatigue is particularly concerning. This would be very bad if we analogized it to CFS/ME, and still pretty bad if we analogized it to other known postviral syndromes. There is no proof that this always gets better over the long term, although no study has looked at them for more than a few years. Facing postviral fatigue on this scale is a new problem. 6 . Children probably get Long COVID less than adults, probably at a rate of less than 5% of symptomatic cases. But we don’t know how much less, and we can’t rule out that some children get pretty severe symptoms. 7. Although vaccination decreases the risk of symptomatic COVID, it probably doesn’t decrease the risk of Long COVID per symptomatic COVID case by very much, though it might decrease it by a factor of 2-3. 8. Your chance of really bad debilitating lifelong Long COVID, conditional on getting COVID, is probably somewhere between a few tenths of a percent, and a few percent. Your chance per year of getting it by living a normal lifestyle depends on what you consider a normal lifestyle and on the future course of the pandemic. For me, under reasonable assumptions, it’s probably well below one percent. EDIT: Here are some other people who tried to do this same analysis. I learned about all of these after I wrote the first draft of this, so you can consider the basic thought process here to be independent of them - but I edited some things to account for what I learned from them before writing the final version. AcesoUnderGlass: Long COVID Is Not Necessarily Your Biggest Problem
Inline links: Lizardman, this Australian study, these people, The Dubious Origins Of Long COVID, weird x-rays, Sniffin Sticks test, somewhere between 60 and 80%, somewhere between 2x and 4x more likely, for example, the creepy shadow war between mother and fetus, Bryan Caplan, wrote, added, Preliminary Evidence On Long COVID In Children, Blankenburg et al, one of them, English team says, Here’s, the scientific paper font, This NEJM study, infection severity is linked to Long COVID risk, this study, vaccines can help relieve symptoms of existing long COVID, this essay, a writeup of this, This site says, MicroCOVID, This tracker, the BMJ, https://substackcdn.com/image/fetch/$s_!yL40!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F1eea7cfa-df08-4c67-acf2-da24ce860ae3_713x373.png, a meta-analysis by Poole-Wright et al, scattered reports, this paper, postviral syndrome
Ivermectin: Much More Than You Wanted To Know
November 17, 2021 · Original source
On the one hand, I have immense contempt for ivmmeta for letting all those other awful studies pass and then pulling out all the stops to try to nitpick this one. I have no idea if their proposed randomization failure really happened. And no doubt the reason they’re even able to investigate this is that this study is really careful and transparent - most of them don’t tell you anything about their randomization method. I would be shocked if other studies don’t have all these problems and worse. On the other hand, the point isn’t to be fair, it’s to be right. And this is a potential confounder. Not a huge one. But a potential one. I guess all we can do is try to bound the damage. Even if the confounding is 100% real and bad, there’s no way to make this study consistent with the crazy super-pro-ivermectin results of studies like Espitia-Hernandez and Aref. And even if we deny any confounding, we see the same slight pro-ivermectin trend - 86 hospitalizations vs. 95 - that we’ve seen in so many other studies. Nothing is going to make me believe that this isn’t in the top 33% of studies we’ve been looking at, so let’s add it as grist for the meta-analysis (though maybe not quite as much grist as its vast size indicates) and move on, angrily. Buonfrate et al: An Italian RCT. Patients were randomized into low-dose ivermectin (32), placebo (29), or high-dose ivermectin (32). Primary outcome was viral load on day 7. There was no significant difference (average of 2 in ivermectin groups, 2.2 in placebo group). They admit that they failed to reach the planned sample size, but did a calculation to show that even if they had, the trial could not have returned a positive result. Clinically, an average of 2 patients were hospitalized in each of the ivermectin arms, compared to 0 in the placebo arm - which bucks our previously-very-constant pro-ivermectin trend. Mayer et al: Not an RCT. Patients in an Argentine province were offered the opportunity to try ivermectin; 3266 said yes and become the experimental group, 17966 said no and became the control group. There were many obvious differences between the groups, but they all seemed to handicap ivermectin. There was a nonsignificant trend toward less hospitalization and significantly less mortality (1.5% vs. 2.1%, p = 0.03). While looking into this study, I learned the term “immortal time bias”. This means a period in between selection for the study and the beginning of study recording where patient outcomes are not counted. I think the problem here is that if you signed up for the system on Day X, and if you got sick before they could give you ivermectin, you were in the control group. See this Twitter thread, I have not confirmed everything he says. This only hardens my resolve to stay away from non-RCTs. Borody et al: Our last paper! …is it a paper? I can’t find it published anywhere. It mostly seems to be on news sites. Doesn’t look peer-reviewed. And it starts with “Note that views expressed in this opinion article are the writer’s personal views”. Whatever. 600 Australians were treated with ivermectin, doxycycline, and zinc. The article compares this to an “equivalent control group” made of “contemporary infected subjects in Australia obtained from published Covid Tracking Data”; this is not how you control group, @#!% you. Then it gets excited about the fact that most patients had better symptoms at the end of the ten-day study period than the beginning (untreated COVID resolves in about ten days). Why are these people wasting my time with this? Let’s move on. The Analysis If we remove all fraudulent and methodologically unsound studies from the table above, we end up with this: Gideon Meyerowitz-Katz, who investigated many of the studies above for fraud, tried a similar exercise. I learned about his halfway through, couldn’t help seeing it briefly, but tried to avoid remembering it or using it when generating mine (also, I did take the result of his fraud investigations into account), so they should be considered not quite independent efforts. His looks like this: He nixed Chowdhury, Babaloba, Ghauri, Faisal, and Aref, but kept Szenta Fonseca, Biber (?), and Mayer. There was correlation of 0.45, which I guess is okay. I asked him about his decision-making, and he listed a combination of serious statistical errors and small red flags adding up. I was pretty uncomfortable with most of these studies myself, so I will err on the side of severity, and remove all studies that either I or Meyerowitz-Katz disliked. We end up with the following short list: We’ve gone from 29 studies to 11, getting rid of 18 along the way. For the record, we eliminated 2/19 for fraud, 1/19 for severe preregistration violations, 10 for methodological problems, and 6 because Meyerowitz-Katz was suspicious of them. …but honestly this table still looks pretty good for ivermectin, doesn’t it? Still lots of big green boxes. Meyerowitz-Katz accuses ivmmeta of cherry-picking what statistic to use for their forest plot. That is, if a study measures ten outcomes, they sometimes take the most pro-ivermectin outcome. Ivmmeta.com counters that they used a consistent and reasonable (if complicated) process for choosing their outcome of focus, that being: If studies report multiple kinds of effects then the most serious outcome is used in calculations for that study. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. I’m having trouble judging this, partly because Meyerowitz-Katz says ivmmeta has corrected some earlier mistakes, and partly because there really is some reasonable debate over how to judge studies with lots of complicated endpoints. By this point I had completely forgotten what ivmmeta did, so I independently coded all 11 remaining studies following something in between my best understanding of their procedure and what I considered common sense. The only exception was that when the most severe outcome was measured in something other than patients (ie average number of virus copies per patient), I defaulted to one that was measured in patients instead, to keep everything with the same denominator. My results mostly matched ivmmeta’s, with one or two exceptions that I think are within the scope of argument or related to my minor deviations from their protocol. Placebo vs. ivermectin groups sometimes differed in size, which I’ve adjusted for and rounded off. Probably I’m forgetting some reason I can’t just do simple summary statistics to this, but whatever. It is p = 0.15, not significant. This is maybe unfair, because there aren’t a lot of deaths in the sample, so by focusing on death rather than more common outcomes we’re pointlessly throwing away sample size. What happens if I unprincipledly pick whatever I think the most reasonable outcome to use from each study is? I’ve chosen “most reasonable” as a balance between “is the most severe” and “has a lot of data points”: Now it’s p = 0.04, seemingly significant, but I had to make some unprincipled decisions to get there. I don’t think I specifically replaced negative findings with positive ones, but I can’t prove that even to myself, let alone to you. [UPDATE 5/31/22: A reader writes in to tell me that the t-test I used above is overly simplistic. A Dersimonian-Laird test is more appropriate for meta-analysis, and would have given 0.03 and 0.005 on the first and second analysis, where I got 0.15 and 0.04. This significantly strengthens the apparent benefit of ivermectin from ‘debatable’ to ‘clear’. I discuss some reasons below why I am not convinced by this apparent benefit.] (how come I’m finding a bunch of things on the edge of significance, but the original ivmmeta site found a lot of extremely significant things? Because they combined ratios, such that “one death in placebo, zero in ivermectin” looked like a nigh-infinite benefit for ivermectin, whereas I’m combining raw numbers. Possibly my way is statistically illegitimate for some reason, but I’m just trying to get a rough estimate of how convinced to be) So we are stuck somewhere between “nonsignificant trend in favor” and “maybe-significant trend in favor, after throwing out some best practices”. This is normally where I would compare my results to those of other meta-analyses made by real professionals. But when I look at them, they all include studies later found to be fake, like Elgazzar, and unsurprisingly come up with wildly positive conclusions. There are about six in this category. One of them later revised their results to exclude Elgazzar and still found strong efficacy for ivermectin, but they still included Niaee and some other dubious studies. The only meta-analysis that doesn’t make these mistakes is Popp (a Cochrane review), which is from before Elgazzar was found to be fraudulent, but coincidentally excludes it for other reasons. It also excludes a lot of good studies like Mahmud and Ravakirti because they give patients other things like HCQ and azithromycin - I chose to include them, because I don’t think they either work or have especially bad side effects, so they’re basically placebo - but Cochrane is always harsh like this. They end up with a point estimate where ivermectin cuts mortality by 40% - but say the confidence intervals are too wide to draw any conclusion. I think this basically agrees with my analyses above - the trends really are in ivermectin’s favor, but once you eliminate all the questionable studies there are too few studies left to have enough statistical power to reach significance. Except that everyone is still focusing on deaths and hospitalizations just because they’re flashy. Mahmud et al, which everyone agrees is a great study, found that ivermectin decreased days until clinical recovery, p = 0.003? So what do you do? This is one of the toughest questions in medicine. It comes up again and again. You have some drug. You read some studies. Again and again, more people are surviving (or avoiding complications) when they get the drug. It’s a pattern strong enough to common-sensically notice. But there isn’t an undeniable, unbreachable fortress of evidence. The drug is really safe and doesn’t have a lot of side effects. So do you give it to your patients? Do you take it yourself? Here this question is especially tough, because, uh, if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout “horse dewormer!” at you and compare you to Josef Mengele. But good doctors aren’t supposed to care about such things. Your only goal is to save your patient. Nothing else matters. I am telling you that Mahmud et al is a good study and it got p = 0.003 in favor of ivermectin. You can take the blue pill, and stay a decent respectable member of society. Or you can take the horse dewormer pill, and see where you end up. In a second, I’ll tell you my answer. But you won’t always have me to answer questions like this, and it might be morally edifying to observe your thought process in situations like this. So take a second, and meet me on the other side of the next section heading. … … … … … The Synthesis Hopefully you learned something interesting about yourself there. But my answer is: worms! As several doctors and researchers have pointed out (h/t especially Avi Bitterman and David Boulware), the most impressive studies come from places that are teeming with worms. Mahmud from Bangladesh, Ravakirti from East India, Lopez-Medina from Colombia, etc. Here’s the prevalence of roundworm infections by country (source). But alongside roundworms, there are threadworms, hookworms, blood flukes, liver flukes, nematodes, trematodes, all sorts of worms. Add them all up and somewhere between half and a quarter of people in the developing world have at least one parasitic worm in their body. Being full of worms may impact your ability to fight coronavirus. Gluchowska et al write: Helminth [ie worm] infections are among the most common infectious diseases. Bradbury et al. highlight the possible negative interactions between helminth infection and COVID-19 severity in helminth-endemic regions and note that alterations in the gut microbiome associated with helminth infection appear to have systemic immunomodulatory effects. It has also been proposed that helminth co-infection may increase the morbidity and mortality of COVID-19, because the immune system cannot efficiently respond to the virus; in addition, vaccines will be less effective for these patients, but treatment and prevention of helminth infections might reduce the negative effect of COVID-19. During millennia of parasite-host coevolution helminths evolved mechanisms suppressing the host immune responses, which may mitigate vaccine efficacy and increase severity of other infectious diseases. Treatment of worm infections might reduce the negative effect of COVID-19! And ivermectin is a deworming drug! You can see where this is going… The most relevant species of worm here is the roundworm Strongyloides stercoralis. Among the commonest treatments for COVID-19 is corticosteroids, a type of immunosuppresant drug. The types of immune responses it suppresses do more harm than good in coronavirus, so turning them off limits collateral damage and makes patients better on net. But these are also the types of immune responses that control Strongyloides. If you turn them off even very briefly, the worms multiply out of control, you get what’s called “Strongyloides hyperinfection”, and pretty often you die. According to the WHO: The current COVID-19 pandemic serves to highlight the risk of using systemic corticosteroids and, to a lesser extent, other immunosuppressive therapy, in populations with significant risk of underlying strongyloidiasis. Cases of strongyloidiasis hyperinfection in the setting of corticosteroid use as COVID-19 therapy have been described and draw attention to the necessity of addressing the risk of iatrogenic strongyloidiasis hyperinfection syndrome in infected individuals prior to corticosteroid administration. Although this has gained importance in the midst of a pandemic where corticosteroids are one of few therapies shown to improve mortality, its relevance is much broader given that corticosteroids and other immunosuppressive therapies have become increasingly common in treatment of chronic diseases (e.g. asthma or certain rheumatologic conditions). So you need to “address the risk” of strongyloides infection during COVID treatment in roundworm-endemic areas. And how might you address this, WHO? Treatment of chronic strongyloidiasis with ivermectin 200 µg/kg per day orally x 1-2 days is considered safe with potential contraindications including possible Loa loa infection (endemic in West and Central Africa), pregnancy, and weight <15kg. Given ivermectin’s safety profile, the United States has utilized presumptive treatment with ivermectin for strongyloidiasis in refugees resettling from endemic areas, and both Canada and the European Centre for Disease Prevention and Control have issued guidance on presumptive treatment to avoid hyperinfection in at risk populations. Screening and treatment, or where not available, addition of ivermectin to mass drug administration programs should be studied and considered. This is serious and common enough that, if you’re not going to screen for it, it might be worth “add[ing] ivermectin to mass drug administration programs” in affected areas! Dr. Avi Bitterman carries the hypothesis to the finish line: First two images are with all relevant studies; second two are a sensitivity analysis that removes some of the most dubious. The good ivermectin trials in areas with low Strongyloides prevalence, like Vallejos in Argentina, are mostly negative. The good ivermectin trials in areas with high Strongyloides prevalence, like Mahmud in Bangladesh, are mostly positive. Worms can’t explain the viral positivity outcomes (ie PCR), but Dr. Bitterman suggests that once you remove low quality trials and worm-related results, the rest looks like simple publication bias: This is still just a possibility. Maybe I’m over-focusing too hard on a couple positive results and this will all turn out to be nothing. Or who knows, maybe ivermectin does work against COVID a little - although it would have to be very little, fading to not at all in temperate worm-free countries. But this theory feels right to me. It feels right to me because it’s the most troll-ish possible solution. Everybody was wrong! The people who called it a miracle drug against COVID were wrong. The people who dismissed all the studies because they F@#king Love Science were wrong. Ivmmeta.com was wrong. Gideon Meyerowitz-Katz was…well, he was right, actually, I got the worm-related meta-analysis graphic above from his Twitter timeline. Still, an excellent troll. Also, the best part is that I ignorantly asked, in my description of Mahmud et al above: And it was! It was a fluke! A literal, physical, fluke! For my whole life, God has been placing terrible puns in my path to irritate me, and this would be the worst one ever! So it has to be true! The Scientific Takeaway About ten years ago, when the replication crisis started, we learned a certain set of tools for examining studies. Check for selection bias. Distrust “adjusting for confounders”. Check for p-hacking and forking paths. Make teams preregister their analyses. Do forest plots to find publication bias. Stop accepting p-values of 0.049. Wait for replications. Trust reviews and meta-analyses, instead of individual small studies. These were good tools. Having them was infinitely better than not having them. But even in 2014, I was writing about how many bad studies seemed to slip through the cracks even when we pushed this toolbox to its limits. We needed new tools. I think the methods that Meyerowitz-Katz, Sheldrake, Heathers, Brown, Lawrence and others brought to the limelight this year are some of the new tools we were waiting for. Part of this new toolset is to check for fraud. About 10 - 15% of the seemingly-good studies on ivermectin ended up extremely suspicious for fraud. Elgazzar, Carvallo, Niaee, Cadegiani, Samaha. There are ways to check for this even when you don’t have the raw data. Like: The Carlisle-Stouffer-Fisher method: Check some large group of comparisons, usually the Table 1 of an RCT where they compare the demographic characteristics of the control and experimental groups, for reasonable p-values. Real data will have p-values all over the map; one in every ten comparisons will have a p-value of 0.1 or less. Fakers seem bad at this and usually give everything a nice safe p-value like 0.8 or 0.9.
Inline links: Buonfrate et al:, Mayer et al:, immortal time bias, this Twitter thread, Borody et al:, https://substackcdn.com/image/fetch/$s_!Wpjs!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F2d8a451b-b1fc-44e5-ae67-b1506e491762_914x657.png, https://substackcdn.com/image/fetch/$s_!DOjA!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F17d5827a-38da-4a99-beb3-c3018df5c633_920x604.png, https://substackcdn.com/image/fetch/$s_!GX1n!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fc692fec8-a450-4579-b337-c72bec060970_912x298.png, https://substackcdn.com/image/fetch/$s_!YcH4!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ff36db98e-e653-44da-906c-20312b1689a3_468x205.png, https://substackcdn.com/image/fetch/$s_!jbcL!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fd189a844-daf2-4199-bb2e-830d4fc64415_468x206.png, later revised their results to exclude Elgazzar, Popp, https://substackcdn.com/image/fetch/$s_!2B6r!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F505c5ac4-3fe8-47a4-8505-dab80601b44d_416x198.png, Avi Bitterman, David Boulware, https://substackcdn.com/image/fetch/$s_!JWWh!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fac9e4f34-f9cc-40f2-9d83-da4e7178fad7_772x330.png, source, Gluchowska et al, the WHO, carries, https://substackcdn.com/image/fetch/$s_!xExE!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F5da21781-249c-4e59-b616-9f23d83cc044_2048x1184.jpeg, https://substackcdn.com/image/fetch/$s_!4SMr!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fdcd6e4b2-37f7-4602-93d5-2581c3b27a60_700x432.png, https://substackcdn.com/image/fetch/$s_!-6n2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F7fd6e8f4-093e-4e02-bce7-363615146c9c_2228x1346.jpeg, https://substackcdn.com/image/fetch/$s_!CPZs!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fb0425847-198a-4bd3-a63b-149f15d147ba_700x432.png, https://substackcdn.com/image/fetch/$s_!H3rK!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F9972491b-25b0-4c06-8aca-86fce102ae63_666x147.png, even in 2014, The Carlisle-Stouffer-Fisher method
Source. Real data would follow something like a bell curve. This is going to require a social norm of always sharing data. Even better, journals should require the raw data before they publish anything, and should make it available on their website. People are going to fight hard against this, partly because it’s annoying and partly because of (imho exaggerated) patient privacy related concerns. Somebody’s going to try make some kind of gated thing where you have to prove you have a PhD and a “legitimate cause” before you can access the data, and that person should be fought tooth and nail (some of the “data detectives” who figured out the ivermectin study didn’t have advanced degrees). I want a world where “I did a study, but I can’t show you the data” should be taken as seriously as “I determined P = NP, but I can’t show you the proof.” The second reason I think this, aside from checking for fraud, is checking for mistakes. I have no proof this was involved in ivermectin in particular. But I’ve been surprised how often it comes up when I talk to scientists. Someone in their field got a shocking result, everyone looked over the study really hard and couldn’t find any methodological problems, there’s no evidence of fraud, so do you accept it? A lot of times instead I hear people say “I assume they made a coding error”. I believe them, because I have made a bunch of stupid errors. Sometimes you make the errors for me - an early draft of this post of mine stated that there was an strong positive effect of assortative mating on autism, but when I double-checked it was entirely due to some idiot who filled out the survey and claimed to have 99999 autistic children. In this very essay, I almost said that a set of ivermectin studies showed a positive result because I was reading the number for whether two lists were correlated rather than whether a paired-samples t-test on the lists was significant. I think lots of studies make these kinds of errors. But even if it’s only 1%, these will make up much more than 1% of published studies, and much more than 1% of important ground-breaking published studies, because correct studies can only prove true things, but false studies can prove arbitrarily interesting hypotheses (did you know there was an increase in the suicide rate on days that Donald Trump tweeted?!?) and those are the ones that will get published and become famous. So if the lesson of the original replication crisis was “read the methodology” and “read the preregistration document”, this year’s lesson is “read the raw data”. Which is a bit more of an ask. Especially since most studies don’t make it available. The Sociological Takeaway I’ve been thinking about this one a lot too. Ivermectin supporters were really wrong. I enjoy the idea of a cosmic joke where ivermectin sort of works in some senses in some areas. But the things people were claiming - that ivermectin has a 100% success rate, that you don’t need to take the vaccine because you can just take ivermectin instead, etc - have been untenable not just since the big negative trials came out this summer, but even by the standards of the early positive trials. Mahmud et al was big and positive and exciting, but it showed that ivermectin patients recovered in about 7 days on average instead of 9. I think the conventional wisdom - that the most extreme ivermectin supporters were mostly gullible rubes who were bamboozled by pseudoscience - was basically accurate. Mainstream medicine has reacted with slogans like “believe Science”. I don’t know if those kinds of slogans ever help, but they’re especially unhelpful here. A quick look at ivermectin supporters shows their problem is they believed Science too much. @jonno_bosch I work in hospitality so I need things to return to normal ASAP. I am using Ivermectin as a prophylactic. Hugely influenced by Carvallo trail and Chala trail which showed huge protection","username":"Bannisterious","name":"Andrew Bannister","profile_image_url":"","date":"Fri Feb 12 16:21:14 +0000 2021","photos":[],"quoted_tweet":{},"reply_count":0,"retweet_count":0,"like_count":0,"impression_count":0,"expanded_url":{},"video_url":null,"belowTheFold":true}" data-component-name="Twitter2ToDOM"> @mtskullcrusher @HereComeTheJud @therealjosexy @joeycadre @PeegeRiley @dcwickedestcity @blaireerskine Read Raad. Or Mahmud. Or ICON study from Florida. Or Mexico City hospitalizations study. Or Niaee. Or...\n\nOr just type \"ivermectin covid\" in Google Scholar and read.","username":"fatlas6","name":"fatlas","profile_image_url":"","date":"Thu Sep 02 21:34:59 +0000 2021","photos":[],"quoted_tweet":{},"reply_count":0,"retweet_count":0,"like_count":1,"impression_count":0,"expanded_url":{},"video_url":null,"belowTheFold":true}" data-component-name="Twitter2ToDOM"> They have a very reasonable-sounding belief, which is that if dozens of studies all say a drug works really well, then it probably works really well. When they see dozens of studies saying a drug works really well, and the elites saying “no don’t take it!”, their extremely natural conclusion is that it works really well but the elites are covering it up. Sometimes these people even have a specific theory for why elites are covering up ivermectin, like that pharma companies want you to use more expensive patented drugs instead. This theory is extremely plausible. Pharma companies are always trying to convince people to use expensive patented drugs instead of equally good generic alternatives. Ivermectin believers probably heard about this from the many, many good articles by responsible news outlets, discussing the many, many times pharma companies have tried to trick people into using more expensive patented medications. Like this ACSH article about Nexium. Or my article on esketamine. Given that dozens of studies said a drug worked, and elites continued to deny it worked, and there are well-known times where elites lie about drugs in order to make money, it was an incredibly reasonable inference that this was one of those times. If you have a lot of experience with pharma, you know who lies and who doesn’t, and you know what lies they’re willing to tell and which ones they shrink back from. As far as I know, no reputable scientist has ever come out and said ‘esketamine definitely works better than regular ketamine’. The regulatory system just heavily implied it. I claim that with ivermectin, even the people who don’t usually lie were saying it was ineffective, and they were saying it more directly and decisively than liars usually do. But most people can’t translate Pharma → English fluently enough to know where the space of “things people routinely lie about and nobody worries about it too much” ends. So they incredibly reasonably assume anything could be a lie. And if you don’t know which statements about pharmaceuticals are lies, “the one that has dozens of studies contradicting it” is a pretty good heuristic! If you tell these people to “believe Science”, you will just worsen the problem where they trust dozens of scientific studies done by scientists using the scientific method over the pronouncements of the CDC or whoever. So “believe experts”? That would have been better advice in this case. But the experts have beclowned themselves again and again throughout this pandemic, from the first stirrings of “anyone who worries about coronavirus reaching the US is dog-whistling anti-Chinese racism”, to the Surgeon-General tweeting “Don’t wear a face mask”, to government campaigns focusing entirely on hand-washing (HEPA filters? What are those?) Not only would a recommendation to trust experts be misleading, I don’t even think you could make it work. People would notice how often the experts were wrong, and your public awareness campaign would come to naught. But also: one of the data detectives who exposed some fraudulent ivermectin papers was a medical student, which puts him somewhere between pond scum and hookworms on the Medical Establishment Totem Pole. Some of the people whose studies he helped sink were distinguished Professors of Medicine and heads of Health Institutes. If anyone interprets “trust experts” as “mere medical students must not publicly challenge heads of Health Institutes”, then we’ve accidentally thrown the fundamental principle of science out with the bathwater. But Pierre Kory, spiritual leader of the Ivermectin Jihad, is a distinguished critical care doctor. What heuristic tells us “Medical students should be allowed to publicly challenge heads of Health Institutes” but not “Distinguished critical care doctors should be allowed to publicly challenge the CDC”? Then what about “believe statisticians”? I’ve never heard anyone propose this before, but re-centering the mystique of scientific-expertise in study-analyzers and study-aggregators rather than object-level scientists is…one way you could go, I guess. Statisticians admittedly sort of failed us here: the first several meta-analyses said ivermectin worked. But the statistical process - the idea that studies are raw materials, but it takes skill to turn them into the finished good of scientific knowledge - sort of comes out looking good. If we need to summarize our takeaway in a slogan of exactly two words, one of which is “trust”, you could do worse than this one. (am I secretly suggesting that we make rationality higher status? Maybe, although rationalists did no better here during the early phase of “looks promising so far” than anyone else, and it was researchers digging into the nitty-gritty of the data who really solved this.) Or maybe this is the wrong level on which to think about this. Maybe there isn’t and can’t be a simple heuristic you can teach everyone in school or via a PR campaign which will lead to them having making good health decisions in an adversarial information environment, without having any negative effects anywhere else. But you also don’t want people to make bad health decisions. So what do you do? The Political Takeaway All of this is complicated by the impression many people (including me) have, that ivermectin boosterism and vaccine denialism are closely linked. The ivermectin evidence is complicated. There’s room for doubt. I can maybe see room for doubt on some marginal vaccine-related issues like how seriously to take the occasional reports of myocarditis in teens. But the basic issue - that the vaccine works really well and is incredibly safe for adults - seems beyond question. Yet people keep questioning it. I think it’s important to address ivermectin support on its own terms - as a potentially plausible scientific theory in a debris field of confusing evidence, which should be debated to the usual standards of scientific debate. I’ve tried to do that above. But this picture wouldn’t be complete without acknowledging the overlap with vaccine denial - a segment of people who are completely crazy and wrong and who happen to have fixated on this mildly interesting question as opposed to some other one with even less evidence. I’ve been trying to figure out a model where ivermectin support and vaccine denialism both make visceral sense to me, and here’s what I’ve got: Imagine that in 2025, an alien invasion fleet reaches Earth. But it got hit by a supernova on the way, the spaceships are partly disabled, and they’re only able to conquer some out-of-the-way place - let’s say Australia. There’s a few cycles of conflict and cease-fire, a few cities get nuked, and finally we settle into an uneasy peace. Over the next few years, humanity grudgingly admits the invaders into the world community. They get a seat in the United Nations. We sort of cooperate with them on projects that are important to both sides, like stopping climate change. We still hate them, but only at the level of ordinary international rivalries, like USA/USSR. In 2035, the aliens announce that a quantum memetic plague from the Andromeda Sector has reached Earth. Billions of people will die unless we let them put an immunity-granting cybernetic implant in all humans’ brain. The aliens admit we haven’t always been friends, and honestly they would still like to conquer us someday. But this plague is an ancient enemy of all sentient beings, they dealt with it on their homeworld eons ago, and they want to help us out here. Humans apparently don’t have the ability to detect quantum memetic plagues, but mortality rates for over-65s do seem weirdly high this year, something like 10x worse than a normal flu season. Do you let the aliens put an implant in your brain, or not? If it helps, the aliens look like this. Surely anyone with a brain that size must know what they’re talking about, right? (source) Fine, you don’t have to decide immediately. The brain implants aren’t even ready yet. Some human scientists suggest wearing face masks in the interim. The aliens say no, that will never work, that’s not how you deal with quantum memetic plagues, if you do anything other than wait for the brain implants you’re anti-science idiots who are wasting precious time and will kill millions of people. Human nations try face masks anyway…and they clearly and conspicuously work. The aliens say whatever, we’re still the advanced spacefaring civilization here, maybe it works for humans but that’s not the point, the point is you’ve got to let us put implants in your brains. Some human scientists suggest reopening vital services. The aliens say no, millions will die, this is “mass human sacrifice”, humans apparently must care nothing about their families’ lives. The humans try reopening anyway, and…it goes kind of okay? Maybe the death rate goes up 10% to 20% or so, hard to say? The aliens say whatever, maybe their calculations were off by a few orders of magnitude, the point is, you have to let us put implants in your brain or you’ll all die. Then some human scientists suggest vaccinating against the plague. The aliens say this is idiotic, vaccines originally come from cowpox, even the word “vaccine” comes from Latin vaccus meaning “cow”, are you saying you want cow medicine instead of actual brain implants which alien Science has proven will work? They make lots of cartoons displaying humans who want vaccines as having cow heads, or rolling around in cow poop. Meanwhile, the first few dozen studies show vaccines work great. Many top human leaders, including war heroes from the struggle against the aliens, get vaccines and are seen going out in public, looking healthy and happy. The aliens say that human science is hopelessly flawed because of complicated statistical concepts that inferior life forms like us don’t even have words for. You need to ignore all the studies and meta-analyses showing that vaccines definitely work, and let the aliens give you brain implants instead. So do you let the aliens put an implant in your brain, or not? Obviously you think long and hard before doing this. And obviously this is an extended metaphor for vaccine denialism. So what’s the difference between the metaphor (where you’re presumably anti-implant) and the real world (where you’re presumably pro-vaccine?) For me, it’s a combination of: The aliens are hostile, so I don’t trust them no matter how smart they are
Inline links: Source, this post, this ACSH article about Nexium, my article on esketamine, https://substackcdn.com/image/fetch/$s_!1UIV!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F598d7d6c-de95-4b94-b13a-3c8a452b0409.jp2, source
Book Review: Lifespan
December 02, 2021 · Original source
Sinclair thinks self-evidently yes. He tells the story of his grandmother - a Hungarian Jew who fled to Australia to escape communist oppression. She was adventurous, “young at heart”, and “she did her damnedest to live life with the spirit and awe of a child”. Sinclair remembers her as a happy person and free spirit who was always there for him and his family during their childhood in the Australian outback.
Inline links: Hungarian Jew
It was a quick death, thankfully, caused by a buildup of liquid in her remaining lung. We had just been laughing together about the eulogy I’d written on the trip from the United States to Australia, and then suddenly she was writhing on the bed, sucking for air that couldn’t satisfy her body’s demand for oxygen, staring at us with desperation in her eyes.
Does Georgism Work? Part 1: Is Land Really A Big Deal?
December 09, 2021 · Original source
For context, Terrence Dwyer is a Georgist who spent several years as an Australian Treasury tax official, was an advisor to the Prime Minister and Cabinet, and has written extensively about tax policy. His paper is called The Taxable Capacity of Australian Land and Resources.
Inline links: The Taxable Capacity of Australian Land and Resources
Unlike America, Australia has a long history of land taxation and detailed land valuation records, which Dwyer leans on to put together four tables comparing land incomes to all Australian tax receipts. Although Australia has a history of land valuation and LVT that continues to this day, they fall far short of Fully Automated Luxury Space Georgism, relying on quite a bit of conventional capital and labor taxes.
But I want to see what we can say about America, so let's check that National Income ratio real quick. In 1999, Dwyer gives land income as $132.7 billion AUD. In 1999, Macrotrends says Australian GNI was $405.5 billion USD, and, using the 1999 conversion rate, that's $623.9 billion AUD. That gives a land-rent-to-GNI ratio of 21.3%. Spot-checking 1991 gives me 20.8%, so about the same.
Inline links: Australian GNI, 1999 conversion rate
Does Georgism Work? Part 2: Can Landlords Pass Land Value Tax on to Tenants?
December 10, 2021 · Original source
He does, however, cite Mary Edwards' 1984 study and claims it says an Australian LVT had no effect on housing prices, once you control for public expenditure level.
In his case study of Australia for the same article, Hagman points to too low a rate of land tax as making it hard to see the full predicted effects borne out. Maybe a similar thing was going on in New Zealand?
Does Georgism Work, Part 3: Can Unimproved Land Value be Accurately Assessed Separately From Buildings?
December 11, 2021 · Original source
Gwartney says that when he was the assessment commissioner and chief executive officer in British Columbia, he had a staff of 690, and that this number has not changed significantly since then. British Columbia has a population of about 5 million, so that's 1 assessment officer for every 7,250 British Columbians. For context, the IRS has a staff size of 74,454, or about one IRS agent for every 4,425 Americans. I don't have data on how many property tax assessors the USA has in total, but the above slide suggests British Columbia's figure is on the high end. As for how you actually do assessments, sure, you can send out an army of assessors to value each and every property in your jurisdiction by hand. However, not only is that labor-intensive, it's also a recipe for inconsistency. Whatever method you're using to value properties needs to be consistent and standardized across all properties, so you don't have sharp discontinuities on the assessment map that are due solely to differences between Assessor Fred and Assessor Sally's personal methodologies. Thankfully, we're living in the modern age, and we have some fancy new tools at our disposal. 4. Modern Technology Georgists were doing split-rate assessments to allegedly good success long before the rise of the computer, such as J. J. Pastoriza's effort in setting up a Georgist tax regime in Houston, Texas in 1911. Today, we have spreadsheets, property value databases, GIS mapping visualizations, regression analysis, machine learning...the works. According to Gwartney, the Canadian province of British Columbia has revalued all its land and all its property on an annual basis simply by using computers and market analysis, ever since he first helped them set up their system back in 1975. Not every jurisdiction revalues their land this thoroughly and this often, but Gwartney says there is no significant technical or staffing barrier standing in the way. Gwartney has been retired for some time, so his seminar didn't cover all the latest cutting-edge techniques that have come out in the last few years. Let's look at some recent papers and see what new tools assessors have to play with. The first on my list is Land Value Appraisal Using Statistical Methods by Kolbe, Schulz, Wersing, and Werwatz (2019). This is a study on mass appraisal techniques using real estate transaction data from Berlin, Germany. It claims that not only are the results cheaper and faster to generate than those done by conventional property assessment methods, but they are also no less accurate than those done "by hand" by experts. Kolbe et al. assert that, provided you have access to high quality market transaction data, you can perform accurate and efficient mass appraisals of land values. They chose Berlin because it "has a very effective system of property transaction data collection and storage," in contrast to other parts of Germany. They cite some prior work by Almy (2014) studying Canada, the Netherlands, and the United States, suggesting that the assessment cost per property can be brought down to 20 Euros–25 times cheaper than what some other people (Fuest, et al. (2018)) assert. Given an average tax receipt of 2,000 Euros per property, this means that the assessment cost should represent only about 1% of the funds raised. Is that good? Let's take this assertion at face value for the moment and compare it to the cost of the IRS. Federal tax receipts in 2020 were $3.42 trillion, and operation costs for the IRS were $12.3 billion, or 0.36%. However, the IRS outsources most of the labor of tax preparation to the taxpayers themselves, with compliance costs estimated between $200 billion and $400 billion a year, to the delight of Intuit. Add that up and the total cost of federal tax collection to the economy is anywhere between 6-12% of the amount it raises. And what about sales tax? According to a 2006 report by PriceWaterHouseCoopers: The study finds that the national average annual state and local retail sales tax compliance cost in 2003 was 3.09 percent of sales tax collected for all retailers, 13.47 percent for small retailers, 5.20 percent for medium retailers, and 2.17 percent for large retailers So a compliance cost of 1% would be way more efficient in terms of cost collection than the other two most common forms of taxation, and taxpayers don't even have to do anything themselves, other than pay the bill. Alrighty, how about the accuracy? The authors cite two international examples, Australia and Lithuania, as among the few countries in the world that have both a Land Value Tax and statistical methods for mass appraisals. Hefferan and Boyd (2010) assert that objections to assessments from property owners in Australia are less than 1%. I'm willing to buy the improved efficiency claims just by taking a look at some methodologies. It seems reasonable that computerized records and algorithms can cut costs significantly; the real question is if you're trading off accuracy. The other papers I found on the subject are Bencure, et al (2019) in BayBay City, Philippines, Kilić, et al (2019) in Croatia, Yalpir & Unel (2017) in Konya, Turkey, and Raslanas et al. (2014) in Vilnius, Lithuania. Let's dive in and examine some methods. 5. Mass Appraisal Methods Here are some of the latest mass appraisal methods cribbed from the research papers listed above. All of these are based on taking market transaction data, plotting them out on a map, and running computations over them to estimate valuations for the properties you don't have known values for. Furthermore, all of these methods are able to value land and building values separately. Multiple Regression Analysis This paper by Yalpir and Unel out of Turkey gives a straightforward example of using Multiple Regression Analysis for land valuation. For those of you who didn't study math, let me explain regression analysis. This is a family of mathematical models where you basically take a data set, ask the question "what mathematical formula would best fit this data," choose a basic equation model, and then have a computer search for a set of coefficients that "best fit" that curve to the data with the least amount of error. The simplest example is using linear regression on a scatterplot of observed data points to fit a trend line. This is a common exercise in freshman physics and statistics classes. You can use more complicated versions of this numerical method to take a big bag of observations (real estate sales) and use "multiple regression" to tease out dependent variables (land value and improvements value) based on the independent variables (size, location, age, number of bedrooms) of your observations. In this case the team identified about a hundred different factors that can affect the price of a property: Then you create an entry for each property, fill in the values for each of those characteristics, and run it through the regressor. Take note of how many of these factors start with the words "proximity to." Each of these can be calculated automatically just by knowing where the property is on a map, and each of them is an independent contributor to the value of the property's location. The next step is to generate individual "index maps" that combine various related features into combined heat maps. Then you run everything through and see if it works. You can get the land share of the final value by combining the contributions of all the individual factors that you associate with "land," such as proximity to important things. In the verification section the authors say: As a result of the analysis, since the significance level (0.000) p <.05, corresponding to the F values in the ANOVA test, indicates that the regression analysis is appropriate and the models are significant. The criteria that make up the model account for about 85% of the market value and 15% cannot be explained for reasons such as economic, non-existent data and unearned income. Unfortunately, they don't say anything about how accurate their model is for assessing land values specifically. Otherwise, this is a pretty good example of using the Multiple Regression method for estimating the individual contributions of various factors to overall property values. Gwartney says Multiple Regression Analysis was a standard method he typically used, of which this specific paper is just one example. Nonparametric kernel regression This will be a method familiar to the programmers in the audience who have any experience with image processing algorithms. Here's an example from this old Gamasutra article: The basic idea here is to take a matrix of numbers, called a "kernel", and run that over every pixel in a source image. The kernel tells you how strongly to weight all of the source pixel's neighbors to compute a final result for that position. A simple "box blur" is a kernel where every value is 1 (meaning it averages the values of all neighboring pixels within a range). The more subtle gaussian blur illustrated above uses a two-dimensional normal distribution of values so that each pixel is most affected by those nearest to it. So let's apply the same principle to land valuations. If you have a map with lots of transaction data of pure land sales–defined as sales of either vacant land or teardown properties (where the building value is essentially zero)–then you can use a special kernel filter to smoothly interpolate land values across the region. So you basically have a smooth curve that mostly favors close-by points, tapers off a bit, and then disregards anything outside a certain distance entirely. The big assumption here is that land values change smoothly and do not change suddenly across very short distances. There are, in fact, locations with sharp jumps in value (any town with an "other side of the tracks," for instance). But for cases where we know a priori that land values change smoothly, this method is appropriate. No other prior restriction is placed on the form of the land value map, however, and this is why it's called "nonparametric." Here's an illustration. The outer box is the entire search distance that the kernel considers, and the circles represent the falloff of the curve itself. The size of the box is called the "bandwidth" and is set by the user. Everything outside of it will have zero influence on the kernel's output at any given location. This method operates on the same basic logic that I used when I hand-estimated the land value of that San Francisco house in Part I based on the value of the empty lot next door. However, it makes the whole procedure systematic. It can easily and accurately estimate the land value of a property with a big fat building on it simply by smoothly interpolating the known values of the nearby parking lots. Of course, it has limitations. First and foremost, it's a highly local operation, so if you have properties you're trying to value that don't have nearby pure land sales data, you can't really do much with this. Also, most people assume that city centers have less market transactions for undeveloped land than the countryside, as did I until I read that paper by Albouy in Part I. But in any case, this is just one method in your toolbox and might not be sufficient by itself. Its key advantage is that it works directly from true market data for land and doesn't need or want any other subjective data. In the end, basic kernel estimation just fills in the land value of unmeasured locations with a local weighted average of known locations. Nonparametric adaptive regression Kolbe, et al. build on the kernel regression method with a technique called Adaptive Weights Smoothing (AWS), which runs in several iterations and adds additional weight to any observed data points that are sufficiently close to the point being estimated. I'm not 100% sure about what all the math means, but it seems like it's basically a "smarter" version of the basic kernel method. Left: Nonparametric kernel regression, Right: Adaptive Weights Smoothing. I think the authors goofed and printed the same figure twice with different headings because they're identical if you overlay them in Photoshop. Semiparametric regression Now, the above two methods assume you have plenty of "pure" land sale records to work with. But if you're trying to work out prices in the city center, you've probably mostly got land and buildings mixed together. To do this effectively, we need more data, and this is where the "parameter" in "semiparametric" comes in. The model described in Kolbe et al. seems like a flavor of multiple regression analysis that takes the price, the location, and various characteristics of the building and feeds it into a regressor. But we've got "semi" parametric here. What does that mean? Well, if you already know how certain relationships between the data work a priori, it's better to enforce those relationships yourself rather than leave it to the computer. Here, we enforce the assumption that if two properties are right next to each other, then the value due to location is going to be essentially identical. This algorithm starts by ordering things geographically and then working out the differences in observed price by regressing on the difference between remaining property characteristics. In this method, the power of "location, location, location" is not something we're leaving to the regressor to discover by itself. Results of the Semiparametric regression method, we can see some significant differences from the simple kernel-based model. As you can see above, this gives you more detailed and likely more accurate results, and you're better able to assess the values of properties with buildings on them, even in the absence of pure land sales. This technique is more complicated and bakes in assumptions about the power of location, but otherwise doesn't assign subjective human weights to the various property characteristics. The chief human bias comes in the form of deciding which property characteristics are measured and made legible to the model in the first place. Okay great, but how accurate are the above three methods? Their main point of comparison is this thing called the "Bodenrichtwerte," or BRW. I think that means "ground-level-values" in English, and it's an expert-assessed map of land values for Berlin done the traditional way. The nonparametric kernel regression method has a correlation of 0.704 with the traditional method and has the added disadvantage that it's not able to produce estimates for the city center, only the outlying areas. Furthermore, the BRW map does show sharp discontinuities, which is another knock against the kernel method, at least for the city center. What about the iterative method? Kolbe et al. find that "the agreement between [Adaptive Weights Smoothing] land value estimates and, both, land prices and BRW land values is fairly good for all values of λ." Doing some quick checks, their values seem to be within about 85% of the BRW values. A different Kolbe et al. paper called Identifying Berlin's land value map using adaptive weights smoothing goes into more detail and claims to give "similar" values to that of the BRW. For the semiparametric method, they "found a strong positive correlation of 0.845" between their numbers and a previously expert-assessed set done using the traditional method. That sounds pretty good. It seems their margin for error is about plus or minus 15% compared to the traditional expert method. I'd like to see more direct comparisons against market transactions themselves, though, because if the prior expert assessments are wrong, then the main achievement here is improved efficiency, not accuracy. However, this method doesn't seem to be dramatically less accurate than the old way of doing things. The last three models came from the Berlin case study, where you have excellent market transaction data in an extremely wealthy and high-trust society. But what if you're trying to assess land in a developing nation with poor market transaction records, weak institutions, and widespread poverty? Innovative Land Valuation Model (iLVM) This is the particular name of the method described in Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches by Bencure, Tripathi, Miyazaki, Ninsawat, and Kim. They used BayBay City, Philippines as their case study. Whereas the previous models are very "hands-off" and let the computer work out the relationships between prices and property characteristics, here you get expert human opinion directly involved in building the model, baking in weights that directly embody judgments like "properties next to major roads are more valuable." These judgments are based on expert opinions that presumably come from observed experience but are a priori judgments nonetheless. Here, look at this big complicated flowchart. The "Analytic Hierarchy Process" in the box on the left is a particular kind of method for getting experts to set weights. The authors give this reason for using it: Despite criticism pinpointed by other scholars, the AHP remains the commonly used in many research fields and practical applications. This is because the AHP: (1) overcomes human difficulty in making simultaneous judgment among factors to be considered in the model; (2) is relatively simple as compared to other MCDA [multi-criteria decision analysis] methods; (3) is flexible to be integrated in various techniques such as programming, fuzzy logic, etc.; and (4) has the ability to check consistency in judgment After identifying a list of "factors" that can affect land value, they group them into taxonomical buckets: Note that certain factors like "Coastline" appear in multiple buckets; this captures the various influences a characteristic can have. For instance, land on the coast tends to be more economically valuable because of tourism, shipping, fishing, etc., so that goes under "economic." But land that's next to the coast is also more likely to flood, so it also goes under "environmental." And then there are various land use restrictions that apply specifically to coastal areas, so it goes under "legal" as well. In this way, a single factor like "the property is on the coastline" can have both positive and negative effects on land value (e.g., it's more economically valuable but it also might flood, and there are certain things you aren't allowed to do there). The next step is to set down some rules for how sensitive each factor is to location and distance. So here we can see that the economic benefit of being on the coast is most strongly felt if you're within half a kilometer of the ocean, but the environmental effect (e.g., risk of flooding) is most strongly felt when you're within 0.03 kilometers. And so on and so forth. Your experts help you work out all these rules. Note that for a few of these factors (such as land use and slope), you use metrics other than distance (e.g. land use classification and grade). Then you take all that stuff and assign everything a value between 0 and 5. Your team of experts then uses this table to come up with a set of weights for everything. What essentially comes out of this is a big linear equation with a bunch of coefficients for every one of your factors, which is then broadly fit to the observed market prices. When you're done, you can take any property on your list, multiply each of its characteristics by its respective weight, run that through your equation, and calculate the predicted price of the land. So how accurate is it? The authors compare it to standard Multiple Regression Analysis and claim it fares better. The Root Mean Square Error is quite a bit less than MRA. In addition, I think it's also saying that the MRA algorithm decided that only four of the factors were significant and basically ignored all the rest. By contrast, iLVM was able to maintain contributions from all the factors, because it doesn't leave that decision to the computer. I'm not 100% sure; it's not clear from the paper. The authors claim that about 67% of the variability is explained by their model, but they note that there are some areas where the model can be off by more than a factor of 1.0 in either the positive or negative direction. One thing that's kind of fun about this model is that you can make neat graphs like this that show the individual contribution of each factor: The main downside to this model is that it relies on a whole lot of subjective expert opinion and can be questioned on that basis. That said, it can be cheaply deployed in a transparent and consistent way across a large area. You can see why that's attractive for a developing nation with weak institutions and poor market transaction records; the argument is that this is a significant improvement over the former status quo. I wonder how well this model performs when you feed it better market transaction data, and how that would compare against all the others methods under identical conditions. More research is needed. Rather than drag you through a bunch more research papers, I'll just leave these others I found cited in the above studies: Killić et al. (2019) - Fuzzy expert system for land valuation in land consolidation processes
Inline links: J. J. Pastoriza's effort in setting up a Georgist tax regime in Houston, Texas in 1911, Land Value Appraisal Using Statistical Methods, Almy (2014), Fuest, et al. (2018), $12.3 billion, $200 billion, $400 billion, delight of Intuit, 2006 report by PriceWaterHouseCoopers, Hefferan and Boyd (2010), Bencure, et al (2019), Kilić, et al (2019), Yalpir & Unel (2017), Raslanas et al. (2014), https://substackcdn.com/image/fetch/$s_!stkG!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fef4c64b9-38bc-43c8-aa83-05eae3576e03_923x600.png, https://substackcdn.com/image/fetch/$s_!_9z0!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd98e114-7eb2-4566-a979-1f2f2dd27c22_701x867.png, https://substackcdn.com/image/fetch/$s_!8HN7!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ff1232522-51e0-4438-998a-b0be4615df6b_534x806.png, https://substackcdn.com/image/fetch/$s_!jFqw!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fd5836457-7642-4235-9410-00906f043428_662x357.png, Gamasutra article, https://substackcdn.com/image/fetch/$s_!foLQ!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ff74d378c-39b7-49f9-9655-8cbbf7c89ff5_592x270.png, https://substackcdn.com/image/fetch/$s_!AjnN!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F458458cc-614e-4ab3-a57b-3f28b70db6c3_458x317.png, https://substackcdn.com/image/fetch/$s_!lVcf!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F77ea4f72-0e51-4c63-b14b-15c603ac2500_901x418.png, https://substackcdn.com/image/fetch/$s_!zoSx!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fed78916c-12c5-42ca-b581-7b59aa25bbd5_757x718.png, https://substackcdn.com/image/fetch/$s_!7Wm9!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F8fe15dbb-181f-46ae-bdde-840bdd6a2064_752x735.png, https://substackcdn.com/image/fetch/$s_!zig5!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F958762f1-a425-4017-86cf-058cb3eb4d59_713x389.png, https://substackcdn.com/image/fetch/$s_!8ZGT!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ff37696ef-7a6a-48ae-8169-734b875b0b57_800x319.png, Identifying Berlin's land value map using adaptive weights smoothing, https://substackcdn.com/image/fetch/$s_!3CR3!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F0489e086-69ae-4840-b658-59fee6b3af44_2000x1672.png, https://substackcdn.com/image/fetch/$s_!fA0K!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F5d39769d-46e2-4891-92aa-cb3766068204_2000x978.png, https://substackcdn.com/image/fetch/$s_!phFK!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F5d1a519c-93d7-4bed-9577-7478fb239bca_1968x3548.png, https://substackcdn.com/image/fetch/$s_!XtLN!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59cb148-e0da-456b-b205-973e04239be7_587x647.png, https://substackcdn.com/image/fetch/$s_!My3b!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fee7b484b-3be8-4363-bcb8-1cb4fb4a7c01_661x655.png, https://substackcdn.com/image/fetch/$s_!SqQA!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F8a84b431-1250-427e-a67a-b3e2b8a3c0dd_896x623.png, https://substackcdn.com/image/fetch/$s_!qPOz!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fb71526d0-736c-45d7-ad14-36e5670f78ab_1153x881.png
Hefferan and Boyd (2010) - Property taxation and mass appraisal valuations in Australia - adapting to a new environment
Inline links: Hefferan and Boyd (2010)
See if you can improve on the state of the art. How close to ground truth can you get? Once the first study is done, you'd want to test it in another area–maybe Australia, Denmark, Germany, or the Philippines. If Georgism is true, and the only thing standing in the way is being able to pull off accurate assessments, then let's just get better at doing that. We're the species that split the atom and travelled to the moon. Surely we can handle this. 6.2. Total Land Value of the United States It's really annoying that we don't confidently know this figure, and it has huge implications for LVT policy. Technically, this is an "assessment" problem, but in practice, when you're assessing the entire USA, you're often falling back on big black-box buckets of aggregated property values rather than building a database of direct ground-truth market transactions yourself. In Part I, we saw how big the difference was between Albouy, who used pure land sales directly from the market, and Larson, who applied the cost approach to official figures. If one of you readers has MLS access for all 50 states and/or a bunch of other records, it'd be interesting to see if we could settle this debate once and for all. 6.3. A Push for More Open Real Estate Market Transaction Data To my knowledge, there's no good, one-stop shop for solid, historical, ground truth real estate market transaction data that's uniform and detailed across, say, the entire United States. I'm well aware of how important access to solid data is for researchers. I run a site called www.gamedatacrunch.com that just quietly scrapes public metrics from the PC video game store Steam (they don't mind–I asked). I'm constantly getting requests from researchers to dump slices from my DB for them, which I'm always happy to do. If not for making this data available, those research papers might not be happening. So many questions that are answerable in principle go unanswered in practice simply for want of access to data, and then smart people make bad policy decisions because of that ignorance. In principle, I suppose nothing would legally stop someone from scraping listing prices on Zillow and Redfin all day, every day, but I have a feeling I'd probably get sued if I did that. (Just checked with my lawyer; he says it's a legal grey area but probably wouldn't end well for me.) If you're an eccentric billionaire who wants to do something for Georgism, instead of building a $400 billion super city in the desert, you could buy Redfin for about 1% of that and make their data available to researchers. In any case, whether improved access to consistent, country-wide data were to come from data mining or repeal of real estate non-disclosure laws, it would be an invaluable resource for researchers. 6.4. Empirical examination of ATCOR If ATCOR (All Taxes Come Out of Rent) holds up empirically, it would be a super big deal. Then, it wouldn't matter whose land value estimates you accept, because you'd always be able to shift taxes off of income and capital and onto land without losing revenue. Mason Gaffney cites a few cases where it's supposed to have been observed, but we could really dig into this further. A claim this tantalizing really needs to be nailed down and resolved once and for all. 6.5. Responses to Comments I've been absolutely drowning in comments since the first article posted and there's no way I'll be able to address everything. Doing full justice to some of these will require their own entire articles, but I can leave some brief notes here. Zoning Many people replied that Land Value Tax is useless until or unless you first fix zoning. First of all, Georgists are natural allies in fixing restrictive zoning policies. This is something they definitely want and will fight for. Second, one of the reasons for restrictive zoning policies is broken incentives. A city doesn't have a huge incentive to repeal restrictive zoning policies because it isn't hurting their tax base. According to Georgists, a city whose tax base is land value has well-aligned incentives. It is incentivized to maximize land value by making the city a more desirable place to live, which also raises their tax base. It is dis-incentivized to over-assess or over-tax the land, however, because that will cause people to leave, which will lower their land values and also their tax base. One of the principle things that depresses land values and the tax base in this scenario is restrictive zoning. I personally don't care whether you first pass LVT or first repeal restrictive zoning, you can and should do both. Either one helps the other along. Transitional Politics Honestly this needs its own entire article without me going out on a limb and accidentally saying something dumb. Suffice it to say, a lot of smart people have spent a lot of time thinking about this, and you'll have to wait for a future article to find out what they are. I will let the commentariat duke this one out in the meantime. Corruption Some people agreed to all of the points raised in theory, but pointed out that human beings are wicked sinners, and LVT will be bent towards the malevolent will of our overlords, just like the old policies. And they're not wrong! The problem with this argument is that it's a fully general argument against change. The overlords game every system to their benefit. Rely on standardized tests? They'll game the SAT's with phony disability accommodations and outright cheating. Abolish standardized tests? They'll make their kids take fifty extracurriculars and pay a ghost writer to pen their college entrance essay about their life-changing volunteer work in Ghana. The right question is not "can the rich game this system?" but rather, "can they game it less than the existing one?" This is why you should keep standardized tests, even though rich people can and do game them. The evidence shows that on balance standardized tests are one of the few ways a minority student from a poor background even has a chance to move upwards. So let's dig in. The chief way you can game Land Value Tax is to cozy up to your local assessor and get them to say your land is garbage and it's not valuable. However, you have to do this kind of corruption in the open. Your land value assessment is public record, and highly visible on a map, and will stick out like a sore thumb unless the entire area has been corrupted too. I grant that motivated people could plausibly pull this off to various degrees. You might be able to get the assessor to lie about your land value, but what's the status quo we're comparing against? We don't even know how much cash money value is being socked away in Switzerland and the Caymans, let alone by whom. And even if we did, good luck figuring out how to lure that back to a taxable jurisdiction. Land at the very least can't run or hide. My dream is for us to commoditize open source mass appraisal systems and push for public real estate transaction records everywhere, so that organizations and educated members of the public can do their own land value audits at scale. And again, this is something that just needs to be subjected to empirics. We can sling theory back and forth at each other all day, but the proof is in the pudding. There are places that have done Land Value Tax in the past, and there are places that do it today. A good candidate for a future article is looking at case studies of where LVT has been tried and explicitly look for this problem. Finally, defeatism is corruption's best friend. If you believe everything I'm saying here, and your only obstacle is fear of corruption, and you accept that LVT's vulnerability to corruption is not any worse than the status quo's...then why not just get out there and fight for the world you want to see? Nothing good ever came without a struggle. Finally, we come to the most important comment of all. By George Some people said I did the whole "By George" schtick too much. I'm sorry you feel that way, but... by George, the people have spoken: 6.6. Future Direction This won't be my last article on Georgism, but I haven't yet decided whether to post them on my own blog, Fortress Of Doors, or some standalone site. Nor have I decided what topic should come next. In the comments, feel free to weigh in with which direction you'd like to see me go, as well as any issues you felt were unresolved to your satisfaction. Also, please point out any places where my math looks weird, I was just plain wrong, or where I have misunderstood or misstated the research I'm citing. Thanks very much to this readership and to our host, Scott, for graciously letting me share these findings with you. Acknowledgements: I would like to thank the following people and organizations without whom this series would not have been possible: My wonderful wife Emily, for everything
Inline links: www.gamedatacrunch.com, $400 billion super city in the desert, Redfin, 1% of that, real estate non-disclosure laws, cites a few cases, evidence, shows, he people have spoken, https://substackcdn.com/image/fetch/$s_!5S5P!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fd56e9475-5ec5-4122-a9c3-38eaa388d7e7_594x504.png, Fortress Of Doors
ACX Grants Results
December 28, 2021 · Original source
NA, $90,000, to buy a year of his time. NA is an experienced Australian political operative "on a first name basis with multiple federal politicians". You might remember some of his comments and stories from the ACX comment section, where he goes by AshLael. He's interested in using his expertise to promote effective altruism, either by lobbying directly or by training EAs in how to produce political change. I have no idea what to do with him right now but I am going to figure it out and then do it. If you're in EA and have a good idea how to use this opportunity, please let me know.
Inline links: comments and stories
Book Review: Which Country Has The World's Best Health Care?
January 19, 2022 · Original source
3: Single Payer With Substantial Private Insurance is typical of Australia and France. It works as above, except that citizens can buy private insurance which purports to be better than the standard government insurance in some way. For example, in Australia sometimes the private insurance has shorter waiting times, or can get you nicer rooms in more luxurious hospitals. Often the same doctors and hospitals treat the government and private patients, but give the private patients more time and resources, which leads to resentment and scandals. On the other hand, the private patients sometimes subsidize the public ones - ie a hospital charges extra for private patients and uses that to make up a funding shortfall if the government doesn’t pay them enough.
Highlights From The Comments On Health Care Systems
January 27, 2022 · Original source
I want to push back on the assertion Scott made that "Certainly rich people in America get good health care." After he published this book in June 2020, Ezekiel Emmanuel published an article in JAMA IM (link: https://bit.ly/3nGRHL8) called "Comparing Health Outcomes of Privileged US Citizens With Those of Average Residents of Other Developed Countries." He wanted to test the commonly stated trope that a feature of the US healthcare system is that the rich here get the very best care in the world. To do that, he looked at outcomes across six benchmark diseases (heart attack, colon cancer, breast cancer, infant mortality, maternal mortality, and pediatric acute lymphocytic leukemia). He compared outcomes for white people in the 1% of richest counties in the US, 5% richest counties in the US, and average outcomes in 12 rich countries (i'm not going to type them all out but they're places like Australia, Canada, and Germany). The results were...not so great for rich Americans!
Inline links: https://bit.ly/3nGRHL8
Australia, via Patrick:
Inline links: Patrick
I think it's worth noting that in Australia the private system only offers some services, with the most specialised procedures (transplants, major trauma, most paediatric specialties etc) only offered through the public system.
Links For February
February 22, 2022 · Original source
33: Sovereign-citizen-like movements around the US and the world. Every commentary you’ll read on sovereign citizens focuses on how the only possible explanation for the movement is (white) racism. I think pieces like this show a more subtle story. Yes, white nationalist groups are heavily involved in sovereign citizenry. But so are black nationalist groups, Native Hawaiian secessionist groups, Australian Aboriginal independence movements, etc, etc, etc. It seems like a powerful attractor for anyone who’s angry or feels mistreated for any reason.
Inline links: Sovereign-citizen-like movements around the US and the world
3: At long last, scientists have discovered a millipede that really does have (more than) a thousand legs, Eumillipes persephone, which lives tens of meters underground in Australia and in your nightmares. Recent progress in this area inspired me to Fermi-estimate a millipede version of Moore’s Law, which suggests we should be up to megapedes by 2140 and gigapedes by 2300.
Inline links: Eumillipes persephone
Biological Anchors: A Trick That Might Or Might Not Work
February 23, 2022 · Original source
It looks like this (source) So why don’t we have AI yet? Why don’t we have ten AIs? In the modern paradigm of machine learning, it takes very big computers to train relatively small end-product AIs. If you tried to train GPT-3 on the same kind of medium-sized computers you run it on, it would take between tens and hundreds of years. Instead, you train GPT-3 on giant supercomputers like the ones above, get results in a few months, then run it on medium-sized computers, maybe ~10x better than the average desktop. But our hypothetical future human-level AI is 10^16 FLOP/S in inference mode. It needs to run on a giant supercomputer like the one in the picture. Nothing we have now could even begin to train it. There’s no direct and obvious way to convert inference requirements to training requirements. Ajeya tries assuming that each parameter will contribute about 10 FLOPs, which would mean the model would have about 10^15 parameters (GPT-3 has about 10^11 parameters). Finally, she uses some empirical scaling laws derived from looking at past machine learning projects to estimate that training 10^15 parameters would require H*10^30 FLOPs, where H represents the model’s “horizon”. If I understand this correctly, “horizon” is a reinforcement learning concept: how long does it take to learn how much reward you got for something? If you’re playing a slot machine, the answer is one second. If you’re starting a company, the answer might be ten years. So what horizon do you need for human level AI? Who knows? It probably depends on what human-level task you want the AI to do, plus how well an AI can learn to do that task from things less complex than the entire task. If writing a good book is mostly about learning to write good sentence and then stringing them together, a book-writing AI can get away with a short horizon. If nothing short of writing an entire book and then evaluating it to see whether it is good or bad can possibly teach you book-writing, the AI will need a long time horizon. Ajeya doesn’t claim to have a great answer for this, and considers three models: horizons of a few minutes, a few hours, and a few years. Each step up adds another three orders of magnitude, so she ends up with three estimates of 10^30, 10^33, and 10^36 FLOPs. (for reference, the lowest training estimate - 10^30 - would take the supercomputer pictured above 300,000 years to complete; the highest, 300 billion.) Or What If We Ignore All Of That And Do Something Else? This is piling a lot of assumptions atop each other, so Ajeya tries three other methods of figuring out how hard this training task is. Humans seem to be human-level AIs. How much training do we need? You can analogize our childhood to an AI’s training period. We receive a stream of sense-data. We start out flailing kind of randomly. Some of what we do gets rewarded. Some of what we do gets punished. Eventually our behavior becomes more sophisticated. We subject our new behavior to reward or punishment, fine-tune it further. Rent asks us: how do you measure the life of a woman or man? It answers: “in daylights, in sunsets, in midnights, in cups of coffee; in inches, in miles, in laughter, in strife.” But you can also measure in floating point operations, in which case the answer is about 10^24. This is actually trivial: multiply the 10^15 FLOP/S of the human brain by the ~10^9 seconds of childhood and adolescence. This new estimate of 10^24 is much lower than our neural net estimate of 10^30 - 10^36 above. In fact, it’s only a hair above the amount it took to train GPT-3! If human-level AI was this easy, we should have hit it by accident sometime in the process of making a GPT-4 prototype. Since OpenAI hasn’t mentioned this, probably it’s harder than this and we’re missing something. Probably we’re missing that humans aren’t blank slates. We don’t start at zero and then only use our childhood to train us further. The very structure of our brain encodes certain assumptions about what kinds of data we should be looking out for and how we should use it. Our training data isn’t just what we observed during childhood, it’s everything that any of our ancestors observed during evolution. How many floating-point operations is the evolutionary process? Ajeya estimates 10^41. I can’t believe I’m writing this. I can’t believe someone actually estimated the number of floating point operations involved in jellyfish rising out of the primordial ooze and eventually becoming fish and lizards and mammals and so on all the way to the Ascent of Man. Still, the idea is simple. You estimate how long animals with neurons have been around for (10^16 seconds), total number of animals at any given second (10^20) times average number of FLOPS per animal (10^5) and you can read more here but it comes out to 10^41 FLOs. I would not call this an exact estimate - for one thing, it assumes that all animals are nematodes, on the grounds that non-nematode animals are basically a rounding error in the grand scheme of things. But it does justify this bizarre assumption, and I don’t feel inclined to split hairs here - surely the total amount of computation performed by evolution is irrelevant except as an extreme upper bound? Surely the part where Australia got all those weird marsupials wasn’t strictly necessary for the human brain to have human-level intelligence? One more weird human training data estimate attempt: what about the genome? If in some sense a bit of information in the genome is a “parameter”, how many parameters does that suggest humans have, and how does it affect training time? Ajeya calculates that the genome has about 7.5x10^8 parameters (compared to 10^15 parameters in our neural net calculation, and 10^11 for GPT-3). So we can… Okay, I’ve got to admit, this doesn’t have quite the same “huh?!” factor as trying to calculate the number of FLOs in evolution, but it is in a lot of ways even crazier. The Japanese canopy plant has a genome fifty times larger than ours, which suggests that genome size doesn’t correspond very well to organism awesomeness. Also, most of the genome is coding for weird proteins that stabilize the shape of your kidney tubule or something, why should this matter for intelligence? The Japanese canopy plant. I think it is very pretty, but probably low prettiness per megabyte of DNA. I think Ajeya would answer that she’s debating orders of magnitude here, and each of these weird things costs only a few OOMs and probably they all even out. That still leaves the question of why she thinks this approach is interesting at all, to which she answers that: The motivating intuition is that evolution performed a search over a space of small, compact genomes which coded for large brains rather than directly searching over the much larger space of all possible large brains, and human researchers may be able to compete with evolution on this axis. So maybe instead of having to figure out how to generate a brain per se, you figure out how to generate some short(er) program that can output a brain? But this would be very different from how ML works now. Also, you need to give each short program the chance to unfold into a brain before you can evaluate it, which evolution has time for but we probably don’t. Ajeya sort of mentions these problems and counters with an argument that maybe you could think of the genome as a reinforcement learner with a long horizon. I don’t quite follow this but it sounds like the sort of thing that almost might make sense. Anyway, when you apply the scaling laws to a 7.5*10^8 parameter genome and penalize it for a long horizon, you get about 10^33 FLOPs, which is weirdly similar to some of the other estimates. So now we have six different training cost estimates. First, neural nets with short, medium, and long horizons, which are 10^30, 10^33, and 10^36 FLOPs, respectively. Next, the amount of training data in a human lifetime - 10^24 FLOs - and in all of evolutionary history - 10^41 FLOPs. And finally, this weird genome thing, which is 10^33 FLOPs. An optimist might say “Well, our lowest estimate is 10^24 FLOPs, our highest is 10^41 FLOPs, those sound like kind of similar numbers, at least there’s no “5 FLOPs” or “10^9999 FLOPs” in there. A pessimist might say “The difference between 10^24 and 10^41 is seventeen orders of magnitude, ie a factor of 100,000,000,000,000,000 times. This barely constrains our expectations at all!” Before we decide who to trust, let’s remember that we’re still only at Step 2 of our eight step Methodology, and continue. How Do We Adjust For Algorithmic Progress? So today, in 2022 (or in 2020 when this was written, or whenever), assume it would take about 10^33 FLOs to train a human-level AI. But technology constantly advances. Maybe we’ll discover ways to train AIs faster, or run AIs more efficiently, or something like that. How does that factor into our estimate? Ajeya draws on Hernandez & Brown’s Measuring The Algorithmic Efficiency Of Neural Networks. They look at how many FLOPs it took to train various image recognition AIs to an equivalent level of performance between 2012 and 2019, and find that over those seven years it decreased by a factor of 44x, ie training efficiency doubles every sixteen months! Ajeya assumes a doubling time slightly longer than that, because it’s easier to make progress in simple well-understood fields like image recognition than in the novel task of human-level AI. She chooses a doubling time of “merely” 2 - 3 years. If training efficiency doubles every 2-3 years, it would dectuple in about 10 years. So although it might take 10^33 FLOPs to train a human level AI today, in ten years or so it may take only 10^32, in twenty years 10^31, and so on. When Will Anyone Have Enough Computational Resources To Train A Human-Level AI? In 2020, AI researchers could buy computational resources at about $1 for 10^17 FLOPs. That means the 10^33 FLOPs you’d need to train a human-level AI would cost $10^16, ie ten quadrillion dollars. This is about twenty times more money than exists in the entire world. But compute costs fall quickly. Some formulations of Moore’s Law suggest it halves every eighteen months. These no longer seem to hold exactly, but it does seem to be halving maybe once every 2.5 years. The exact number is kind of controversial: Ajeya admits it’s been more like once every 3-4 years lately, but she heard good things about some upcoming chips and predicted it might revert back to the longer-term faster trend (it’s been two years now, some new chips have come out, and this prediction is looking pretty good). So as time goes on, algorithmic progress will cut the cost of training (in FLOPs), and hardware progress will also cut the cost of FLOPs (in dollars). So training will become gradually more affordable as time goes on. Once it reaches a cost somebody is willing to pay, they’ll buy human-level AI, and then that will be the year human-level AI happens. What is the cost that somebody (company? government? billionaire?) is willing to pay for human-level AI? The most expensive AI training in history was AlphaStar, a DeepMind project that spent over $1 million to train an AI to play StarCraft (in their defense, it won). But people have been pouring more and more money into AI lately: Source here. This is about compute rather than cost, but most of the increase seen here has been companies willing to pay for more compute over time, rather than algorithmic or hardware progress. The StarCraft AI was kind of a vanity project, or science for science’s sake, or whatever you want to call it. But AI is starting to become profitable, and human-level AI would be very profitable. Who knows how much companies will be willing to pay in the future? Ajeya extrapolates the line on the graph forward to 2025 and gets $1 billion. This is starting to sound kind of absurd - the entire company OpenAI was founded with $1 billion in venture capital, it seems like a lot to expect them to spend more than $1 billion on a single training run. So Ajeya backs off from this after 2025 and predicts a “two year doubling time”. This is not much of a concession. It still means that in 2040 someone might be spending $100 billion to train one AI. Is this at all plausible? At the height of the Manhattan Project, the US was investing about 0.5% of its GDP into the effort; a similar investment today would be worth $100 billion. And we’re about twice as rich as 2000, so 2040 might be twice as rich as we are. At that point, $100 billion for training an AI is within reach of Google and maybe a few individual billionaires (though it would still require most or all of their fortune). Ajeya creates a complicated function to assess how much money people will be willing to pay on giant AI projects per year. This looks like an upward-sloping curve. The line representing the likely cost of training a human-level AI looks like a downward sloping curve. At some point, those two curves meet, representing when human-level AI will first be trained. So When Will We Get Human-Level AI? The report gives a long distribution of dates based on weights assigned to the six different models, each of which has really wide confidence intervals and options for adjusting the mean and variance based on your assumptions. But the median of all of that is 10% chance by 2031, 50% chance by 2052, and almost 80% chance by 2100. Ajeya takes her six models and decides to weigh them like so, based on how plausible she thinks each one is: 20% neural net, short horizon 30% neural net, medium horizon 15% neural net, long horizon 5% human lifetime as training data 10% evolutionary history as training data 10% genome as parameter number She ends up with this: How Sensitive Is This To Changes In Assumptions? She very helpfully gives us a Colab notebook and Google spreadsheet to play around with. The notebook lets you change some of the more detailed parameters of the individual models, and the spreadsheet lets you change the big picture. I leave the notebook to people more dedicated to forecasting than I am, and will talk about the spreadsheet here. If you’re following along at home, the default spreadsheet won’t reflect Ajeya’s findings until you fill in the table in the bottom left like so: Great. Now that we’ve got that, let’s try changing some stuff. I like the human childhood training data argument (Lifetime Anchor) more than Ajeya does, and I like the size-of-the-genome argument less. I’m going to change the weights to 20-20-0-20-20-20. Also, Ajeya thinks that someone might be willing to spend 1% of national GDP on training AIs, but that sounds really high to me, so I’m going to down to 0.1%. Also, Ajeya’s estimate of 3% GDP growth sounds high for the sort of industrialized nations who might do AI research, I’m going to lower it to 2%. Since I’m feeling mistrustful today, let’s use the Hernandez&Brown estimate for compute halving (1.5 years) in place of Ajeya’s ad hoc adjustments. And let’s use the current compute halving time (3.5 years) instead of Ajeya’s overly rosy version (2.5 years). All these changes… …don’t really do much. The median goes from 2052 to about 2065. Four of the models give results between 2030 and 2070. The last two, Neural Net With Long Horizon and Evolution, suggest probably no AI this century (although Neural Net With Long Horizon does think there’s a 40% chance by 2100). Ajeya doesn’t really like either of these models and they’re not heavily weighted in her main result. Does The Truth Point To Itself? Back up a second. Here’s something that makes me kind of nervous. Most of Ajeya’s numbers are kind of made up, with several order-of-magnitude error bars and simplifying assumptions like “all animals are nematodes”. For a single parameter, we get estimates spanning seventeen different orders of magnitude: the upper bound is one hundred quadrillion times the lower bound. And yet four of the six models, including two genuinely exotic ones, manage to get dates within twenty years of 2050. And 2050 is also the date everyone else focuses on. Here’s the prediction-market-like site Metaculus: Their distribution looks a lot like Ajeya’s, and even has the same median, 2052 (though forecasters could have read Ajeya’s report). Katja Grace et al surveyed 352 AI experts, and they gave a median estimate of 2062 for an AI that could “outperform humans at all tasks” (though with many caveats and high sensitivity to question framing). This was before Ajeya’s report, so they definitely didn’t read it. So lots of Ajeya’s different methods and lots of other people presumably using different methodologies or no methodology at all, all converge on this same idea of 2050 give or take a decade or two. An optimist might say “The truth points to itself! There are 371 known proofs of the Pythagorean Theorem, and they all end up in the same place. That’s because no matter what methodology you use, if you use it well enough you get to the correct answer.” A pessimist might be more suspicious; we’ll return to this part later. FLOPS Alone Turn The Wheel Of History One more question: what if this is all bullshit? What if it’s an utterly useless total garbage steaming pile of grade A crap? Imagine a scientist in Victorian Britain, speculating on when humankind might invent ships that travel through space. He finds a natural anchor: the moon travels through space! He can observe things about the moon: for example, it is 220 miles in diameter (give or take an order of magnitude). So when humankind invents ships that are 220 miles in diameter, they can travel through space! Ships have certainly grown in size tremendously, from primitive kayaks to Roman triremes to Spanish galleons to the great ocean liners of the (Victorian) present. The AI forecasting organization AI Impacts actually has a whole report on historical ship size trends to prove an unrelated point about technological progress, so I didn’t even have to make this graph up. Suppose our Victorian scientist lived in 1858, right when the Great Eastern was launched. The trend line for ship size crossed 100m around 1843, and 200m in 1858, so doubling time is 15 years - but perhaps they notice this is going to be an outlier, so let’s round up a bit and say 18 years. The (one order of magnitude off estimate for the size of the) Moon is 350,000m, so you’d need ships to scale up by 350,000/200 = 1,750x before they’re as big as the Moon. That’s about 10.8 doublings, and a doubling time is 18 years, so we’ll get spaceships in . . . 2052 exactly. (fudging numbers to land where you want is actually fun and easy) SS Great Eastern, the extreme outlier large steamship from 1858. This has become sort of a mascot for quantitative technological progress forecasters. What is this scientist’s error? The big one is thinking that spaceship progress depends on some easily-measured quantity (size) instead of on fundamental advances (eg figuring out how rockets work). You can make the same accusation against Ajeya et al: you can have all the FLOPs in the world, but if you don’t understand how to make a machine think, your AI will be, well, a flop. Ajeya discusses this a bit on page 143 of her report. There is some sense in which FLOPs and knowing-what-you’re-doing trade of against each other. If you have literally no idea what you’re doing, you can sort of kind of re-run evolution until it comes up with something that looks good. If things are somehow even worse than that, you could always run AIXI, a hypothetical AI design guaranteed to get excellent results as long as you have infinite computation. You could run a Go engine by searching the entire branching tree structure of Go - you shouldn’t, and it would take a zillion times more compute than exists in the entire world, but you could. So in some sense what you’re doing, when you’re figuring out what you’re doing, is coming up with ways to do already-possible things more efficiently. But that’s just algorithmic progress, which Ajeya has already baked into her model. (our Victorian scientist: “As a reductio ad absurdum, you could always stand the ship on its end, and then climb up it to reach space. We’re just trying to make ships that are more efficient than that.”) Part II: Biology-Inspired AI Timelines: The Trick That Never Works Eliezer Yudkowsky presents a more subtle version of these kinds of objection in an essay called Biology-Inspired AI Timelines: The Trick That Never Works, published December 2021. Ajeya’s report is a 169-page collection of equations, graphs, and modeling assumptions. Yudkowsky’s rebuttal is a fictional dialogue between himself, younger versions of himself, famous AI scientists, and other bit players. At one point, a character called “Humbali” shows up begging Yudkowsky to be more humble, and Yudkowsky defeats him with devastating counterarguments. Still, he did found the field, so I guess everyone has to listen to him. He starts: in 1988, famous AI scientist Hans Moravec predicted human-level AI by 2010. He was using the same methodology as Ajeya: extrapolate how quickly processing power would grow (in FLOP/S), and see when it would match some estimate of the human brain. Moravec got the processing power almost exactly right (it hit his 2010 projection in 2008) and his human brain estimate pretty close (he says 10^13 FLOP/S, Ajeya says 10^15, this 2 OOM difference only delays things a few years), yet there was not human-level AI in 2010. What happened? Ajeya's answer could be: Moravec didn't realize that, in the modern ML paradigm, any given size of program requires a much bigger program to train. Ajeya, who has a 35-year advantage on Moravec, estimates approximately the same power for the finished program (10^16 vs. 10^13 FLOP/S) but says that training the 10^16 FLOP/S program will require 10^33ish FLOPs. Eliezer agrees as far as it goes, but says this points to a much deeper failure mode, which was that Moravec had no idea what he was doing. He was assuming processing power of human brain = processing power of computer necessary for AGI. Why? The human brain consumes around 20 watts of power. Can we thereby conclude that an AGI should consume around 20 watts of power, and that, when technology advances to the point of being able to supply around 20 watts of power to computers, we'll get AGI? […] You say that AIs consume energy in a very different way from brains? Well, they'll also consume computations in a very different way from brains! The only difference between these two cases is that you know something about how humans eat food and break it down in their stomachs and convert it into ATP that gets consumed by neurons to pump ions back out of dendrites and axons, while computer chips consume electricity whose flow gets interrupted by transistors to transmit information. Since you know anything whatsoever about how AGIs and humans consume energy, you can see that the consumption is so vastly different as to obviate all comparisons entirely. You are ignorant of how the brain consumes computation, you are ignorant of how the first AGIs built would consume computation, but "an unknown key does not open an unknown lock" and these two ignorant distributions should not assert much internal correlation between them. Cars don’t move by contracting their leg muscles and planes don’t fly by flapping their wings like birds. Telescopes do form images the same way as the lenses in our eyes, but differ by so many orders of magnitude in every important way that they defy comparison. Why should AI be different? You have to use some specific algorithm when you’re creating AI; why should we expect it to be anywhere near the same efficiency as the ones Nature uses in our brains? The same is true for arguments from evolution, eg Ajeya’s Evolutionary Anchor, ie “it took evolution 10^43 FLOPs of computation to evolve the human brain so maybe that will be the training cost”. AI scientists sitting in labs trying to figure things out, and nematodes getting eaten by other nematodes, are such different methods for designing things that it’s crazy to use one as an estimate for the other. Algorithmic Progress vs. Algorithmic Paradigm Shifts This post is a dialogue, so (Eliezer’s hypothetical model of) OpenPhil gets a chance to respond. They object: this is why we put a term for algorithmic progress in our model. The model isn’t very sensitive to changes in that term. If you want you can set it to some kind of crazy high value and see what happens, but you can’t say we didn’t consider it. OpenPhil: We did already consider that and try to take it into account: our model already includes a parameter for how algorithmic progress reduces hardware requirements. It's not easy to graph as exactly as Moore's Law, as you say, but our best-guess estimate is that compute costs halve every 2-3 years […] Eliezer: The makers of AGI aren't going to be doing 10,000,000,000,000 rounds of gradient descent, on entire brain-sized 300,000,000,000,000-parameter models, algorithmically faster than today. They're going to get to AGI via some route that you don't know how to take, at least if it happens in 2040. If it happens in 2025, it may be via a route that some modern researchers do know how to take, but in this case, of course, your model was also wrong. They're not going to be taking your default-imagined approach algorithmically faster, they're going to be taking an algorithmically different approach that eats computing power in a different way than you imagine it being consumed. OpenPhil: Shouldn't that just be folded into our estimate of how the computation required to accomplish a fixed task decreases by half every 2-3 years due to better algorithms? Eliezer: Backtesting this viewpoint on the previous history of computer science, it seems to me to assert that it should be possible to: Train a pre-Transformer RNN/CNN-based model, not using any other techniques invented after 2017, to GPT-2 levels of performance, using only around 2x as much compute as GPT-2;
Inline links: source, here, Japanese canopy plant, https://substackcdn.com/image/fetch/$s_!gj-T!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F333dcbf2-1f63-42a1-821f-94f39818e62d_1280x897.jpeg, Measuring The Algorithmic Efficiency Of Neural Networks, https://substackcdn.com/image/fetch/$s_!dX1J!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fb9496f1f-ec6c-41a2-8c2e-27f09da22097_1280x759.png, here, https://substackcdn.com/image/fetch/$s_!LnC0!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F62d647ff-58ed-4e9a-9f1a-7febf5859249_1152x842.png, Colab notebook, Google spreadsheet, https://substackcdn.com/image/fetch/$s_!BND-!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F622bac28-eaa6-40b5-b93b-695952966ef7_744x324.png, https://substackcdn.com/image/fetch/$s_!lbos!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F7d5c2306-a123-4903-adb9-d961d56ebfb5_1152x842.png, Metaculus, https://substackcdn.com/image/fetch/$s_!SMnF!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F807f66de-8c5c-4423-b293-ca92b5b64053_763x360.png, surveyed 352 AI experts, https://substackcdn.com/image/fetch/$s_!JxQ5!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fceba6aa0-dbde-41ca-805e-01af4fac9324_769x336.png, a whole report on historical ship size trends, https://substackcdn.com/image/fetch/$s_!PRDj!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fde3d97f4-afca-45c4-9ed2-521cd25041df_460x262.jpeg, AIXI, Biology-Inspired AI Timelines: The Trick That Never Works
Your Book Review: Viral
July 30, 2022 · Original source
[4] This report was widely criticized, with the governments of the US, Canada, Australia, Japan, South Korea, the UK, and others expressing “shared concerns” about the investigation. Even the head of the WHO, Dr. Tedros, suggested that a more thorough follow-up investigation might be required.
Meetups Everywhere 2022: Times & Places
August 26, 2022 · Original source
Asia-Pacific (including Australia)
BRISBANE, AUSTRALIA Contact: Jarred Filmer, jarred[dot]filmer[at]gmail[dot]com Time: Saturday, September 10, 7:00 PM Location: 52 McCaul Street Taringa (house) Coordinates: 5R4JFXXQ+P8 Event link(s): LessWrong, Facebook event Group info: We used to meet once a month years ago, but now just meet whenever there's a Meetups Everywhere :) Notes: Snacks will be provided but dinner will not be, would recommend eating before you come CANBERRA, AUSTRALIA Contact: Andy Bachler, Andy[dot]Bachler[at]gmail[dot]com Time: Wednesday, August 31, 5:30 PM Location: Badger & Co pub at ANU. Central location, parking free after 5pm, might be loud, sorry! Coordinates: 4RPFP4FC+34 Event link(s): LessWrong, Eventbrite Notes: Parking area just to the north of the pub, over the river, is free after 5pm! GOLD COAST (SOUTH), AUSTRALIA Contact: Lerancan, lerancan[at]gmail[dot]com Time: Sunday, September 11, 2:00 PM Location: A picnic table, Wyberba Street Reserve, Tugun Coordinates: 5R3MVF5W+555 Event link(s): LessWrong Notes: Email me in case of bad weather/you can't find me/you can't make that time etc. MELBOURNE, AUSTRALIA Contact: Ryan, xgravityx[at]hotmail[dot]com Time: Friday, September 2, 6:00 PM Location: Beer Deluxe Federation Square Coordinates: 4RJ65XM9+3Q Event link(s): LessWrong, Facebook event Group info: We're officially the Less Wrong Melbourne social meetup group, though our members include the broader rationalist community. We meet once a month for casual discussion (and beers for those so inclined). Please join our Facebook group to see the meeting invite; there you will see a WhatsApp group link - please join that group too to ensure timely updates in case of changes (Facebook notifications don't work reliably for this). Notes: Please RSVP to the meeting invite on the Facebook group so that I can make an appropriate booking. PERTH, AUSTRALIA Contact: Madge, madgech[at]gmail[dot]com Time: Sunday, September 25, 2:00 PM Location: Russell Square, Northbridge, corner of Shenton and Aberdeen St. There will be some sort of ACX meetup sign. Coordinates: 4PWQ3V34+W6 Event link(s): LessWrong, Facebook event Group info: I run one meetup per year, if someone else wants to take over please do Notes: Please RSVP on LessWrong or Facebook SYDNEY, AUSTRALIA Contact: Eliot, Redeliot[at]gmail[dot]com Time: Thursday, September 15, 6:00 PM Location: City of Sydney rsl, lvl 2 in the fishbowl Coordinates: 4RRH46F4+983 Event link(s): LessWrong, Meetup.com Group info: We meet monthly WOLLONGONG, AUSTRALIA Contact: Jason, jason[dot]bowkettblogs[at]gmail[dot]com Time: Saturday, September 3, 12:00 PM Location: UOW Library Coordinates: 4RQGHVVH+69 Event link(s): LessWrong CHENGDU, CHINA Contact: Alex, acx[dot]chengdu[at]gmail[dot]com Time: Thursday, September 15, 7:00 PM Location: Chef Wenwu Hot & Spicy Jianghu Food (Yulin store)/文武大厨·热辣江湖菜(玉林店). I (a foreigner) will be wearing a green shirt. Coordinates: 8P26J3C5+462 Event link(s): LessWrong Notes: Please RSVP at the above email address, I will give you my Wechat contact if you're interested in attending. Open to time/date/location changes, so let me know if the proposed event doesn't work for you! Can be a bilingual event; all welcome. 有双语交流的可能性。如果想来的话，请提前发给我个电子邮件。 HONG KONG Contact: Nathan, nathan[at]xevarion[dot]org Time: Saturday, September 10, 1:00 PM Location: The Catalyst, 2 Po Yan Street, Sheung Wan. Big wooden door. Coordinates: 862M74PW+6XP Event link(s): LessWrong BANGALORE, INDIA Contact: Nihal, propwash[at]duck[dot]com, Discord: propwash#4648 Time: Sunday, September 18, 4:00 PM Location: Matteo Coffea, Church Street Coordinates: 7J4VXJF4+PR Event link(s): LessWrong Group info: We're the longest active group in Asia — we've been meeting monthly for the last 4 years, discussing ACX posts, LW content with a diverse and friendly group of people. Check our website for more info. Notes: Please RSVP on LessWrong to help me be better prepared. HYDERABAD, INDIA Contact: Vatsal, vmehra[at]pm[dot]me, Whatsapp: +919944430856 (username: Vim) Time: Sunday, September 11, 5:00 PM Location: The Weekend Cafe, Plot No D, 3, Vikrampuri Colony, beside vac's bakery, Vikrampuri Colony, Lane, Secunderabad, Telangana, 500015, India Coordinates: 7J9WFF4X+5P Event link(s): LessWrong Group info: Our rationality meetup group has been around for about 3 months and we discuss articles and exercises (eg. CFAR handbook) that can help us improve epistemic and instrumental rationality. MUMBAI, INDIA Contact: PB, e2y94n1nv[at]relay[dot]firefox[dot]com Time: Sunday, October 9, 4:00 PM Location: Jamjar Diner, Versova Coordinates: 7JFJ4RM6+5W Event link(s): LessWrong Notes: Please RSVP on LessWrong or via email so I can plan activities accordingly. NEW DELHI, INDIA Contact: Suryansh Tyagi, suryanshtyagiphone[at]gmail[dot]com, WhatsApp/phone +919997299972 Time: Sunday, September 11, 5:00 PM Location: Select CityWalk Mall, Saket. Where inside the mall depends on the number of people interested. Coordinates: 7JWVG6H9+8H Event link(s): LessWrong Notes: Please either send me an email or message me on WhatsApp if you want to attend. Any suggestions/changes are welcome. UDAIPUR, RAJASTHAN, INDIA Contact: Shailendra Paliwal, acx-meetup-2022[at]shailendra[dot]me Time: Saturday, September 10, 7:00 PM Location: We'll be at Doodh Talai near Pichola Lake and I'll be wearing a gray t-shirt carrying a sign ACX Meetup Coordinates: 7JPMHM9M+HG Event link(s): LessWrong Notes: Please RSVP on LessWrong so that I can plan ahead UBUD, BALI, INDONESIA Contact: William Ubud, Napaproject[at]gmail[dot]com Time: Tuesday, August 30, 6:00 PM Location: PARQ Ubud Coordinates: 6P3QG789+F7 Event link(s): LessWrong TOKYO, JAPAN Contact: Harold Godsoe, hgodsoe[at]gmail[dot]com Time: Saturday, October 8, 10:00 AM Location: Near Nakameguro station - RSVP for details Coordinates: 8Q7XJPV2+QFP Event link(s): LessWrong, Meetup.com Notes: ACX Tokyo meets monthly since Sept 2021. Our meetups are in English, so far. To join in, feel free to get in touch in any of the many ways to do so (email, Meetup.com). It's useful to be in contact before coming to an event, to help with that first leap of faith. KUALA LUMPUR, MALAYSIA Contact: Yi-Yang, yi[dot]yang[dot]chua[at]gmail[dot]com, LessWrong profile Time: Saturday, September 17, 2:00 PM Location: I'll be in Lisette's Bangsar, which is a 5-minute walk from Bangsar LRT. I'll be wearing a pale green t-shirt and carrying an ACX sign. Coordinates: 6PM34MHH+VW Event link(s): LessWrong AUCKLAND, NEW ZEALAND Contact: Jonathan De Wet, jonpdw[at]gmail[dot]com Time: Saturday, September 3, 6:30 PM Location: 32 Stanley Ave Milford, Auckland Coordinates: 4VMP6QH4+86 Event link(s): LessWrong, Facebook event Notes: It’s a dinner party! Please RSVP on FB so I know how much food to make DUNEDIN, NEW ZEALAND Contact: Gavin, bisga673[at]student[dot]otago[dot]ac[dot]nz Time: Saturday, September 3, 3:00 PM Location: Picnic tables outside of St. David's lecture theatre on Otago University campus. I'll make a sign with ACX meetup. Coordinates: 4V6G4GP7+GM5 Event link(s): LessWrong Notes: There is no Dunedin group as far as I'm aware of, but I'd be keen to meet other likeminded people and organise group hangouts occasionally. WELLINGTON, NEW ZEALAND Contact: Ben W, benwve[at]gmail[dot]com Time: Tuesday, September 27, 5:30 PM Location: Rutherford House, Bunny Street, Wellington. Room MZ05, which is on the mezzanine floor Coordinates: 4VCPPQCH+FGC Event link(s): LessWrong Notes: We're running the event this time in partnership with Effective Altruism Wellington LAPU LAPU, CEBU, PHILIPPINES Contact: Dave, tokkolizard[at]tutanota[dot]com Time: Sunday, September 4, 2:00 PM Location: Starbucks in Mactan Newtown, there will be a sign with ACX MEETUP on it. Coordinates: 7Q268257+4F Event link(s): LessWrong Notes: Please RSVP by mail so I know if I need to set up a bigger meeting place SINGAPORE Contact: Jonathan Ng, jonathan[dot]ng1[at]gmail[dot]com, Telegram @derpy Time: Tuesday, September 6, 6:30 PM Location: Tanjong Pagar MRT gantry, I'll be wearing the dark blue EA Global 2022 jumper Coordinates: 6PH57RGW+J8 Event link(s): LessWrong
Inline links: 5R4JFXXQ+P8, LessWrong, Facebook event, 4RPFP4FC+34, LessWrong, Eventbrite, 5R3MVF5W+555, LessWrong, 4RJ65XM9+3Q, LessWrong, Facebook event, 4PWQ3V34+W6, LessWrong, Facebook event, 4RRH46F4+983, LessWrong, Meetup.com, 4RQGHVVH+69, LessWrong, 8P26J3C5+462, LessWrong, 862M74PW+6XP, LessWrong, 7J4VXJF4+PR, LessWrong, our website, 7J9WFF4X+5P, LessWrong, 7JFJ4RM6+5W, LessWrong, 7JWVG6H9+8H, LessWrong, 7JPMHM9M+HG, LessWrong, 6P3QG789+F7, LessWrong, 8Q7XJPV2+QFP, LessWrong, Meetup.com, Meetup.com, LessWrong profile, Lisette's Bangsar, 6PM34MHH+VW, LessWrong, 4VMP6QH4+86, LessWrong, Facebook event, 4V6G4GP7+GM5, LessWrong, 4VCPPQCH+FGC, LessWrong, 7Q268257+4F, LessWrong, 6PH57RGW+J8, LessWrong
Book Review Contest 2022 Winners
September 02, 2022 · Original source
Exhaustion: A History, reviewed by Van Occupanther. Van is a psychiatrist from Australia who would prefer to remain pseudonymous
Inline links: Exhaustion: A History
Highlights From The Comments On The Central Valley
October 13, 2022 · Original source
Nobody wants to bother with the giant political struggle it would be to rationalize water rights, although they apparently did do this in Australia in the early part of this century due to a huge drought.
ACX Grants: Project Updates
November 04, 2022 · Original source
12: Biosecurity And Existential Safety Lobbying In Australia (?/10) Nathan Ashby, Austen Erickson, and Susan Pennings have founded the Institute For Effective Policy. They’re currently working on getting an amendment to the $4.5 billion Emergency Response Fund Amendment (Disaster Ready Fund) Bill to include funding for longer-term risks, as well as helping build a broader and more unified Australian EA policy community. As a result of their work highlighting long term catastrophic risks, the government has invited their group to participate in a national summit on risk resilience
Inline links: Institute For Effective Policy
Response To Alexandros Contra Me On Ivermectin
February 01, 2023 · Original source
Carvallo said that zero people in the treatment group of his study got COVID, compared to 58% of people in the control group. This is a pretty implausibly big effect, even by the standards of other pro-ivermectin studies, although I don’t know if anyone else tried the exact same preventative protocol as Carvallo. I think this is a more nuanced story than Alexandros’ version where Buzzfeed just doesn’t know that sometimes studies happen at more than one hospital. Is fraud the best explanation? I think Alexandros thinks of Carvallo as just not keeping very good records, so he doesn’t have raw data, and probably mixed up his numbers a few times or gave false numbers, and didn’t have anything to send his collaborators when they asked. I think this is maybe possible, although it seems suspicious that he falsely said Dr. Lombardo was involved, falsely claimed the hospital involved was doing a different trial, and got very implausible results. I can imagine weird chains of events that would cause all of these things through honest misunderstandings. But they don’t seem like the best explanation. After discussing this with Alexandros, he objects to my use of the term “known fraudster”. Perhaps I should have said “highly credibly suspected fraudster” instead, although in a Bayesian sense nothing can ever be 100% and at some point plausibility shades imperceptibly into knowledge. Still, I feel like my description here was more accurate than Alexandros’, which just mentions the hospital approval issue and says nothing about any of the rest of this in a thousand word subsection about this study in particular. I did err in saying the Carvallo paper was retracted. According to the article: After BuzzFeed News raised questions about how the study’s data was collected and analyzed, a representative from the Journal of Biomedical Research and Clinical Investigation, which published the results, said late Monday, “We will remove the paper temporarily.” A link was removed from the table of contents — but was reinstated by Thursday. The journal’s explanation, provided after this story was published, was that the author “informed us that he has already provided the evidence of his study to the media.” I apologize for the error. Elalfy et al (still disagree with Alexandros) I described this as: As best I can tell, this is some kind of Egyptian trial. It might or might not be an RCT; it says stuff like “Patients were self-allocated to the treatment groups; the first 3 days of the week for the intervention arm while the other 3 days for symptomatic treatment”. Were they self-allocated in the sense that they got to choose? Doesn’t that mean it’s not random? Aren’t there seven days in a week? These are among the many questions that Elalfy et al do not answer for us. The control group (which they seem to think can also be called “the white group”) took zinc, paracetamol, and maybe azithromycin. The intervention group took zinc, nitazoxanide, ribavirin, and ivermectin. There were very large demographic differences between the groups of the sort which make the study unusable […] There is no primary outcome assigned, but viral clearance rates on day seven were 58% in the yellow group compared to 0% in the white group, which I guess is a strong positive result. This table looks very impressive, in terms of the experimental group doing better than the control, except that they don’t specify whether it was before the trial or after it, and at least one online commentator thinks it might have been before, in which case it’s only impressive how thoroughly they failed to randomize their groups. Overall I don’t feel bad throwing this study out. I hope it one day succeeds in returning to its home planet. In the summary post, Alexandros’ entire criticism of my coverage of this trial, one of the seven trials he focuses on as most unfairly covered and uses as the lynchpin of his argument that I am morally culpable for disastrously bad reporting, is: [Elalfy et al] are accused of incompetence for failing to randomize their groups multiple times in Scott’s piece. The paper writes in six separate places that it is not reporting on a randomized trial, amongst them on a diagram that Scott included in his own essay. Hard to imagine how else they could have made it clear. In his full post on this, he goes line by line to point out all the places they say they are non-randomized, pausing to snark about how dumb I am for not noticing each time4. But he never addresses the actual source of my confusion, which is the part of the paper where it says that: Patients were self-allocated to the treatment groups; the first 3 days of the week for the intervention arm while the other 3 days for symptomatic treatment. If this was done as described, it should be an (almost) random trial; patients who come in on Wednesdays shouldn’t systematically differ from patients who come in on Thursdays5. But in fact, it looks (assuming I am understanding a very ambiguous table correctly) like there are very large pre-existing differences between the groups, sufficient to explain the entire result. If they in fact followed their days-of-the-week protocol, and it was random as expected, then I’m misunderstanding the table seeming to show very large differences, and they have indeed found evidence for ivermectin’s efficacy. If they didn’t follow their day-of-the-week protocol and it’s non-random, then maybe I’m understanding the table correctly and their groups had large differences to begin with and the fact that they had large differences at the end of the trial doesn’t demonstrate anything about ivermectin. This is all I was trying to say in the post, and instead of having any opinion on it Alexandros just makes fun of me for saying it. I think our actual crux is that Alexandros thinks a table of big differences between the groups has to be post-treatment (based on how big the differences are), whereas I’m not sure (because it’s unclear in the study, and also because the authors describe what could be a randomization method but also go on and on about how nonrandom they are). This is why I thought it mattered how random it was! Maybe instead of mocking me for this, you can admit it’s an important and relevant question! Ghauri et al (still disagree with Alexandros) I describe this as: Pakistan, 95 patients. Nonrandom; the study compared patients who happened to be given ivermectin (along with hydroxychloroquine and azithromycin) vs. patients who were just given the latter two drugs. There’s some evidence this produced systematic differences between the two groups - for example, patients in the control group were 3x more likely to have had diarrhea (this makes sense; diarrhea is a potential ivermectin side effect, so you probably wouldn’t give it to people already struggling with this problem). Also, the control group was twice as likely to be getting corticosteroids, maybe a marker for illness severity. Primary outcome was what percent of both groups had a fever: on day 7 it was 21% of ivermectin patients vs. 65% of controls, p < 0.001. No other outcomes were reported. I don’t hate this study, but I think the nonrandom assignment (and observed systematic differences) is a pretty fatal flaw. Alexandros notes that these are three differences between experimental/control groups, out of 33 listed characteristics that could have been different. There is approximately a 23% chance (he calculates) that you could get these differences by chance. He accuses me of failing to do a formal Carlisle test - the usual test you would use to determine whether weird differences between randomized groups are because of fraud - instead eyeballing it and getting it wrong. Here I do want to defend myself: I am not accusing Ghauri et al of fraud. In fact, this would be nonsensical: they admit they are assigning patients nonrandomly. Carlisle tests are usually done to show that something about group assignment is impossible (and therefore fraudulent) in a fair random assignment. But these people aren’t claiming to have done a fair random assignment, so I’m not sure what a Carlisle test would prove. My argument is more like: this is nonrandom, therefore we should expect it to be unfair. It is unnecessary, but helpful, to note an actual apparent unfairness - there’s some evidence they gave the ivermectin to less severe patients (as measured by corticosteroid use). Therefore, we can’t necessarily trust this to be a fair trial (which it was never really claiming to be). In the end I kept Ghauri as an okay study, although GMK didn’t so it ended out trashed in the final analysis anyway. I think my thinking was that I never claimed to be only looking at RCTs, so this non-RCT whose between-group-differences confirmed that it was indeed a non-RCT with all the risk of bias that entails, didn’t necessarily need to be ruled out. Still, I don’t think I was wrong to mention this possibility, and I think Alexandros was wrong to suggest that I needed to do extra tests for this to be fair. Borody et al (still disagree with Alexandros) I described this as: Our last paper! …is it a paper? I can’t find it published anywhere. It mostly seems to be on news sites. Doesn’t look peer-reviewed. And it starts with “Note that views expressed in this opinion article are the writer’s personal views”. Whatever. 600 Australians were treated with ivermectin, doxycycline, and zinc. The article compares this to an “equivalent control group” made of “contemporary infected subjects in Australia obtained from published Covid Tracking Data”; this is not how you control group, @#!% you. Then it gets excited about the fact that most patients had better symptoms at the end of the ten-day study period than the beginning (untreated COVID resolves in about ten days). Why are these people wasting my time with this? Let’s move on. Alexandros lists his full concerns here. My summary: Scott is being incredibly disrespectful to the authors, who are in fact a legendary gastroenterologist who invented life-saving h. pylori therapy and a brilliant immunologist who invented a well-regarded bronchitis vaccine (in particular, in describing their control group, I said “this is not how you control group, @#!% you”.
Inline links: This table, his full post on this, 4, 5, here
“Synthetic control groups” - ie comparing people in a trial to some previously-known understanding of how a disease progresses - are a standard practice, and basically fine. Borody et al indeed have had amazing careers with many things they can be proud of. But I continue to believe that this paper is not among them. Synthetic control groups are more common in social sciences, but have occasionally been used in pharmacology when it would be unethical or extremely difficult to use a real control group. The most common use case is rare cancers, where it takes years to get enough patients to test a drug and it also seems kind of unethical to delay. Another good thing about rare cancers is that they're pretty discrete; you don't have to worry about things like "well, 90% of leukemias never make it to a doctor anyway, so maybe we're only seeing the serious leukemias" or "these guys counted the leukemias that get dealt with by the local doctors' office, but those other guys counted the leukemias that have to go to the hospital". More important, studies with synthetic control groups usually go above and beyond to justify why their synthetic control group should be a fair comparison to the treatment group. Here's an example, from a paper about a rare leukemia. They start by getting a synthetic control group from a previous randomized controlled trial of leukemia drugs (not the general population!) Then they throw out more than half their patients for not being a good match for the selection criteria of the current study. Then they investigate whether there are significant differences on five important demographic factors, and find a few. Then they re-weight the patients in the historical comaprator study to adjust out the differences between the previous population and the current population. Then they do some analyses to check if they re-weighted everything correctly. Then they apologize profusely for having to use this vastly inferior methodology at all: In special cases when a disease is rare, prognosis is very poor, and there are limited therapeutic options available, single-arm clinical trials may be used as evidence for accelerated drug approvals. Comprehensive evaluation of historical comparator or reference data can provide an additional approach for putting the efficacy of a new therapy into perspective.11, 12 In this study, we applied different statistical methods and sensitivity analyses to evaluate the clinical efficacy of blinatumomab against historical data. Concerns often raised regarding the use of historical comparator data are the influence of potential biases related to selection, misclassification and confounding.12 The requirement of rigorous eligibility criteria in the blinatumomab clinical study—such as Eastern Cooperative Oncology Group status of two or lower and absence of abnormal lab values during screening—may increase the chance of better outcomes in the clinical study than the historical data. While it may be possible to use unadjusted historical data when patient populations are sufficiently similar,27 the disproportionate number of advanced-stage patients in the blinatumomab trial required methods applied to individual-level data to minimize bias. Selection bias was minimized by use of stringent inclusion criteria into the historical data set and by weighting or adjusting for known prognostic factors. In addition, the historical data set represented adult R/R patients who received standard of care (excluding palliative care patients where possible), without any restrictions to any patient subgroups. Residual confounding may still remain and be difficult to control for, particularly in data sets where differences in important prognostic factors are unknown or not measured in one data set. In this study, nearly all known important prognostic factors were adjusted for in the weighted or propensity score analyses. Missing data on key covariates lead to exclusion of some records from the analyses (Figure 1), which may theoretically bias the overall results. However, our examination of records with missing covariates did not identify significant differences by patient demographic characteristics compared with patients who had complete data (data not shown). Misclassification bias was limited by harmonization of patient-level data in the pooled analysis, which employed common data definitions for disease classification and outcomes characterization. Compare this to how the Borody study discusses its synthetic control group: The control data was from contemporary infected subjects in Australia obtained from published Covid Tracking Data. I hesitate to say “they didn’t even say which tracking data”, because in the past I’ve said things like that and just missed it. But I can’t find them saying which tracking data. In Borody et al’s synthetic control group, 70/600 (11.5%) patients required hospitalization. But the US hospitalization rate appears to be about 1% for unvaccinated individuals. So Borody’s synthetic control group got 10x the expected hospitalization rate. This seems very relevant to this study finding that ivermectin decreases hospitalization by 90%! I’m not claiming this is fraudulent, or impossible, or means the study couldn’t have been good. And Borody claim to have used an “equivalent” control group, so maybe there was some adjustment done for this. But this is why we usually use more than one word to describe our control groups! Or use real control groups that don’t ruin your study if you do a finicky adjustment slightly wrong! I feel like these are the kinds of questions Alexandros needs to be asking, instead of just giving a link to a Stat News article about how sometimes synthetic control groups are okay. Also other questions, like “how come this found a 90% decrease in hospitalization and mortality, but lots of other studies found smaller decreases, and the biggest and best studies found none at all?” I know Alexandros’ answers are to find lots of flaws with the biggest and best studies, but these flaws wouldn’t be enough to cover up a 90% cure rate. And if you’re in the business of calling out flaws in studies I genuinely think having your control group be “we used some group of people somewhere in Australia, they had 10x the normal hospitalization rate, we won’t tell you anything else” would be the sort of flaw you would call out! Thomas Borody is a genuinely brilliant gastroenterologist and I am very grateful for his life-saving discoveries. But Elon Musk is a genuinely brilliant engineer and I am very grateful for his low-cost reusable rockets - and this doesn’t mean he never does crazy inexplicable things. Maybe Borody and his collaborators have a point from this study, but I don’t feel like it makes sense as written. If they ever explain what they were doing in more detail and it’s some sort of amazing 4D-chess move that makes total sense, I will apologize to them. Otherwise, stick to inventing amazing life-saving digestive therapies. In response to this section, Alexandros stresses that he is not necessarily saying Borody et al is incorrect or challenging my decision to leave it out. He writes: I will repeat that my strong objection, is that you wrote " this is not how you control group, @#!% you". I therefore pointed to stat news to support my case that, yes, this can indeed be how you control group. That's all. In the article I even noted that this aversion towards disrespect to elders may even be a cultural difference between us. To be clear, if I were making a case for ivermectin, I would not be relying on this study as my starting point. III. Hokey Meta-Analysis Alexandros points out that I used the wrong statistical test when analyzing the overall picture gleaned from this studies. He’s right. The right statistical test would make ivermectin look stronger, without changing the sign of the conclusion. After getting a core group of potentially trustworthy studies, I tried to see whether ivermectin still had a statistically significant positive effect in them. I tried to be honest that I didn’t really know how to do formal meta-analyses: Probably I’m forgetting some reason I can’t just do simple summary statistics to this, but whatever. It is p = 0.15, not significant . . . What happens if I unprincipledly pick whatever I think the most reasonable outcome to use from each study is? . . . Now it’s p = 0.04, seemingly significant I in fact could not do simple summary statistics to this. Alexandros describes the test I should have used, a DerSimonian-Laird test, and applies it to the same data. Now the numbers are p = 0.03 and p < 0.0001. I accept that I was wrong, he is right, and this is more accurate. My original conclusion to this section is that although you couldn’t be absolutely sure from the numbers, eyeballing things it definitely looked like ivermectin had an effect. I then went on to try to explain that effect. With Marinos’ corrections, you can be sure from the numbers, but the rest of the post - an attempt to explain the effect - still stands. IV. Worms Alexandros brings up issues with the Strongyloides hypothesis; Dr. Bitterman graciously responds. I find the issues real enough to lower my credence in the idea, but not to completely rule it out. Even if it is true, I probably overestimated how important it was. My original explanation for the effect was Dr. Avi Bitterman’s theory of Strongyloides hyperinfection. Many people in certain tropical regions are infected with the parasitic worm Strongyloides. Usually a person’s immune system keeps this worm under control, and the parasites cause only limited problems. But under certain situations - especially when people take immune-suppressing corticosteroids - the immune system fails, the worms multiply, and the patient can potentially die of sudden worm overgrowth (“hyperinfection”). Corticosteroids are a common COVID treatment. So plausibly some people in tropical areas fighting COVID are at risk of dying from worm hyperinfection. Ivermectin was originally an anti-parasitic-worm medication before being repurposed to fight COVID, and everyone agrees it is very good at this. So if many people in COVID trials are dying of worm infections, then ivermectin could help them. This would look like ivermectin reducing mortality in COVID trials, and make people wrongly conclude that ivermectin treats COVID. Alexandros responds to this theory here, again I’ll try to summarize: The original Bitterman paper concludes that ivermectin trials show stronger results in high-Strongyloides-prevalence regions. But it mixes prevalence data from two different papers with different methodologies. Correcting for this, the findings no longer clear a formal bar for statistical significance, and don’t really look significant either.
Inline links: are a standard practice, Here's an example, 11, 12, 27, Figure 1, about 1% for unvaccinated individuals, here
Links For March 2023
March 10, 2023 · Original source
7: I still haven’t read Garett Jones’ The Culture Transplant yet, but I’m seeing a lot of good discussion. Via Paul Graham, here’s a graph of migration-adjusted tech history score 1500 (ie how advanced a region was in 1500, adjusting for the fact that eg Australia is mostly inhabited by English people and should count as England rather than as the Aborigines) vs. income per person today (actually 2005):
Inline links: Paul Graham
Highlights From The Comments On Telemedicine Regulations
April 03, 2023 · Original source
In Australia, as far as I know, telemedicine doctors are allowed to prescribe drugs provided there has been a face to face appointment in the past 12 months.
Spring Meetups Everywhere 2023
April 10, 2023 · Original source
Asia-Pacific (including Australia)
CANBERRA, AUSTRALIA Contact: Andy Contact Info: Andy[dot]bachler[at]gmail[dot]com Time: May 8th, 06:00 PM Location: King O'Malleys Pub, the Snug Room, located in Civic. Coordinates: https://plus.codes/4RPFP4CJ+MC Event Link: https://www.meetup.com/canberra-astral-codex-ten-meetup-group/events/292816447
Inline links: https://plus.codes/4RPFP4CJ+MC, https://www.meetup.com/canberra-astral-codex-ten-meetup-group/events/292816447
MELBOURNE, AUSTRALIA Contact: Yitzi Contact Info: metonacx[at]gmail[dot]com Time: Sunday, May 7th, 07:00 PM Location: The Inkerman Hotel Coordinates: https://plus.codes/4RJ64XMX+F5 Notes: Look for the ACX meetup sign ... and if you're not sure whether to come or not, come! :)
Inline links: https://plus.codes/4RJ64XMX+F5
Open Thread 273
April 24, 2023 · Original source
1: New spring meetups added since I last updated you: Phoenix, Arizona; Melbourne, Australia. Check the list for dates and times
Inline links: spring meetups
Links For May 2023
June 01, 2023 · Original source
25: Australia has a National Sorry Day where they focus on various atrocities perpetrated against the indigenous population. I think this makes more sense than the American solution of having it be a mildly awkward undercurrent across all the other more celebratory holidays (eg July 4, Thanksgiving, Columbus Day).
Inline links: National Sorry Day, Columbus Day
Highlights From The Comments On Social Model Of Disability
July 25, 2023 · Original source
Anyway. This is something I've studied a decent amount because of the impact the Social Model, Charity Model and Medical Model has had on the Australian model, and in particular studying the history of the IDF (the former ICIDH), and a few other things. But I'm not an expert. And I'm also speaking from the perspective of a disabled Australian and not Ireland (our history with the social model mostly goes back to the late 80s at best).
Meetups Everywhere 2023: Times & Places
August 25, 2023 · Original source
MUKONO, UGANDA Contact: Neil Contact Info: neilsotherinbox[at]gmail[dot]com Time: Sunday, October 15th, 11:00 AM Location: Bushbaby Lodge, there will be seating arranged and a sign in case there are other groups meeting that day too. Coordinates: https://plus.codes/6GGJ7RHC+X2 Notes: Tea and coffee will be served. Other food and drinks available for purchase. Feel free to bring kids/dogs. Asia-Pacific Australia CANBERRA, ACT, AUSTRALIA Contact: Andy B Contact Info: Andy[dot]Bachler[at]gmail.com Time: Tuesday, October 10th, 5:00 PM Location: Looking to meet at Grease Monkey in Braddon. I will book a table under the name "Andy", will probably be in the outside area. Coordinates: https://plus.codes/4RPFP4GM+Q2X Notes: GreaseMonkey have half-price drinks and snacks from 4pm-6pm. I will look to organise a chip-in for pizzas for those that are keen :-) I run a small regular meetup on the first Monday of every month.
Inline links: https://plus.codes/6GGJ7RHC+X2, https://plus.codes/4RPFP4GM+Q2X
CANBERRA, ACT, AUSTRALIA Contact: Andy B Contact Info: Andy[dot]Bachler[at]gmail.com Time: Tuesday, October 10th, 5:00 PM Location: Looking to meet at Grease Monkey in Braddon. I will book a table under the name "Andy", will probably be in the outside area. Coordinates: https://plus.codes/4RPFP4GM+Q2X Notes: GreaseMonkey have half-price drinks and snacks from 4pm-6pm. I will look to organise a chip-in for pizzas for those that are keen :-) I run a small regular meetup on the first Monday of every month.
Inline links: https://plus.codes/4RPFP4GM+Q2X
GOLD COAST, QUEENSLAND, AUSTRALIA Contact: Lerancan Contact Info: lerancan[at]gmail[dot]com Time: Sunday, October 8th, 2:00 PM Location: A picnic table, Wyberba Street Reserve, Tugun Coordinates: https://plus.codes/5R3MVF5W+26 Notes: I will have an ACX sign. Email me in case of bad weather/you can't find me/you can't make that time but would still like to meet etc.
Inline links: https://plus.codes/5R3MVF5W+26
Highlights From The Comments On Kidney Donation
November 07, 2023 · Original source
(The UK, Canada, Australia, and I think most other European countries also allow altruistic donation; Germany is a rare holdout here. Still, the US was one of the first, and I’m still proud of it.)
Spring Meetups Everywhere 2024
March 30, 2024 · Original source
Asia-Pacific (including Australia)
ISTANBUL, TURKEY Contact: Ozge Contact Info: ozgeco[at]yahoo[dot]com Time: Saturday, May 4th, 12:00 PM Location: We meet in Kadıkoy at Kahve Dunyası at Yeni Iskele. Yeni Iskele is the seaport where we take ferry to get to Eminonu/Karakoy from Kadıkoy ( not to Besiktas). Please go upstairs, walk through the bookstore Istanbul Kitapcisi to meet me at the terrace. I will have a ACX MEETUP sign. If it rains, we meet inside the cafe, or under large cafe umbrellas. Coordinates: https://plus.codes/8GGFX2VC+4R Notes: I hope we chat with coffee. Asia-Pacific Australia CAIRNS, QUEENSLAND, AUSTRALIA Contact: Ben Contact Info: greenblue4004[at]gmail[dot]com Time: Saturday, April 6th, 5:00 PM Location: Near the Cairns Esplanade Fun Ship Playground. I will be wearing a green t shirt and a black legionnaire hat. Coordinates: https://plus.codes/5RM73QW7+383 Notes: Feel free to bring kids/dogs.
Inline links: https://plus.codes/8GGFX2VC+4R, https://plus.codes/5RM73QW7+383
CAIRNS, QUEENSLAND, AUSTRALIA Contact: Ben Contact Info: greenblue4004[at]gmail[dot]com Time: Saturday, April 6th, 5:00 PM Location: Near the Cairns Esplanade Fun Ship Playground. I will be wearing a green t shirt and a black legionnaire hat. Coordinates: https://plus.codes/5RM73QW7+383 Notes: Feel free to bring kids/dogs.
Inline links: https://plus.codes/5RM73QW7+383
What Is Going On In IFS?
May 28, 2024 · Original source
Let’s look at one more non-Western example of the importance of the inner world. Luh Ketut Suryani, a professor of psychiatry in Bali, and Michelle Stephen, an anthroplogist and professor in Australia, have developed a body of work that emphasizes Stephen’s concept of autonomous imagination. Stephen states that autonomous imagination is:
My 2024 Presidential Debate
June 27, 2024 · Original source
Trump: I think you can rescue the idea of states, if you think of them not as real in themselves, but as different aspects of the American atom. When we consider America in the context of its vastness and its freedom, we call it "Texas". When we consider America in the context of its innovation and cultural influence, we call it "California". When we consider America in the context of its barrenness and oil-producing-capacity, we call it "North Dakota". And so on. America does not have states in the sense that Queensland is a state of Australia, it has states in the sense that ice or steam is a state of water. This isn’t to say that America ever changes between these states, because change is a property of compound entities. But it may appear to outside observers in one or another of these ways at different times.
Your Book Review: The Family That Couldn’t Sleep
July 12, 2024 · Original source
(This report was, as it happens, published in the exact same month as The Family That Couldn’t Sleep.) DTM came to know the family well. He befriended them by way of two members of their younger generation, Lisi – a woman terrified by the shadow of the disease, and Ignazio – the doctor she had married, who was more terrified by the shadow of the disease. Ignazio put together the pieces of the family puzzle, consolidating all the disparate diagnoses into a single disorder and filling out a lot of blank spots on family trees. When DTM came along, he was able to help Ignazio make the case that the family would benefit from the spotlight – that greater awareness of FFI could lead to a cure both for them and for a slew of other prion diseases. As it so happens, he is one of those nonfiction authors who serve as a character in their own story. DTM has some form of progressive muscular palsy. He is, or at least was in 2006, not entirely sure what it is. The relatively unimpressive state of genetics at the time had not identified his causative mutation, though it looked a lot like one of the rarer forms of Charcot-Marie-Tooth disease2. DTM is pragmatic about this, the way everyone chronically ill is either pragmatic or doomed. Whatever he has, it is a defect in protein structure; his peripheral nerves decay not because of a problem with the nerves themselves but an inability of their scaffolding to hold them together, as he puts it. The last chapter of the book dwells on this, on the web of connections popping up between a thousand disorders. DTM’s disease is something vaguely similar, if you squint, to an exceptionally slow-progressing motor neurone disease; if you jump another level out, you see amyloid plaque diseases like Huntington’s and Alzheimer’s, and if you jump yet another level out, you see something like prions. His interest in the Venetian family was driven by this. Some of its members thought this a beautiful act of sympathy; others thought him a grotesque parody of themselves, an onlooker, a gawker, peddling their tragedy to salve his relatively insignificant problems. They are, he thinks, both right. That’s the beginning, and that’s the end. What happens in the middle? --------------------------------------------------------- The Venetian family lends the book its title, but they’re really more of a framing device. The Family That Couldn’t Sleep is separated into four parts, of which the first and fourth – the shortest by far – deal with the family. Part 2 is kuru, the king of fucked up diseases you read about in clickbait Weird Medicine listicles. Let’s talk about kuru! Kuru, is, famously, the prion disease you get if you eat another person’s brain. Well, not quite. It’s a prion disease that became endemic amongst women in the Fore society, who ritually ate brains, one of which had an inherited or spontaneous prion disease. This is an important note – there’s a tendency (which the book’s later chapters engage in) to assume cannibalism just has a Prion Disease Generator attached. If you eat people who don’t have prion diseases, you won’t suddenly get one. Uh, don’t eat people. Anyway, part 2 is DTM’s historiography of Fore-Westerner first contact. It’s hilarious. Papua New Guinea is a frankly ridiculous place; one of the all-time best Lyttle Lytton winners (worst first sentence from a hypothetical or, in this case, real work) was “Papua New Guinea is so violent that more than 820 languages are spoken there”. The native residents were so hostile to outsiders that all the colonial empires had cut their losses – and when you think about the places they colonized, that says something. After the First World War, PNG was ripped from its nominal German ‘owners’, but no one else wanted the place. So, of course, they gave it to the Australians. It was thirty years and another war before we actually made contact. 1940s Australia was as ‘settled’ as it’d ever be; the cities were bustling and the interior was mapped. The kind of explorer who two centuries before would be heading to new continents had to console himself with Pacific islands. Console he did. The native peoples of the PNG coasts were hostile enough to the wannabe-colonialists that the Australians, flying planes overhead, were the first people to discover that the island’s inland was populated too. No one had broken through on land. In all this deep and angry rainforest, the Fore were the furthest out. They lived far into the island’s mountainous interior; DTM describes their territory as “nearly vertical”. Calling people primitives is a bit passe these days for understandable reasons, but no other term comes to mind. The Fore had no name for themselves; we call them by an exonym, “the people to the south”. They weren’t, to be clear, hunter-gatherers – they were slash-and-burn agriculturalists, but very well-fed ones. Despite the tendency in grain-focused cultures for poor agriculturalists to be stunted/malnourished, the Fore were a remarkably healthy people. Well, except for the famous bit. The first remarkable thing about the Fore was just how quickly they wanted to assimilate. Most PNG tribes weren’t particularly enthused by Western offers of injections/tractors/radios/Christianity. Yet as soon as the Australians arrived, the Fore made ceasefires in their wars with other tribes, volunteered to help large-scale Australian projects on the coast, started planting and trading coffee, and enthusiastically participated in censuses. It’s the only first-contact narrative I’ve seen where the colonizers were concerned about how badly the other guys wanted to be colonized. The next was the one that got their names in the history books. Australian officials started to notice a remarkable lack of women in Fore camps. Some tribes sequestered their women, particularly when Westerners were around, so at first they thought nothing of it. The high rate of unpartnered young men, though, was way out of PNG norms. DTM tells this part fantastically. The Fore chapters drip with the dread of dramatic irony. When the first breakthrough comes, you have to catch your breath: “Tiny” Carey noted something in the middle of August 1950 that deepened this mystery. He noticed that near the village of Henganofi there had been an unusual number of deaths. “It appears,” he wrote his superiors, “natives suffer from stomach trouble, get violent shivering, as with the ague, and die fairly rapidly.” [...] McArthur investigated a little more [...] One day in August 1953 he ran into more of the shivering people Tiny Carey had seen several years before: “Nearing one of the dwellings, I observed a small girl sitting down beside a fire. She was shivering violently and her head was jerking spasmodically from side to side.” It would be quite some time before anyone figured out what caused it – but the problem, as DTM notes, was that its cause wasn’t possible. Everyone priored that the weird undescribed disease in the Fore lands was some nocebo sorcery-sickness. Vincent Zigas, the first actual doctor sent to work with the Fore, tried to placebo-effect them and failed miserably: On the way, Apekono stopped at a hut and showed Zigas his first kuru victim. “On the ground in the far corner sat a woman of about thirty,” the doctor wrote. “She looked odd, not ill, rather emaciated, looking up with blank eyes with a mask-like expression. There was an occasional fine tremor of her head and trunk, as if she were shivering from cold, though the day was very warm.” It was almost exactly the tableau McArthur had witnessed in 1953. Zigas, though, was a doctor. He could do more than look—or so he thought: “I decided I might as well try my own variety of magic,” he remembered. He rubbed Sloan’s Liniment, a balm for sore muscles, on her and declared to her family and his guide: “The sorcerer has put a bad spirit inside the woman. I am going to burn this spirit so that it comes out of her and leaves her. You will not see the fire, but she will feel it. The bad spirit will leave her and she will not die.” The lotion penetrated the woman’s skin and she writhed in pain. “Get up! Walk!” Zigas commanded theatrically. “The woman struggled feebly as if to rise, then, exhausted, started to tremble more violently, making a sound of foolish laughter, akin to a titter.” That evening Apekono asked Zigas not to try to cure any more kuru victims; “Don’t use your magic medicine anymore. It will not win our strong sorcery.” This was a disaster. The Fore were so cooperative precisely because they hoped “Western magic” could conquer theirs. As it became clear it couldn’t, they turned hostile. The Australians had hoped to “modernize a Stone Age people”; now all their subjects were dropping dead before their eyes, from what they could only assume was a “hysterical reaction” to colonization itself. So, to solve this, they needed a batshit insane American. Carleton Gajdusek is one of the characters who dominates The Family That Couldn’t Sleep. He couldn’t not. You could put him in a car commercial and he’d dominate it. Gajdusek was a physician with a rare, intense combination of science and practice. He was a romanticist, a field worker, and a lover of everything strange. He’d been an army doctor, a government conspiracy-cover-upper, and a postdoc under Linus Pauling who described his intent as “to straighten out Pauling’s ideas about proteins”. He hated civilization, in a slightly-to-Ted’s-centre sense, and was passionate about “primitives and isolates”. He jumped at the chance to work in Papua New Guinea; he planned to conduct a multi-site study on child development in such cultures, and relished the opportunity to live in a “primitive” environment himself. He did all this so he could rape kids. Oh, he did it for the scientific curiosity and love of medicine, but he also did it so he could rape kids. Gajdusek was a pedophile in the actual-lifelong-exclusive-paraphilia sense, as opposed to the “metonym for child molester” sense. Some people who roll snake-eyes on the Sexuality Dice repress it, but some are perfectly happy to act on it; Gajdusek was #2 in its fullest form, the kind of guy who believes that a well-lived life includes raping some kids. DTM doesn’t shy from this, not for a moment. It’s the first thing he tells you about Gajdusek. It couldn’t not be; you couldn’t talk about why he went to PNG otherwise. When Gajdusek landed in PNG, he first found the place too civilized. He’d been promised a land of “cannibal savages” – where were they? After some traipsing, he found them, right where he was promised. The Fore were perfect for Gajdusek. They had some kind of medical mystery that’d been lost on everyone else. They ate each other, in exactly the way he loved detailing in his diaries (“”Women and children, particularly, partake of the human flesh,” he noted with pleasure”). As kuru cases popped up, he aggressively recorded them. He wrote lovingly detailed notes that he sent back to his Australian advisor. He wrote with intensity, with exclamation marks, with the joie de vivre of a man just where he wanted to be. Gajdusek smothered the Fore with ‘cures’ that never worked, but they didn’t get angry at him. As DTM dryly puts it: “Their children trusted him, and that was enough for them.” At some point, someone suggested sending an anthropologist...or an epidemiologist...or literally anyone with more credentials than Gajdusek and Zigas3. Gajdusek threw a shitfit, convinced this one-and-a-half-man team was enough to Solve The Problem Forever. But he got bored eventually – running off with another tribe with, as his diary notes at length, an apparent custom of youths ritually fellating older men – and Zigas, I dunno, the book neglects him a bit here. So they managed to sneak in some anthropologists. The husband-and-wife team of Robert Glasse and Shirley Lindenbaum4 were the first involved parties to give a shit about the Fore as people, rather than as colonial subjects/medical mysteries/walking sex toys. What they uncovered was fascinating. The Fore were cannibals, yes, but they were recent cannibals. They didn’t have an ancient tradition of eating their dead, like the other visitors assumed. They happened to be in contact with some cannibal groups, and after a Fore man died of “sorcery”, they thought: well, what would happen if we ate him? “People tasting it expressed their approval. ‘”This is sweet,” they said, “What is the matter with us, are we mad? Here is good food and we have neglected to eat it.”” If not for the wild coincidence that the first Fore cannibalism victim had a prion disease, kuru would never have existed. Glasse and Lindenbaum started to put together the pieces. They’d been sent down to rule out a genetic explanation – to track the kinship ties of the Fore and see how the disease ran through families. It didn’t run through families in any coherent sense, but it sure did run through cannibalism. The clincher was the age distribution. The Fore, ever enthused by colonialism, quit eating each other as soon as the Australians arrived. Children stopped dying of kuru shortly after; they simply weren’t exposed to the infectious agent. The couple sent the news to Gajdusek, who was off raping kids somewhere else. In the next part of the book, DTM runs through Gajdusek’s many conjectures of kuru’s cause – more like sketches or abstract paintings than like true hypotheses. Gajdusek was annoyed that someone else was doing something he “totally could’ve done”, and even more annoyed that another lab was running similar experiments – an attempt at a vaccine for a particular sheep disease had accidentally created a prion generator. But he was happy to swoop in and claim the credit for what he was starting to think of as “slow viruses”, an infection that somehow lays dormant for years. DTM portrays Gajdusek perfectly, in that “real life has no need for verisimilitude” way. Gajdusek was at once a brilliant man, an all-consuming narcissist, an entertaining character, and a monster beyond redemption. A lesser book might pick one or two. The Family That Couldn’t Sleep portrays him as all four, and on a personality level (as opposed to a scientific one), the Gajdusek-focused parts are some of the most gripping. --------------------------------------------------------- Outside of the jumps between the Venetian family and everything else, The Family That Couldn’t Sleep is not siloed. The narratives of all prion diseases are deeply intertwined. This is what makes it a great book. It’s 300 pages of dramatic irony. You read the whole thing, waiting for the eureka moment – the point everyone realizes they’re looking at the same cause. It does, however, make it a tad difficult to review or synopsize. The book’s story is so weird – and, often, so at odds with conventional wisdom that trickles down about the Fore et al – that you have to recap quite a bit, and the book steadfastly resists recapping. The next couple chapters after we depart from Gajdusek’s credit-claiming are mostly about experiments with various prion diseases. They’re scientifically fascinating. Unlike some medical-books-for-general-audiences (cough, How Not to Study a Disease), DTM never talks down to the reader. He assumes someone reading a 300-page book about prions is smart and wants to learn about prions. He also has – you can feel it in his words – the agonizing experience of spending his life on the other side of the doctor’s desk, trying to beat into whoever he’s talking to that no, seriously, you don’t need to lie to him or try explain a complex disease at a fourth-grade level. The first prion disease studied was scrapie. Scrapie was a big deal – it starved and killed large shares of British sheep flocks, making it a serious economic problem. Veterinary researchers had tried to prevent or cure it for centuries. It was a veritable graveyard of ambitions: Quintessential was D. R. Wilson at the Moredun Institute in Scotland, who worked in the middle of the last century for more than a decade trying, with mounting frustration, to kill the scrapie agent. He found that it survived desiccation; dosing with chloroform, phenol, and formalin; ultraviolet light; and cooking at 100 degrees centigrade for thirty minutes. The scrapie researcher Alan Dickinson told me he remembered Wilson at the end of his career as “very, very, very quiet. Of course, that was after his breakdown.” “Now it is our turn to study prions. Perhaps we should approach the subject cautiously.” The problem, as DTM explains, is that prion diseases were impossible. They violated 20th-century understandings of biology. Proteins “were no more alive, and no more infectious, than bone”. Prion diseases seemed to have too many causes – genetic, infectious, and sporadic. They looked infection-like in some ways, but patients didn’t produce virus antibodies. Sheep exposed to scrapie, or chimps infected with kuru, took years to develop symptoms. Their facts did not fit together. In the 1960s, people started wondering. The unifying trait of prion agents was that they had to be denatured to be destroyed. Was this a particularly small virus defined by its protein coating? Or – even more outre – was it pure protein, no DNA at all? No one could figure out quite how the latter worked, but it was tempting. Gajdusek, by now a major figure in this field, kept a foot in both worlds. He didn’t want to stake his reputation on a no-DNA hypothesis, but he certainly sympathized. Enter Prusiner. Stanley Prusiner was Gajdusek’s counterpart. Where Gajdusek seemed permanently manic, Prusiner was deliberate and exacting. He entered Gajdusek’s “slow viruses” field in the early 1970s after a chance encounter with a CJD patient. He relished the laboratory in a way Gajdusek didn’t at all, and set out to optimize the hell out of his projects. Prusiner set out to isolate the smallest infectious particle in the scrapie agent. He injected tons of hamsters (hamsters got sick faster than mice) with increasingly tiny scrapie proteins, hoping to determine whether the Minimum Viable Scrapie was DNA. By the mid-1980s, he’d produced something so small it couldn’t possibly be a virus. Denaturing it destroyed it; exposing it to nucleic acid dissolvers actually made it stronger. Emboldened by this discovery, Prusiner set out to anoint himself the King of Prions. Here emerges something of a Voldemort-Umbridge distinction – the difference between cartoonish villainy and banal evil. Gajdusek is a bad guy because he rapes kids. Prusiner is a bad guy because he is the most grotesque stereotype of the Advisor/Peer Reviewer from Hell made flesh. Everything Prusiner did was to build his reputation atop a pile of skulls. When recruited as a peer reviewer for other prion papers, he wrote negative reviews to undermine their authors. He worked his grad students to the bone and intentionally destroyed their careers, telling them he’d “ruin them” if they entered prion research as competitors. He lied about the origin of the protein-only hypothesis, claiming he originated it a decade after it was actually conjectured. But hey, he was good at getting grants. I was surprised reading a lot of this, because for all the time I’ve been aware of it, the cause of prion disease has seemed settled. “Oh yeah, it’s a protein that gets all fucked up.” But DTM goes through just how unsettled it was right up through to The Family That Couldn’t Sleep’s publication. Serious confirmation only arrived a couple years later. Many people were deeply critical of the prion hypothesis – often, it seemed, because they loathed Prusiner too much to go along. Throughout the book, he cuts an uncharismatic figure. Gajdusek and Prusiner both won the Nobel for discovering prions, decades apart. This tells you something – the “discovery” of prions can be construed quite a few ways. Gajdusek formulated the hypothesis; Prusiner proved it. Gajdusek was grievously offended by Prusiner’s Nobel, perceiving his rival – not inaccurately – as a follower who never originated any ideas of his own. But Gajdusek was offended from a federal prison cell, so how’d that work out for him? Fascinating as all this is, no one published a book about prions in the mid-2000s because it was about kuru or FFI. They published books about prions because teenagers were dying, and people wanted to know why. DTM lays the seeds for part 3 – the mad cow section – in part 1. This is a discussion of scrapie, the longstanding prion disease of sheep. Scrapie was a medical mystery for centuries (remember poor D. R. Wilson), precisely because of the intuitive implausibility of prions. The scrapie chapter is a great history-of-science piece, covering the agricultural productivity revolutions of the 18th century, the surfeit of bizarre origins veterinarians concocted, and the treatments that never worked. Scrapie is not transmissible to humans – well, we hope. It’s concerningly transmissible to primates. But it’s been around for a long, long time, and it doesn’t epidemiologically look like humans get it...we hope. Anyway, you ever tried to generalize from one example? The British government did! In the mid-1980s, strange reports started coming out of the UK’s farms. Farmers were describing a new disease where dairy cows – incredibly docile creatures, under normal circumstances – turned hostile, kicking them as they went into the milking stalls. The symptoms looked to all the world like scrapie. Epidemiologists tracing the outbreaks found a unifying link with “cake” – animal protein feed sweetened with molasses. The scrapie-like symptoms must have traced to an infected sheep. But scrapie doesn’t transmit to humans, so it must be okay to keep slaughtering them, right? We all know how this ended. The best term for the British response to the mad cow outbreak is “cacklingly evil conspiracy”. The agricultural industry really, really didn’t need a huge zoonotic outbreak – so it decided it didn’t have one. They first suppressed all mentions that the disease looked like scrapie, then – when this became impossible – hyped up that scrapie doesn’t transmit to humans, so there’s nothing to worry about. The formal name of the disease, “bovine spongiform encephalopathy”, was supposedly chosen to optimize for unfamiliarity – it wouldn’t fit well in a headline. They emphasized, extensively, that there was nothing to worry about. Ever. At some point, people started asking questions. If there was nothing to worry about, why was the agricultural industry panicking so hard? As things became ever more worry-inducing, this turned down ludicrously twisting paths: Meanwhile, the Southwood Working Party and the experts who advised it were learning on the job. They learned, for instance, that the BSE agent entered the animal through the mouth and then followed the digestive tract into the organs that try to filter out infections—the tonsils, the guts, and the spleen—and from there traveled into the peripheral and central nervous system, and finally arrived at the brain. They also learned that pasties, meat pies, and even some baby foods contained tissues from a lot of those organs. So the Southwood Working Party recommended banning these organs, but only from baby food. This started a chain reaction of consumer doubt: if infected cow organs were unsafe for babies, how could they be good for adults? The government then banned offal, as the organs were collectively called, in all human food but gave the industry a grace period to get it out of the feed supply. Then pet food manufacturers began to wonder if what drove cows mad might not also drive dogs, cats, and parrots mad. The feed they sold came from concentrate made of the same sick animals that had previously made up the meat and bone meal farmers used. Their trade group decided to put a similar ban in place—immediately. So for five months it was safer to be a dog than a human in Britain. DTM spends pretty much this whole section of the book making fun of the British government. To be fair, they deserved it. They killed hundreds of kids in agonizing and preventable ways – they could take some ribbing. This is all throughout the mid-1980s to early-mid 1990s. Through this period, it wasn’t yet clear that mad cow could spread to humans. The panic was clear, and deserved, but it didn’t yet have a match for its powder keg. It would alight. The first suspected case of vCJD – human mad cow – was in 1994. Fifteen-year-old Vicky Rimmer developed a sudden, strange disease. Doctors gave her months to live...until she died in 1998. A couple other suspected cases trickled down through the mid-90s, including a young man who made meat pies for a living, whose grieving mother received a letter from the Prime Minister that “humans do NOT get mad cow disease”. (That must’ve been fun.) Soon, they couldn’t deny it any longer. On March 20, 1996, Stephen Dorrell, the health secretary, stood up in Parliament to announce the news that had already appeared as a tentative conclusion in scientific journals and as rumor in newspapers for the previous two years: British beef was killing British teenagers. The first confirmed death was that of Stephen Churchill, a nineteen-year-old student from Wiltshire, who died in May 1995. Back in 1989, at the Southwood Working Party’s suggestion, the government had set up a surveillance unit in Edinburgh to watch for any evidence that BSE had crossed to humans. One worry had been that if BSE passed to humans, how would anyone know it? How would you recognize something you had never seen? It turned out to be easy: Churchill and the nine other teenagers who had gotten sick had spectacular amyloid plaques in their brains, chunks of dead protein almost visible to the naked eye. If sporadic CJD was a whisper, BSE-caused prion disease was a shout. The investigators sat open-mouthed looking at slides whose damage, they feared, portended the most severe epidemic in modern British history. This part of the book is not fun. It lacks the insane personalities and duelling careers of the other entries. It is an honest chronology of the vCJD epidemic – a gruesome failure of the agricultural industry, the one system that everyone is vulnerable to. The government and industry had completely violated their duty of care to citizens and consumers. They were paying the price. No one would buy British beef anymore – not while they watched their children die. Now here’s the thing: this is ethnography, not historiography. The Family That Couldn’t Sleep is a book from the mid-2000s. The epidemic was not at all in the rear view mirror. There were piles of unanswered questions that DTM constantly alludes to. We have eighteen years more hindsight than he did then. What do we know now? --------------------------------------------------------- In 2006, the vCJD epidemic looked like it was going to be a lot better than the worst fears. BSE itself was a huge problem for the cattle industry, but honestly, no one is too sympathetic to the cattle industry. People were not going to die in anywhere near the numbers believed. We had all sorts of reassuring data coming out about this, which DTM chronicles. We were learning that only some genotypes seemed susceptible to vCJD. We didn’t see any older people die of the disease. We were seeing numbers drop, such that vCJD must have a pretty short incubation period. Anyway, all of this is wrong! The Family That Couldn’t Sleep was written in the candidate gene era. Back then, the nascent field of human genetics was sure it was about to Solve Polygenism. Yes, the simple Mendelian monogenic patterns popular a few decades back clearly didn’t apply to common diseases, but how many variants could there be? We were about to discover the five genes influencing 20% of Alzheimer’s risk each, the five genes influencing 20% of heart disease risk each, etc., and once we were done we’d just do gene therapy and cure Alzheimer’s. A paper on autism genetics from 1999 was so outre as to speculate there might be as many as fifteen genes involved. The fact we are now using the term “omnigenic model” should tell you roughly how well this worked out. Do you remember SNPedia? If you were a 2014 Slate Star Codex reader, you might. 2014 was still pretty candidate gene. People were out there publishing papers saying a single variant could increase your life expectancy by 15 years. SNPedia was a site that beautifully categorized all of these, so you could do 23andme or whatever, look up your results on SNPedia, and make horrible life choices.5 It was eventually bought out by one of the consumer DNA companies, so no one ever edited it again, making it a great time capsule of early-mid 2010s behavioural/medical genetics takes. SNPedia will excitedly explain to you that common genetic variants make you immune to vCJD. They cite a 2009 post from the now-archived 23andme blog titled “No Good Evidence That Potential Pool of Mad Cow Disease Victims Is Expanding”, explaining how fears of late-onset vCJD are clearly debunked by new Scientific Knowledge. Everyone who developed vCJD in the 1990s and 2000s had an M/M genotype in a particular part of the PRNP prion gene, so the roughly half the population with M/V or V/V genotypes were immune. The Family That Couldn’t Sleep buys this, too. In fact, it buys it in an even more agonizingly 2000s way. The first sign that transmissible prion diseases weren’t genotype-restricted should’ve been the growth hormone kids. You might have heard this story – from the late 1950s through mid-1980s, human growth hormone produced from brain tissue was used as a treatment for pituitary dwarfism, until it turned out to spread CJD if the originating brain was infected. DTM discusses this, to set the scene for the genetics thing. He mentions what was the state of the art at the time – that a disproportionate share of both the growth hormone kids and sporadic CJD cases were V/V homozygotes. This, uh – so the book was written in the mid-2000s, yeah? Yeah. The conclusion DTM drew – and this was a common conclusion at the time – was that homozygosity somehow made you more vulnerable to CJD, and M/M homozygosity made you vulnerable to BSE-borne CJD in particular. We cannot criticise the author for not predicting the future, but we live in the future, and can say how this worked out. Turns out, nope, M/V heterozygotes totally get vCJD. After a British man in his 30s died of CJD in 2016, he was found to have vCJD and an M/V genotype. He was tested for vCJD only because he was exceptionally young for someone with a sporadic prion disease – meaning people developing it later in life would be missed6. Did you know up to 1 in 2000 people in the UK have latent vCJD? There is one line in The Family That Couldn’t Sleep that stopped me dead in my tracks when I read it: What happens to the Italian family in the end depends less on their own actions than on the world’s interest in prion diseases, which they cannot control. If lots of people are afraid of getting variant CJD, the family benefits. If fear of prion disease goes the way of the fear of swine flu or Ebola, then they will be orphaned again. THIS BOOK IS FROM 2006! Three years before the swine flu pandemic! Eight years before the Ebola pandemic! “If you’re looking for a sign, this is it.” --------------------------------------------------------- The last section of The Family That Couldn’t Sleep addresses BSE fears in America and a nascent internet subculture DTM calls “Creutzfeldt Jakobins” – people who track American CJD cases, trying to spot vCJD patterns. When reading his description of the Creutzfeldt Jakobins, my mind constantly, uncontrollably turned to covid. Here it was – an online community of people deeply skeptical about a disease’s official story, tracking every contradiction, every implausibility, every statistic that failed to apply to the individual. Self-described “redneck hippies” and “soccer mom Republicans” teaming up to find the truth hidden behind an impossible world. You know what they’re doing now. I’ve always combined a deep interest in medicine with a healthy distrust for it. People who are constitutionally inquisitive, anti-authoritarian, and suspicious about official narratives tend to end up skeptical of at least some mainstream claims in the field. This is not to say I think you should take bleach enemas or something, just that I understand the impulse behind concluding the US government was covering up a local vCJD wave. Traditionally, sporadic prion diseases are said to have a prevalence of one in a million. (Hold on to that for a second.) The last section of the book is a chronology of Americans finding bizarrely more than one in a million of their friends dying of sporadic CJD, often at inexplicably young ages, sometimes in geographical clusters. This is understandably suspicious. Then DTM goes on to reassure us by saying none of these cases were confirmed to have an M/M genotype, which OH GOD OH FUCK A number of high-profile people in the prion world, including Gajdusek, are clarified as not believing sporadic prion diseases exist. You get the impression DTM doesn’t, either. Now, how common are prion diseases? Eric Vallabh Minikel has an answer for you! Eric and his wife Sonia are prion researchers from a rather unique background – after Sonia was diagnosed as having a single-gene mutation with ~100% penetrance for prion disease, they left their previous jobs to dedicate their lives to curing it. It turns out, when you run the numbers, you get not one in a million but 1 in 5000 people dying of prion diseases. This is best described as “nightmarishly high”. I’m normed on genetic disorders. A genetic disorder that affects one in five thousand people is pretty common! I have known, in person, completely unselected, just from “random people I’ve met in my life in a non-medical context”, someone with a ~1/250k syndrome and someone with a ~1/50k-100k syndrome. I don’t think anyone in my extended family knows someone who died of a prion disease. I feel like it would’ve come up if they did! Prion diseases have distinctive phenotypes. Not distinctive enough, apparently, to avoid a lot of CJD being misdiagnosed as Alzheimer’s – but diagnosis is consistently insane. Something DTM reiterates throughout The Family That Couldn’t Sleep is just what prion dementia looks like. The characteristic dementia in prion diseases spares something – “self” or “recognition” or “reflection” – that is not spared by Alzheimer’s, or by most common dementias. Shouldn’t this be, uh, noticeable?7 They kill rapidly, often over the course of months, and often onset in midlife. ALS shares this pattern and is way, way more common than prion diseases; you hear about ALS far more in the “disorder people actually have” sense. What am I missing here? Anyway: 1 in 2000 prevalence of latent vCJD in the UK + extreme lack of clarity over whether scrapie is human-transmissible + blood donations spread vCJD + sporadic CJD prevalence keeps going up = ??? (Yes, I am annoyed that most countries have lifted their ban on UK blood donors, thank you for asking!) --------------------------------------------------------- But back to the book. The “American chapter” is one-third about the country’s response to vCJD, one-third about the Creutzfeldt Jakobins, and one-third about chronic wasting disease. The last part is the most interesting. Chronic wasting disease is a prion disease of deer. Like scrapie, it “probably, we hope” isn’t human-transmissible (eat venison at your own risk). Under natural circumstances, deer shouldn’t get prion diseases: A prion plague should not be possible among ruminants in the wild. Deer are not cannibals, as the cows that spread BSE were forced to be; and, because deer and elk are not domesticated, they do not have enough contact with one another to spread a prion infection the way sheep are thought to spread scrapie. But deer do not live as they used to live, humans having once again brought their ambitions to bear on the natural course of things. The Family That Couldn’t Sleep is a book of medical anthropology. Anthropology of the Veneto, anthropology of Papua New Guinea, anthropology of 1990s Britain. Here, it is an anthropology of America. Americans, having won the world, still fight to win their own backyard. The North American continent is geographically diverse, cutting through rain-snow-shine, mountains jutting over plains, cities sprawling into wilderness, habitations criss-cross dotted with surprisingly few empty zones. Go somewhere like Denver, the Mile High City, three million people fighting against nature. Few other countries have anything like this; geographically vast polities usually have uninhabitable blocks. Australians are twenty-five million people clustered against the shore. It still surprises me, after all this time, how every US state has a meaningful city8. Midcentury Denver, growing and sprawling out across its mountains, started to run into their natural inhabitants – deer. Starvation is one way nature adjusts the deer population to the available food supply. People did not usually see this process, but in the 1950s and 1960s Colorado became more densely settled, reducing forested areas and forcing deer to look longer and harder for food. At the same time, the state enacted conservation laws, limiting when and where hunters could shoot. Soon emaciated deer began wandering onto the lawns and through suburban streets looking for a meal. People began to feed them, only to find that they died anyway. They would drop dead by haystacks, along highways, and in flower beds. In the late 1960s, a young biologist named Gene Schoonveld tried to figure out why the deer starved even when they were fed.9 He deprived some deer of food for a while, “[h]e cut windows in their stomachs to see what went on inside, and then he began to feed them”. While this was going on, he had a control group of healthy, well-fed deer as backups in case anything went wrong. It did...but not to the experimental group. The pen in which the deer were kept also housed sheep, which, it turned out, were scrapie carriers. The deer somehow acquired scrapie – there’s a huge unanswered question here, which DTM doesn’t address. How did they get scrapie? They didn’t eat the sheep, presumably. Did it somehow transmit from casual contact? This is not supposed to happen. And yet: the deer in the sheep pen started dying of a mysterious scrapie-like disease, one never reported before, that would go on to infect thousands. These deer were released into the wild. Ten years later, the first reports of chronic wasting disease came out. The disease spread across deer and elk in the western half of the country. By the turn of the millennium, cases were exploding – and lost all geographical restriction. DTM can report up to 2005, at which point it was floating around Upstate New York. This kind of spread doesn’t track natural deer migration. That’s irrelevant, because nothing about CWD’s spread is natural. We shift gears into an anthropology of the American hunter. The hunter wants to shoot the most impressive buck, to bag himself one with as many “points” as possible – one whose antlers branch out most. A “ten-point buck” has five branches on each horn: Original by Ric McArthur Nature doesn’t make enough bucks with perfectly symmetrical ten-point horns. To fill the demand, the market had to step in. Thus was born the deer farm industry, which raises captive deer in better genetic and nutritional conditions than Nature permits, then ships them across the country so hunters who couldn’t get legit ten-point bucks get the taxidermy piece for their wall. These are controversial amongst hunters and illegal in numerous states – but the industry is big enough to spread CWD. (The kind of hunter who needs a deer shipped to his house is the kind of hunter who will fumble killing it.) Another problem is supplemental feeding – leaving out protein-enriched food for deer to eat. This produces “trophy class animals at an earlier age”, but again, what’s in that protein? (“It is much like feeding your cows 41 percent protein cottonseed cake during the winter to raise the protein level in the cow’s diet to a level that will maintain acceptable production”, says that article from 1991.)10 The book segues into a vignette. CWD was new in Wisconsin in the early 2000s, and the state’s Department of Natural Resources was optimistic it could eradicate it. In a state with a love of hunting, you could, in theory, recruit people to kill every single deer in a 400-square-mile radius: In many states, the state would have had to call out the National Guard for such an onslaught, but hunting is a passion in Wisconsin. Hunters shoot 450,000 deer every year, more than in any other state. “I’m looking for ardent hunters to help us, unless fear or their wives keep them away,” one DNR official told a Milwaukee magazine. The state extended the normal hunting season and waived the usual limit of one buck per hunter, and the hunters came out in force. The whole affair was gruesome – one official called it “hunting for slob hunters”. If you’re trying to eradicate a prion disease, you can’t very well let people take the carcasses home to eat. Bodies piled up in control stations, decomposition mingling with bleach. The 2002 hunt established a base rate of 2% for chronic wasting disease in Wisconsin deer, with the most affected areas getting up to 10%. Further hunts in 2003, 2004, and 2005 spread to wider and wider areas – and didn’t move the needle one bit. This is to say that CWD is quite a bit more common in the American deer population than BSE ever was in British cattle. Since publication, it’s popped up in Norway and South Korea. Notably, Norway doesn’t allow for the import of cervids, raising numerous questions about how it got there. There are no unambiguous cases of CWD transmission to humans, and in vivo/in vitro primate studies have mixed results. There sure are some unusually young hunters with sporadic CJD, though. But don’t worry, most of them aren’t M/M homozygotes! There is an absolute ton going on in this book. I’ve had to skim over whole sections. Parts that couldn’t be easily slotted into a narrative review include: When Gajdusek was invited to a party at Prusiner’s house, he was horrified to find his rival had purchased hundreds of New Guinean statues – all with the genitals removed.
Inline links: 2, Lyttle Lytton, 3, 4, a couple years later, concerningly transmissible to primates, omnigenic model, you might, 5, SNPedia, a 2009 post, https://substackcdn.com/image/fetch/$s_!N93S!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fced2eca3-ec58-40b8-8c13-debdb559ab8f_651x601.png, Yeah., nope, M/V heterozygotes totally get vCJD, 6, up to 1 in 2000 people in the UK have latent vCJD, bleach enemas, has an answer for you, 7, blood donations spread vCJD, going, up, 8, 9, not supposed to happen, https://substackcdn.com/image/fetch/$s_!0J8B!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2a64b287-a7c6-4d82-bbe1-da949fc93118_1024x683.png, Ric McArthur, controversial, amongst, hunters, illegal in numerous states, trophy class animals at an earlier age, 10, Norway, South Korea, mixed results, sure are
Yeah. The conclusion DTM drew – and this was a common conclusion at the time – was that homozygosity somehow made you more vulnerable to CJD, and M/M homozygosity made you vulnerable to BSE-borne CJD in particular. We cannot criticise the author for not predicting the future, but we live in the future, and can say how this worked out. Turns out, nope, M/V heterozygotes totally get vCJD. After a British man in his 30s died of CJD in 2016, he was found to have vCJD and an M/V genotype. He was tested for vCJD only because he was exceptionally young for someone with a sporadic prion disease – meaning people developing it later in life would be missed6. Did you know up to 1 in 2000 people in the UK have latent vCJD? There is one line in The Family That Couldn’t Sleep that stopped me dead in my tracks when I read it: What happens to the Italian family in the end depends less on their own actions than on the world’s interest in prion diseases, which they cannot control. If lots of people are afraid of getting variant CJD, the family benefits. If fear of prion disease goes the way of the fear of swine flu or Ebola, then they will be orphaned again. THIS BOOK IS FROM 2006! Three years before the swine flu pandemic! Eight years before the Ebola pandemic! “If you’re looking for a sign, this is it.” --------------------------------------------------------- The last section of The Family That Couldn’t Sleep addresses BSE fears in America and a nascent internet subculture DTM calls “Creutzfeldt Jakobins” – people who track American CJD cases, trying to spot vCJD patterns. When reading his description of the Creutzfeldt Jakobins, my mind constantly, uncontrollably turned to covid. Here it was – an online community of people deeply skeptical about a disease’s official story, tracking every contradiction, every implausibility, every statistic that failed to apply to the individual. Self-described “redneck hippies” and “soccer mom Republicans” teaming up to find the truth hidden behind an impossible world. You know what they’re doing now. I’ve always combined a deep interest in medicine with a healthy distrust for it. People who are constitutionally inquisitive, anti-authoritarian, and suspicious about official narratives tend to end up skeptical of at least some mainstream claims in the field. This is not to say I think you should take bleach enemas or something, just that I understand the impulse behind concluding the US government was covering up a local vCJD wave. Traditionally, sporadic prion diseases are said to have a prevalence of one in a million. (Hold on to that for a second.) The last section of the book is a chronology of Americans finding bizarrely more than one in a million of their friends dying of sporadic CJD, often at inexplicably young ages, sometimes in geographical clusters. This is understandably suspicious. Then DTM goes on to reassure us by saying none of these cases were confirmed to have an M/M genotype, which OH GOD OH FUCK A number of high-profile people in the prion world, including Gajdusek, are clarified as not believing sporadic prion diseases exist. You get the impression DTM doesn’t, either. Now, how common are prion diseases? Eric Vallabh Minikel has an answer for you! Eric and his wife Sonia are prion researchers from a rather unique background – after Sonia was diagnosed as having a single-gene mutation with ~100% penetrance for prion disease, they left their previous jobs to dedicate their lives to curing it. It turns out, when you run the numbers, you get not one in a million but 1 in 5000 people dying of prion diseases. This is best described as “nightmarishly high”. I’m normed on genetic disorders. A genetic disorder that affects one in five thousand people is pretty common! I have known, in person, completely unselected, just from “random people I’ve met in my life in a non-medical context”, someone with a ~1/250k syndrome and someone with a ~1/50k-100k syndrome. I don’t think anyone in my extended family knows someone who died of a prion disease. I feel like it would’ve come up if they did! Prion diseases have distinctive phenotypes. Not distinctive enough, apparently, to avoid a lot of CJD being misdiagnosed as Alzheimer’s – but diagnosis is consistently insane. Something DTM reiterates throughout The Family That Couldn’t Sleep is just what prion dementia looks like. The characteristic dementia in prion diseases spares something – “self” or “recognition” or “reflection” – that is not spared by Alzheimer’s, or by most common dementias. Shouldn’t this be, uh, noticeable?7 They kill rapidly, often over the course of months, and often onset in midlife. ALS shares this pattern and is way, way more common than prion diseases; you hear about ALS far more in the “disorder people actually have” sense. What am I missing here? Anyway: 1 in 2000 prevalence of latent vCJD in the UK + extreme lack of clarity over whether scrapie is human-transmissible + blood donations spread vCJD + sporadic CJD prevalence keeps going up = ??? (Yes, I am annoyed that most countries have lifted their ban on UK blood donors, thank you for asking!) --------------------------------------------------------- But back to the book. The “American chapter” is one-third about the country’s response to vCJD, one-third about the Creutzfeldt Jakobins, and one-third about chronic wasting disease. The last part is the most interesting. Chronic wasting disease is a prion disease of deer. Like scrapie, it “probably, we hope” isn’t human-transmissible (eat venison at your own risk). Under natural circumstances, deer shouldn’t get prion diseases: A prion plague should not be possible among ruminants in the wild. Deer are not cannibals, as the cows that spread BSE were forced to be; and, because deer and elk are not domesticated, they do not have enough contact with one another to spread a prion infection the way sheep are thought to spread scrapie. But deer do not live as they used to live, humans having once again brought their ambitions to bear on the natural course of things. The Family That Couldn’t Sleep is a book of medical anthropology. Anthropology of the Veneto, anthropology of Papua New Guinea, anthropology of 1990s Britain. Here, it is an anthropology of America. Americans, having won the world, still fight to win their own backyard. The North American continent is geographically diverse, cutting through rain-snow-shine, mountains jutting over plains, cities sprawling into wilderness, habitations criss-cross dotted with surprisingly few empty zones. Go somewhere like Denver, the Mile High City, three million people fighting against nature. Few other countries have anything like this; geographically vast polities usually have uninhabitable blocks. Australians are twenty-five million people clustered against the shore. It still surprises me, after all this time, how every US state has a meaningful city8. Midcentury Denver, growing and sprawling out across its mountains, started to run into their natural inhabitants – deer. Starvation is one way nature adjusts the deer population to the available food supply. People did not usually see this process, but in the 1950s and 1960s Colorado became more densely settled, reducing forested areas and forcing deer to look longer and harder for food. At the same time, the state enacted conservation laws, limiting when and where hunters could shoot. Soon emaciated deer began wandering onto the lawns and through suburban streets looking for a meal. People began to feed them, only to find that they died anyway. They would drop dead by haystacks, along highways, and in flower beds. In the late 1960s, a young biologist named Gene Schoonveld tried to figure out why the deer starved even when they were fed.9 He deprived some deer of food for a while, “[h]e cut windows in their stomachs to see what went on inside, and then he began to feed them”. While this was going on, he had a control group of healthy, well-fed deer as backups in case anything went wrong. It did...but not to the experimental group. The pen in which the deer were kept also housed sheep, which, it turned out, were scrapie carriers. The deer somehow acquired scrapie – there’s a huge unanswered question here, which DTM doesn’t address. How did they get scrapie? They didn’t eat the sheep, presumably. Did it somehow transmit from casual contact? This is not supposed to happen. And yet: the deer in the sheep pen started dying of a mysterious scrapie-like disease, one never reported before, that would go on to infect thousands. These deer were released into the wild. Ten years later, the first reports of chronic wasting disease came out. The disease spread across deer and elk in the western half of the country. By the turn of the millennium, cases were exploding – and lost all geographical restriction. DTM can report up to 2005, at which point it was floating around Upstate New York. This kind of spread doesn’t track natural deer migration. That’s irrelevant, because nothing about CWD’s spread is natural. We shift gears into an anthropology of the American hunter. The hunter wants to shoot the most impressive buck, to bag himself one with as many “points” as possible – one whose antlers branch out most. A “ten-point buck” has five branches on each horn: Original by Ric McArthur Nature doesn’t make enough bucks with perfectly symmetrical ten-point horns. To fill the demand, the market had to step in. Thus was born the deer farm industry, which raises captive deer in better genetic and nutritional conditions than Nature permits, then ships them across the country so hunters who couldn’t get legit ten-point bucks get the taxidermy piece for their wall. These are controversial amongst hunters and illegal in numerous states – but the industry is big enough to spread CWD. (The kind of hunter who needs a deer shipped to his house is the kind of hunter who will fumble killing it.) Another problem is supplemental feeding – leaving out protein-enriched food for deer to eat. This produces “trophy class animals at an earlier age”, but again, what’s in that protein? (“It is much like feeding your cows 41 percent protein cottonseed cake during the winter to raise the protein level in the cow’s diet to a level that will maintain acceptable production”, says that article from 1991.)10 The book segues into a vignette. CWD was new in Wisconsin in the early 2000s, and the state’s Department of Natural Resources was optimistic it could eradicate it. In a state with a love of hunting, you could, in theory, recruit people to kill every single deer in a 400-square-mile radius: In many states, the state would have had to call out the National Guard for such an onslaught, but hunting is a passion in Wisconsin. Hunters shoot 450,000 deer every year, more than in any other state. “I’m looking for ardent hunters to help us, unless fear or their wives keep them away,” one DNR official told a Milwaukee magazine. The state extended the normal hunting season and waived the usual limit of one buck per hunter, and the hunters came out in force. The whole affair was gruesome – one official called it “hunting for slob hunters”. If you’re trying to eradicate a prion disease, you can’t very well let people take the carcasses home to eat. Bodies piled up in control stations, decomposition mingling with bleach. The 2002 hunt established a base rate of 2% for chronic wasting disease in Wisconsin deer, with the most affected areas getting up to 10%. Further hunts in 2003, 2004, and 2005 spread to wider and wider areas – and didn’t move the needle one bit. This is to say that CWD is quite a bit more common in the American deer population than BSE ever was in British cattle. Since publication, it’s popped up in Norway and South Korea. Notably, Norway doesn’t allow for the import of cervids, raising numerous questions about how it got there. There are no unambiguous cases of CWD transmission to humans, and in vivo/in vitro primate studies have mixed results. There sure are some unusually young hunters with sporadic CJD, though. But don’t worry, most of them aren’t M/M homozygotes! There is an absolute ton going on in this book. I’ve had to skim over whole sections. Parts that couldn’t be easily slotted into a narrative review include: When Gajdusek was invited to a party at Prusiner’s house, he was horrified to find his rival had purchased hundreds of New Guinean statues – all with the genitals removed.
Inline links: Yeah., nope, M/V heterozygotes totally get vCJD, 6, up to 1 in 2000 people in the UK have latent vCJD, bleach enemas, has an answer for you, 7, blood donations spread vCJD, going, up, 8, 9, not supposed to happen, https://substackcdn.com/image/fetch/$s_!0J8B!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2a64b287-a7c6-4d82-bbe1-da949fc93118_1024x683.png, Ric McArthur, controversial, amongst, hunters, illegal in numerous states, trophy class animals at an earlier age, 10, Norway, South Korea, mixed results, sure are
Let’s pretend for rhetorical purposes that somewhere like Cheyenne, Wyoming is a meaningful city. The metro area is 100k people – it’s meaningful enough. The equivalent spot in Australia has a population of “no one”.
Highlights From The Comments On Mentally Ill Homeless People
July 18, 2024 · Original source
HemiDemiSemiName on the Australian system:
I live in Australia. We have 1., we effectively have 2. for mentally ill homeless people (I think public transport staff are told to let anyone who's visibly mentally ill but not too distressed through the gates), and I have no idea if 3. already exists or not.
Your Book Review: How the War Was Won
August 09, 2024 · Original source
Data from HtWWW, recreated to improve image quality. German oil shortages caused exactly the same training problem Japan had faced, with a slightly different but similarly disastrous outcome. Japanese training and production problems led to planes not arriving where they were supposed to in fighting condition (perhaps as few as 10% were actually combat capable when they arrived!) For Germany, training shortfalls meant annihilation for their air force as inexperienced pilots were forced to fight numerically and qualitatively superior American and British pilots. German monthly aircraft lost/damaged rates increased from 52.5% in January 1944 to 96.3% in June. One particularly illuminating episode illustrates how these problems manifested for Germany. The German air force had a reserve of 800 aircraft to counter the D-Day landings. The pilots of that force were used to only flying under expert control systems in Germany (countering bombing raids). When they went to France, they had trouble navigating and often landed on the wrong fields. Ultimately, they were poorly prepared to fight. The head of German fighter command was certain that the entire reserve did not destroy even two dozen Allied aircraft. American/British Airpower Decided the Outcome of Land Battles Beyond the strategic effects of bombing, tactical airpower (i.e., airplanes attacking land forces) gave an insurmountable advantage to the western Allies’ land forces. After D-Day, the Germans had a very strong defensive position in the hedgerows of northwest France. Allied aircraft literally carpet bombed one of the strongest divisions in the German army out of existence, with 70% casualties in one day. That division would normally have approximately 200 AFVs. At the end of that one day of bombing, it had 14. The Battle of the Bulge, the last offensive by the Germans to drive back the western Allies’ advance, was almost pathetic in its hopelessness. We Americans tend to focus on the hard fighting at the outset of the battle, and the stout resistance of the 101st Airborne at Bastogne. Knowing that airpower would make their attack impossible, the Germans timed the battle for bad weather and prayed it lasted as long as possible. Prayer was really the only option. Once the skies inevitably cleared after a little over a week of bad weather, more than 2,000(!) Allied bombers destroyed the German offensive. With most logistical support wiped out, one famous German division had to abandon all its vehicles and walk back to Germany. Criticism of HtWWW as a Book: Love the Data, (Mostly) Don’t Care About the People My single biggest criticism of HtWWW is O’Brien spends a lot of time (I would estimate 20% of the book) discussing the relative importance and influence of various people in the United States and United Kingdom. The section on Doug MacArthur is worth a longer digression, which I have included below. The problem is that focusing on personnel is almost completely irrelevant to the main argument of the book. For example, it is modestly interesting that Franklin Roosevelt, consistent with advice from Harry Hopkins and Admiral Ernest King, focused America’s productive effort on air and sea power. It is not at all central to the argument that air and sea power won the war. The fact that these particular people thought it was a good idea to build planes and ships matters less than the outcome that the U.S. did exactly that. I am very much interested in World War II history, and on an interestingness scale of 1-10, I found this discussion to be at about a 4. The central argument of the book about German and Japanese production was a consistent 10. Sidenote: MacArthur Was a Disastrous General In the part of the book focused on personnel, the one discussion that hit around a 9 or 10 was of Douglas MacArthur and the invasion of the Philippines. MacArthur was the American general commanding the defense of the Philippines. The Japanese conquered the Philippines, and MacArthur slipped away to Australia, heroically vowing, “I shall return.” He did in December 1944, and some of the worst fighting of the war took place, with massive casualties for the Americans, Japanese, and Filipino civilians. Fighting was still ongoing in the Philippines when the war ended in August 1945. The Americans took more than 220,000 casualties, the Japanese 430,000. Estimates vary on Filipino civilian deaths, but 750,000 is a credible middle of the road estimate. O’Brien’s contribution here was pointing out the strategic pointlessness of MacArthur’s invasion. The big American strategy in the western Pacific was to penetrate the Japanese defensive line of islands to link up with China. The northern Marianas Islands also were within heavy bomber range of Japan, and so would allow for efficient, effective bombing. (Bombing Japan from bases in China were logistically impractical, with virtually all materials being flown in over the Himalayas—another fascinating logistics discussion in this book.) The Americans had already conquered the Marianas Islands and had total air and sea dominance in the western Pacific. The forces the Japanese had in the Philippines could have been simply left to wither, as they had been on other islands bypassed by the island-hopping campaign. So, why did the Philippines invasion happen? The inescapable conclusion is that MacArthur was too politically formidable to risk angering, and he personally wanted to invade the Philippines to make good on his promise to return. Not coincidentally, the Philippines also offered some prospect of an extended land campaign where MacArthur could improve his reputation after his disastrous original defense of the Philippines. Also relevant, in O’Brien’s words: “MacArthur [] dazzled Roosevelt with tales of easy victories and grateful Filipinos and American voters.” Criticisms of HtWWW’s Central Argument I think it is clear from the data that O’Brien’s argument, that air and sea power played a more important role than land battles in deciding the war, is fundamentally right. Still, one can raise a few objections. Individual naval battles were capable of destroying a significant percentage of overall production. O’Brien discusses the Battle of Midway, where the Japanese lost four aircraft carriers (37 percent of their navy’s aircraft carriers at the time, 22 percent of all carriers they had during the war). This point doesn’t really disprove O’Brien’s core argument—it is basically a footnote saying that individual naval battles are more likely to matter than individual land battles. Politics and psychology matter tremendously in war, sometimes more than productive effort. O’Brien tacitly acknowledges this in the V-2 weapons discussion when he notes that the Germans spent all this money and effort on a psychological salve to the trauma of Allied bombing. The Japanese did ultimately surrender after the atomic bombings. (Or, if you are more on the revisionist end of the spectrum, they surrendered after the Soviets declared war.) France surrendered after a few disastrous battles. The productive effort lens might be useful, but subject to important caveats. Why Does the Conventional Narrative Focus on Battles? A perfect companion book to HtWWW would examine why military historians and the broader public have focused inordinately on battles. Here are some plausible factors: Battles are more dramatic. Propaganda during the war focused on battles so that there would be more inherent drama. Working twelve hour shifts in a factory to win the great battle is probably psychologically easier than thinking your work is going to disappear into an inchoate slog.
Meetups Everywhere 2024: Times & Places
August 29, 2024 · Original source
Contact: Ozge Contact Info: ozgeco[at]yahoo[dot]com Time: Saturday, October 05th, 02:00 PM Location: Kadikoy, Rıhtım, Yeni Iskele Upstairs, Istanbul Kitapcisi Kahve Dunyasi Coordinates: https://plus.codes/8GGFX2VF+4F Notes: This time we organize this meeting together with AI Safety Istanbul Group. Everybody warmly welcomed. Asia-Pacific Australia ALBURY, AUSTRALIA Contact: BK Contact Info: podcastaffix[at]gmail[d ot]com Time: Tuesday, September 17th, 06:30 PM Location: Mitta Mitta Canoe Club building in Noreuil Park Coordinates: https://plus.codes/4RM8WW73+2P7 Notes: Alcohol free venue, please bring snacks
Inline links: https://plus.codes/8GGFX2VF+4F, https://plus.codes/4RM8WW73+2P7
Contact: BK Contact Info: podcastaffix[at]gmail[d ot]com Time: Tuesday, September 17th, 06:30 PM Location: Mitta Mitta Canoe Club building in Noreuil Park Coordinates: https://plus.codes/4RM8WW73+2P7 Notes: Alcohol free venue, please bring snacks BRISBANE, AUSTRALIA Contact: Laura Contact Info: laura[d ot]leighton94[at ]gmai l[dot]com Time: Sunday, September 15th, 05:00 PM Location: The Burrow, West End. We might be either upstairs or downstairs. I will have a sign that says ACX meetup. Coordinates: https://plus.codes/5R4MG2C7+44M
Inline links: https://plus.codes/4RM8WW73+2P7, https://plus.codes/5R4MG2C7+44M
Contact: Laura Contact Info: laura[d ot]leighton94[at ]gmai l[dot]com Time: Sunday, September 15th, 05:00 PM Location: The Burrow, West End. We might be either upstairs or downstairs. I will have a sign that says ACX meetup. Coordinates: https://plus.codes/5R4MG2C7+44M CANBERRA, AUSTRALIA Contact: Declan Contact Info: declan_t[at]hotmail[d ot]com Time: Monday, October 07th, 06:00 PM Location: Grease Monkey Braddon Coordinates: https://plus.codes/4RPFP4GM+R3 Notes: Please RSVP by previous Friday for table booking.
Inline links: https://plus.codes/5R4MG2C7+44M, https://plus.codes/4RPFP4GM+R3
Links For September 2024
September 12, 2024 · Original source
50: Did you know: one of the landmark cases on gender transition in Australia is called Tickle v. Giggle, and Australian gender warriors have to have strong opinions on Tickle v. Giggle and its ramifications. This seems like a good (albeit unintentional) way to make people feel embarrassed to center their entire political identity around this topic.
Inline links: one of the landmark cases on gender transition in Australia is called
24: Another day, another NYT doxxing scandal: an NYT reporter joined a group chat of Jews talking about how they dealt with anti-Semitism. Then she shared the names of everyone in the group with someone who leaked it to anti-Israel activists. The activists proceeded to harass, stalk, threaten, and vandalize group members. NYT says that unspecified “disciplinary action” has been taken against the reporter, which apparently does not include firing her, demoting her, or any other effect observable in the physical world.
Inline links: NYT doxxing scandal
Open Thread 347
September 16, 2024 · Original source
1: Good comments on last week’s links post: Andy McKenzie on whether selection really disproves balancing theories of personality and schizophrenia, and multiple layers of clarification on the Australia/Jews/NYT doxxing story. And several people had good comments on Oregon’s now-repealed drug decriminalization law. Banjo Kildeer blames the law for offering addicts the choice between a $100 fine vs. treatment; the fine was so low that almost everyone paid and kept using. Kerry blames the police for not enforcing it properly. And an email correspondent linked this study suggesting that Oregon’s increase in drug deaths had nothing to do with the law, but was a simple effect of growing fentanyl availability.
Inline links: links post, on whether selection really disproves balancing theories of personality and schizophrenia, multiple layers of clarification on the Australia/Jews/NYT doxxing story, blames the law for, blames the police for, this study suggesting that
Meetups Everywhere Spring 2025: Times & Places
March 25, 2025 · Original source
Asia-Pacific (including Australia)
Contact: Mike Contact Info: lumenwrites[a t]gmail[period]com Time: Sunday, April 13th, 4:00 PM Location: SpartaCUEs Board Game Centre, 2nd floor. Coordinates: https://plus.codes/7HQQ46M8+6Q Group Link: https://chat.whatsapp.com/BfI [remove this bit] iv6EMJOZIVVhTiNocqj Notes: Please message me (+971507349246, or via WhatsApp group) at least a day in advance to let me know that you'll be joining. Asia-Pacific Australia BRISBANE, AUSTRALIA Contact: Laura Contact Info: laura[period]leighton94[a t]gmail[period]com Time: Sunday, April 13th, 5:00 PM Location: The Burrow, West End - we will be downstairs and probably towards the back where it tends to be quieter Coordinates: https://plus.codes/5R4MG2C7+44M Notes: This event will be co-hosted with the regular meetup of Effective Altruism Brisbane as there's usually a ~75% overlap in attendance.
Inline links: https://plus.codes/7HQQ46M8+6Q, https://plus.codes/5R4MG2C7+44M
Contact: Laura Contact Info: laura[period]leighton94[a t]gmail[period]com Time: Sunday, April 13th, 5:00 PM Location: The Burrow, West End - we will be downstairs and probably towards the back where it tends to be quieter Coordinates: https://plus.codes/5R4MG2C7+44M Notes: This event will be co-hosted with the regular meetup of Effective Altruism Brisbane as there's usually a ~75% overlap in attendance. CANBERRA, AUSTRALIA Contact: Declan Contact Info: acxcanberra[a t]outlook[period]com Time: Monday, April 7th, 6:00 PM Location: The Snug Room (up the stairs behind the bar), King O'Malley's Coordinates: https://plus.codes/4RPFP4CJ+MC Notes: No RSVP needed, but please put 'ACX' in the subject if you email.
Inline links: https://plus.codes/5R4MG2C7+44M, https://plus.codes/4RPFP4CJ+MC
Open Thread 376
April 07, 2025 · Original source
4: The Australian branch of our conspiracy is seeking AI safety commitments from the major parties before the upcoming election. Australia doesn’t have any frontier AI companies itself, it does have impressive diplomatic muscle and research talent, and the experts I’ve talked to think it’s could be an important leverage point. If you’re Australian (or have Australian connections) and want to help, see here for the open letter, letter-writing campaign, and more information.
Inline links: see here
Highlights From The Comments On AI Geoguessr
May 08, 2025 · Original source
I then tested for a relatively well-shot, 1000km altitude environment in Kyrgyzstan, with ample geology and foliage to analyse, and it was over 6000 km off (it guessed Colorado), and none of the guesses were even in Asia. But this was in under 2 mins. I told it to try again- over 5k km away, it took 7 mins, and it suggested Australia, Europe, NZ, Argentina etc. Nothing in central Asia.
ACX Grants 1-3 Year Updates
June 18, 2025 · Original source
This is one of my favorite projects - a veteran Australian lobbyist was a prolific ACX commenter, and we gave them an exploratory grant to start an organization there. After some trial and tribulations, this turned into Good Ancestors. More updates on what they’ve been doing lately in the 2024 grants section.
Inline links: Good Ancestors
Codebuff, an AI coding startup I probably can’t take full credit for all of this just from giving them $20K in seed funding, but I continue to appreciate everything they do for this community and the world. 35: Further S’s Political Career This person didn’t win their election, but has since pivoted to AI safety and works in a well-regarded AI policy think tank. 36: Seeds Of Science, A Journal Of Non-Traditional Research No update received, but this was a public journal and it is easy to follow their work, see their website and Substack. They published two dozen articles of widely varying quality through 2023 and 2024, then closed in 2025. A remnant of the original vision survives as a science blogging aggregator. This was about my median expectation for this grant, but it was very inexpensive and I decided to take a chance on it anyway. 37: Good Science Project, Working To Improve Federal Science Funding No update received, but they have a public Substack discussing their progress. Their proposals for NIH reform have influenced Congress and made government agencies pay more attention to scientific integrity. 38: Advising Developing Countries On How To Grow Their Economies With our initial ACX grant, we piloted the Growth Teams model in Rwanda, helping the government jumpstart the export-oriented call center (BPO) industry. Since 2022, that effort has contributed to the creation of 2,000 formal jobs and the emergence of some of the country’s largest private employers. We’ve since expanded to Tanzania, Malawi, and the Indian states of Goa and Meghalaya. To refocus the global development discourse on broad-based economic growth, we co-organized the Growth Summit with the Center for Global Development and the Charter Cities Institute, and have published articles in leading outlets including Stanford Social Innovation Review, ProMarket, and the Global Prosperity Institute. Our work has attracted support from Open Philanthropy, Schmidt Futures, and Mulago Foundation, and our advisors now include economists Lant Pritchett, Stefan Dercon, and Kunal Sen. 39: Help Luca De Leo Get Started In AI Safety Research No update received, but Luca now runs the AI safety group at the University of Buenos Aires, Argentina. 40: Typist For Saharon Shelah This was another ACXG+ Grant, funded by an anonymous outside funder and not listed in the original announcement. Saharon is a prolific and influential Israeli mathematician, but many of his discoveries are hand-written in an unpublishable format. This grant funded a typist to help make his results suitable for publication. According to this page, they have made over fifty new papers and preprints available. Second Cohort: One Year Updates 41: Lead-Acid Battery Recycling In Nigeria The Nigeria field research was a major success. We spent most of September doing field research in multiple major cities in Nigeria, and got a good sense of the used lead-acid battery supply chain. This field research served as the foundation for expanding our project, and has been very impactful in shaping our ongoing research. We published our findings from Nigeria, which were shared with Nigerian government regulators and global NGOs working on lead poisoning. The grant also gave us the on-the-ground experience we needed to both fully understand and credibly engage with groups, both in Nigeria and globally, on the ULAB issue. In the meantime, beyond continued research, we’ve also launched a dashboard (trade.leadbatteries.org) for analyzing global lead trade data. Right now, we’re: Launching two studies (one RCT, one environmental analysis) in Nigeria in collaboration with local universities to develop a more rigorous understanding of lead pollution due to low-standard ULAB recycling in Nigeria Collaborating with a non-profit incubator to launch an NGO focused on demand-side solutions Beginning a partnership with a West African environmental regulator to scale cheap air monitoring technology to quickly identify and reduce lead pollution from low-standard smelting If any of this sounds interesting to you, please sign up for our Substack (leadbatteries.substack.com) or send us an email at hugosmith@uchicago.edu! 42: Compensation For Kidney Donors The End Kidney Deaths Act (H.R. 2687 / EKDA) is a groundbreaking ten-year pilot program designed to save lives and reduce healthcare costs. It provides a refundable tax credit of $10,000 per year for five years, a total of $50,000, to living kidney donors who donate to a stranger, helping those who’ve waited the longest on the transplant list. Between 2010 and 2021, 100,000 Americans died while qualified and waiting for a kidney. The EKDA aims to change that trajectory. Within ten years of its passage, up to 100,000 Americans could receive a life-saving living donor kidney which typically lasts twice as long as a deceased donor kidney. This would not only save lives but also save taxpayers up to $37 billion. The legislation has been reintroduced in the House, and we have a committed Republican Senate lead. Now, we need a Democratic Senator to co-lead and help move this bipartisan effort forward. Time is short, and we are racing to pass the bill this Congressional session. 36 organizations already support the EKDA. Join the movement and help end preventable kidney deaths. Visit EndKidneyDeaths.org to help us get to the finish line. Elaine and her org have been working extremely hard on this; you can read a Vox article on their campaign here. If you want to sign up for her email list and get updates any time there is a representative you can contact or meeting you can join in, go here. 43: Genetic Hack To Prevent Suffering In the estimate of multiple team members, the ACX grant was “worth it” - it likely had a counterfactual net positive impact, even though we had to pivot from our initial fast-track plans for developing the precision anti-suffering therapy. We identify three primary streams of value: a) reducing uncertainty in the emerging field through early exploratory research, helping with the identification of dead ends and promising R&D trajectories; b) a wide range of downstream effects (beyond the “raising awareness” cliché), including talent mobilization and rekindled interest in suffering abolitionism as a distinct cause area; and c) certain developments that cannot yet be publicly disclosed. In December 2024, Marcin Kowrygo (Acting CEO & volunteering contributor), David Pearce (Director of Bioethics), Aatu Koskensilta (President), and a few other team members decided to leave The Far Out Initiative. They look forward to collaborating and applying their experience to advance the suffering abolitionist lineage in the spirit of open science, public good, and thoughtfully decentralized governance. Feel free to reach out to us at suffab at protonmail dot com to discuss collaboration opportunities! I wrote a post profiling the Far Out Initiative here. Unfortunately there were some internal disagreements, and the people ACX Grants was closest to left the organization. I plan to continue to monitor whatever they do next. 44: Advocate For Pandemic Response Team At FDA This team prefers has asked me not to discuss their progress publicly, but you can probably guess what their lives are like right now, and your guess would be correct. 45: Anti-Mosquito Drones We developed a cheap sonar that is able to detect, track and classify the ultrasonic echoes of mosquito wings at more than three meters. I believe it’s a world first! We also have control algorithms that take the sonar data and output control commands that both ram into mosquitoes and avoid the walls of a simulated environment. Our current work is on integrating both components on a real drone, and we expect to be able to kill mosquitoes by June. We’ve also made an internal impact study (napkin-sized) that shows we’ll be more cost-effective than ITNs in urban to periurban environments. So, we’re super excited with what comes next and can’t wait to share the videos of our first interceptions! More information [in the video below] and on our website, https://tornyol.com 46: Tarbell Fellowship For AI Journalism No update received, but they have a public website. I can’t find the Voices program in particular, but the overall fellowship completed their first class of seven fellows and is working on their second. 47: Germicidal UV Lamp Study The research has successfully demonstrated the ability of off the shelf ozone scrubbers to mitigate the ozone production of far-UVC lamps, is now available as a preprint (https://chemrxiv.org/engage/chemrxiv/article-details/67e4cde76dde43c9084d88b7). The paper has been submitted for publication and is currently undergoing peer review. Any ideas you have for potential funders we can approach to help execute our six-year plan to accelerate far-UVC would be appreciated https://blueprintbiosecurity.org/introducing-project-air/ 48: Technological Solutions To Animal Welfare Challenges Directly because of Innovate Animal Ag's work, the first U.S. egg producer publicly announced in the New York Times their adoption of in-ovo sexing technology, eliminating the need to cull day-old male chicks. The initial in-ovo sexing machine began operating in the U.S. at the end of 2024, with the first eggs from these hens expected on shelves in mid-2025. External evaluations estimate our work accelerated U.S. adoption of this technology by over seven years, meaning that once fully implemented, more than 2 billion chicks will have been spared. In addition to continuing to support the rollout of in-ovo sexing in the US and globally, we're now exploring other technologies and paths to impact. Current promising projects include developing humane slaughter methods for fish and advocating for USDA approval of a poultry vaccine against bird flu. They add: If you ever meet folks that are interested animal welfare and are partial to more technocratic and practical solutions, please continue to pass them our way, or connect them directly to me. 49: Assurance Contract Website www.Spartacus.app is an ACX grantee that created a platform to help solve coordination and collective action problems. It enables the creation of campaigns that build critical mass through conditional commitments, which only activate when a sufficient number of people join, converting risk and uncertainty into a higher probability of successful outcomes. They are currently facilitating several projects that leverage conditional commitments, including a dominant assurance contract interface for fashion pop-ups, accelerating a community business association's membership drive, and helping an AI safety organization organize petitions and events, among others. They have pivoted from an emphasis on high-stakes coordination problems requiring anonymity (because they occur too infrequently) to a broader range of more common use cases and have successfully run small-scale campaigns, but are still working toward product-market fit. Despite resource constraints and split time commitments that have impeded faster progress, they remain dedicated to the project's growth and success. You can follow its progress on X or Substack, or email Jordan directly here. 50: Cause Prioritization @ Center For Exploratory Altruism Research Moderately good progress on a salt reduction policy advocacy project we funded; informal commitments have been made by the Ministry of Health, and we're awaiting the publication of a formal administrative order. The official description sounds maximally generic, but this is an EA charity with a broad mandate whose current thesis is that dietary guidelines in developing countries can have outsized effects in saving lives. They’re making some progress on a salt reduction campaign in a developing country they prefer not to name publicly. 51: Mark Webb Studying Land Reform The purpose of this project was to identify specific farmland that could be acquired and transferred to the farmers already working the land. This has been difficult to achieve. I have been able to connect with other charities and landless farmers, and was able to interview a number of people about what their situation looks like, as well as what it would look like to them personally if they owned, rather than rented, their farmland. All this was immensely helpful in pushing this long-term project forward, even if I was unable to identify a specific plot of land that could be used to try the experiment. I intend to continue this project. If you have any insights or connections, I am interested. 52: More AI Advocacy In Australia Good Ancestors is focused on AI safety policy in Australia. Middle powers might be a useful path to influence as the US and China focus on racing, rather than safety. The ACX grant helped us give testimony about AI safety to the Australian Senate alongside Google, Microsoft and Facebook (We were the only nonprofit to give oral evidence to the inquiry. We also engaged government on other AI-related issues, including cybersecurity, biosecurity, consumer law and automated decision making (https://www.goodancestors.org.au/ai-safety). We’re currently working to inform voters about where parties stand on AI safety for the election, ahead of engaging on a likely Australian AI Act in 2025 (https://www.australiansforaisafety.com.au/). This is the same Australian lobbying organization we founded in Year 1, after a change in name and leadership. I continue to be excited about AI safety in middle-tier countries for a few reasons. First, these countries have some power in international organizations to set international standards. Second, companies will usually comply with any not-excessively-burdensome regulation set by any country with a significant market. Third, AI safety is underfunded by the standard of government programs, so Australia setting up a national AI Safety Institute would significantly expand the field. It’s kind of crazy that ACX Grants tier levels of money can have significant effects at this scale, but GA continues to do a great job and we continue to be proud to support them. 53: Campus For African School Of Economics At Zanzibar Charter City The ACX grant helped launch the first research center at the African School of Economics-Zanzibar, which is a main anchor of the Fumba Town charter city project in Zanzibar. This research center is called the Africa Urban Lab (AUL), focused on rapid urbanization across Africa. The AUL launched its first Diploma program in Urban Development with 38 students in our first cohort (now graduated!), including mayors, and deputy mayor, a director of a national Ministry of urban development, and many others. We published our research framing papers for the AUL's research agenda. We raised funding to launch an Urban Expansion Program that's now selecting 15 African cities to support in implementing urban expansion planning on the urban periphery. We held two Public Talks by renowned cities scholars and practitioners. We received additional funding from Emergent Ventures and from the Templeton Foundation. And we've partnered with 8 universities across the region, and with one of these universities (Ardhi) we'll be working with them to update their urban planning and urban economics curriculum (amplifying AUL's impact beyond our own organization). A longer update from end of 2024 is here: https://www.aul.city/blog/reflecting-on-africa-urban-lab-s-inaugural-year-2024-highlights) 54: Online Training Program For Health Workers In Developing Countries To date, over 11,000 health workers in Nigeria have completed our course on basic, life-saving newborn care. ACX funding was catalytic for helping us secure government approvals and complete an evaluation of the impact of our training on health workers' clinical practices. The evaluation shows that birth attendants provide better birth care after taking the course. We fed the evaluation results into an updated model, which suggests the program is 24 times more cost-effective than direct cash transfers (a widely recognized benchmark for cost-effectiveness). The program is likely to become even more cost-effective as we scale up. https://healthlearn.org/blog/updated-impact-model 55: Smartphone Pupillometry To Diagnose Neurological Conditions We have continued to expand our work in the smartphone pupillometry space and the development of our application, PupilScreen (https://www.apertur.ai/). We have expanded our pilot/research program to include new sites across the United States (Missouri, New Jersey, Kentucky, USAC racing, PitFit driver performance training in Indiana) and the world (Nepal, Taiwan, South Africa). We continue to publish at the leading edge of the pupillometry literature as well looking at concussion (https://neuro.jmir.org/2024/1/e58398 and https://pubmed.ncbi.nlm.nih.gov/39682632/), cerebral vasospasm (https://pubmed.ncbi.nlm.nih.gov/39128501/), and stroke (https://pubmed.ncbi.nlm.nih.gov/39674431/ and https://pubmed.ncbi.nlm.nih.gov/39561861/). Currently, we are raising a $3 million seed round via a SAFE to fund the expansion of our work into the hands of healthcare workers and the general public. We will first focus on traumatic brain injury for clinical use and develop a neuro-monitoring wellness application utilizing our technology for the general public. They add: “We would welcome connections to anyone that you think might be interested in supporting our work further by investing in our $3M seed round of funding.” 56: Mike Saint-Antoine’s Biology Tutorial Videos Since getting the grant, I've continued to make Youtube tutorials as planned. One series that I'm especially proud of is about how to make a neural network in the Julia programming language completely from scratch, with no imports, up to the point of being able to solve MNIST (https://www.youtube.com/playlist?list=PLWVKUEZ25V97tNULapu07DhWv6_W4NfpE). Also, a college student in Pakistan came across my videos and invited me to give a virtual Zoom-lecture to her department, so I ended up teaching a 6-hour "Python-for-Biologists" workshop to more than a hundred college students in Pakistan over Zoom. So that was pretty awesome. Also, lately I've been teaching some in-person classes too, mostly at Fractal University in NYC, and I also recently organized a day-long, in-person Beginner Python class for people in my local area (Philly suburbs) who wanted to learn some basic programming. I'm having a lot of fun with this project, and am grateful to Scott and the grant funders for their generosity! 57: Conceptual Boundaries Workshop On AI Safety The workshop was completed successfully; you can read a writeup here. 58: Apart Research To Incubate AI Safety Scientists No update received, but they have a public website, and you can see their impact metrics here. They seem to be in urgent need of more funding. 59: Primer On How To Achieve Political Change No update received and I can’t find anything about this. 60: Research IVF Clinic Success Rates We've built a predictive model that estimates the odds of having a child at different IVF clinics across the country while controlling for factors like patient age and infertility differences that can falsely make some clinics look better than others. We found that an average patient can increase their odds of having a kid by 43% just by going to a top 10% clinic. Patients unlucky enough to go to a bottom 10% clinic will reduce their odds of having a kid by 40%. Next month, we're adding several more clinics, 2023 data, additional procedural controls, and donor/gestational carrier models, which should push our accuracy beyond state-of-the-art models in this space and better isolate clinic impact on patient outcomes. We've launched ivf.clinic, a website where patients can access personalized IVF reports and browse our clinic rankings (though we're still squashing some bugs). Currently, we're expanding our research to include comprehensive insurance coverage and pricing data across clinics nationwide. If anyone has insights on automating the collection of IVF clinic pricing information, I'd love to hear from you at scelarek@gmail.com. 61: Replicate Study On Brain Wave Synchronization For Speeding Learning We have acquired and configured the OpenBCI UltraCortex Mark IV 8-channel EEG headset and a clinical-grade Biosemi 32-channel EEG system. We’ve implemented the required components for the experimental pipeline (computing alpha from EEG, flashing bright white light, presenting stimulus images). We are currently putting them together into a single system that we’ll use to collect the data from several participants. We are aiming to gather data on several participants in late June / early July and complete the pilot of the replication in July 2025. If you’d like to be a participant in the study, [they might announce a link once they have it]. 62: Advocate Repeal Of Interstate Runaway Compact No update received and I can’t find anything about this. 63: Animal Welfare (Especially Fish) In Turkiye Future For Fish asks companies to sign up to FFF's fish welfare commitment, which requires producers to certify their facilities and enforce specific standards for stocking density and harvest. Luckyfish, İlknak, Divan (35 restaurants, 17 hotels) and NG Hotels (5 hotels) have signed and published FFF's fish welfare commitment with İlknak publishing the commitment on their website. Kılıç published its first sustainability report detailing fish welfare policies, including enforcing a maximum stocking density of 10 kg/m³ and confirmation of electrical stunning practices. Longer version with some caveats: https://manifund.org/projects/improving-fish-w From the longer document, these commitments involve things like reducing overcrowding, or stunning fish before killing them. Over 30 million fish were affected just from their single largest commitment, and they say 100 fish are helped per dollar spent. 64: More Georgism Advocacy Lars and Will used the 2021 grant to co-found ValueBase. Will remained with the company, and Lars left to do advocacy work at the Center For Land Economics. Here’s their summary of how things are going: [Our] organization transitioned leadership with Greg Miller, a former Program Analyst at the US Department of Housing and Urban Development, and Lars Doucet, author of Land is A Big Deal and Co-Founder of Valuebase, working full time and Joe Caissie stepping aside. This transition happened naturally as the next career transition for each respective person. Since then, progress has been made on pushing forward legislation. Maryland had two bills introduced to give Baltimore and counties the ability to enact split-rate taxes. One of the bills passed the state senate and would allow Baltimore to enact land value taxes within one mile of rail corridors–this contains 50% of Baltimore’s land value. However, the legislative session ended. We expect the bill to revive next session. The Center for Land Economics has been actively working to help efforts to get this bill passed the line. At the same time, we have uncovered systematic undervaluing of vacant land in assessments. We are writing a report on the assessment issues in Maryland with actionable steps to resolve them.
Inline links: Codebuff, website, Substack, survives, a public Substack, in Rwanda, Growth Summit, Stanford Social Innovation Review, ProMarket, Global Prosperity Institute, Saharon, this page, eadbatteries.substack.com, here, here, a post profiling the Far Out Initiative here, https://tornyol.com, a public website, https://chemrxiv.org/engage/chemrxiv/article-details/67e4cde76dde43c9084d88b7, https://blueprintbiosecurity.org/introducing-project-air/, our way, connect them directly to me, www.Spartacus.app, X, Substack, here, https://www.goodancestors.org.au/ai-safety, https://www.australiansforaisafety.com.au/, https://www.aul.city/blog/reflecting-on-africa-urban-lab-s-inaugural-year-2024-highlights, https://healthlearn.org/blog/updated-impact-model, https://www.youtube.com/playlist?list=PLWVKUEZ25V97tNULapu07DhWv6_W4NfpE, here, public website, here, in urgent need, https://manifund.org/projects/improving-fish-w
Minnesota and Virginia also have legislation to enable cities to implement land value taxes. We are monitoring these efforts. There are a few other cities we are operating in. We have helped another organization prepare for a meeting in Tennessee by doing impact analysis of land value taxes in the city. We have presented to city officials in the City of South Bend who have expressed support for land value taxes. Finally, we are in conversation with a State Senator in Colorado who is a champion of land value taxes. Meanwhile, we have soft launched and developed the OpenAVMKit, which uses a unified schema to do assessment accuracy reports and automated valuation methods for any property tax data given. Valuation of land is the key binding constraint to successful implementation of land value taxes. We plan to be the leaders in this space with strong benchmarking capabilities and a repo that can enable the open-source community to make the best automated valuation methods. Along with these efforts, we have expanded the movement. We have posted to the Progress and Poverty Substack growing the subscriber base to around 5,000 subscribers. We have spoken to over 25 local advocates interested in working on land value taxes in their local communities. Yet, there is a long way to go. We need to start earning income through technical assistance contracts as our grant funding expires. We need to continue pushing for a state to implement, and we need to be prepared to tell the success story for when they do. 65: EN’s Work On Bacteriophage Therapy Our project is aimed at pioneering phage therapy in Nigeria, where limited resources/infrastructure have historically held back research in this field. Starting from the ground up, we are establishing the foundational systems needed to support a robust phage research ecosystem. So far, we’ve isolated 34 bacteriophages targeting Pseudomonas aeruginosa, an essential step toward building a comprehensive phage bank. This began with collecting a wide range of clinical Pseudomonas isolates, which we are now characterizing alongside the phages through genome sequencing and phenotypic assays including studies on phage stability across pH, temperature, and salinity ranges. Our long-term goal is to develop a phage-based hydrogel for treating diabetic wounds. On the regulatory front, we have secured approval from the Attorney General to register our nonprofit organization, the Centre for Phage Biology and Therapeutics. Additionally, we’re expanding into vaccine development; following a research stay in Prof. Roderick's lab at the University of Waterloo, we have initiated the design of a phage-based universal Salmonella vaccine aimed at covering all major serotypes—an urgent need underscored by Africa’s reliance on external vaccine sources during the COVID-19 pandemic. I have signed an MTA agreement with Roderick to use his phage-based vaccine platform patents to enable us to design vaccines against any common disease affecting us. This is only the beginning, but we are proud to be laying the scientific and institutional groundwork for homegrown phage innovation in Africa. Emergent Ventures funded EN before we did and deserves a lot of credit here also. 66: Create An Artificial Kidney For an implantable artificial kidney, the first essential component is a hemofilter designed to emulate the glomerulus. Critical requirements for this hemofilter include high permeability (to maximize flow for a given area), selectivity (specifically, the retention of albumin), and robust blood compatibility (ensuring sustained function over time). Our initial strategy focused on using negative surface charge to reduce fouling. I began by testing polyelectrolyte (PE) coatings on 24nm pore membranes featuring a negative terminal charge, similar to the glomerular barrier. These initial static tests, assessing platelet adsorption in whole blood, yielded positive outcomes for some polyelectrolytes, indicating potentially desirable blood compatibility. However, static test setups are not truly representative of dynamic in-vitro conditions and don't provide data on key parameters like permeability, fouling progression, or changes in membrane selectivity. To address these limitations, I designed and built a blood filtration setup. This system sustains human whole blood in circulation for 20 minutes, allowing us to analyze all the aforementioned parameters, as well as platelet activation markers. This has resulted in a fairly high-throughput system for evaluating any surface coating. I'm pleased to report this setup has been accepted for presentation at this year's European Society for Artificial Organs (ESAIO) conference. I am also currently working on a full manuscript, as I believe this system offers a viable way to partially replace animal experiments in our early-stage research, requiring only 1.2ml of human blood per run. Working with a PhD student (hired to support both this research and work on membrane substrates), we have continued testing these PE coatings, alongside PEG coatings, on our membranes. Here, we're finding that optimization of the coating layer is crucial. With the current PE coatings, we observe a permeability drop of about an order of magnitude compared to the base membrane, making them unsuitable for an implantable device in their present form. This is likely due to the specific nature of the initial PE layer, which we can modify. We also suspect there may be ingress of PE into the pores, meaning we're not achieving just a surface coating (our goal), but rather a very thick coating, which would explain the flux loss. Optimizing the coating process to control penetration depth is now a primary focus of my ongoing work. I am currently aiming for a flux of 20ul/min (as this is cap introduced by the protein gel layer anyway) but for it to be at this 'steady state' permeability without drop in permeability. I am also imaging the membranes after contact with SEM to see if there is indeed any platelet adsorption etc. Tugrul has the dubious honor of maybe being "the only person to climb a 4000m peak with severe kidney failure". To raise money and awareness for his artificial kidney project, he is running Climb Against Time, where he will climb 41 mountains over 4000m (13000 ft) this summer. He is looking for donors and climbing partners. 67: Add Tardigrade Genes To Human Cells The goal of this one was to make hybrid cells that are more resilient for research and certain medical applications. They report: The grant was to synthesize vectors for the expression of humanized tardigrade proteins that can be targeted to different areas of the cell. All the vectors were designed, generated, and transposed into human cells. The proteins all localize successfully (e.g. they match the designed target), with one exception (we are still working on validating it). We've done some stress testing with the trangenic cells, but haven't reached firm conclusions yet. We've further generated some multigene designs but have not yet transposed them into cells, but should shortly. We're hoping to submit a manuscript on the first round later this year. 68: Teach Forecasting To EU Policy-Makers The original project didn't work out, but our grantee (who still prefers to remain anonymous) is now working with an EU think tank pursuing the same agenda, and has been teaching forecasting workshops to policy-makers for the past two months. 69: Platform For Single-Cell Imaging They ended up unable to accept this grant and returned the money. 70: Open Source Polygenic Predictor For EA/IQ They have an update here. They think they have a predictor that can explain 12% of variance in intelligence, and they’re working on validating it and creating an easy-to-use website. 71: Improve Flu Vaccines The grant mainly funded agent based modelling to demonstrate the benefit of pre-existing immunity to pandemic influenza if and when a future pandemic occurs (academic publication will result). The original proposal was to attempt to influence the WHO influenza strain selection process. After attending WHO meetings and a global influenza conference, I believe this is not feasible. Stakeholder feedback was the potential short term negative effect on vaccine hesitancy is believed to outweigh the less tangible future benefit. Given the conservative nature of decision makers, pandemic vaccines are likely to remain research only. There are still green shoots of research into pandemic preparedness/prevention that I am continuing to work on. I'm working under the "Australians for Pandemic Prevention" brand of Good Ancestors, another group that ACX funded in 2024. 72: Scenario Analysis For Developing World Agricultural Programs In addition to the research and analysis funded by the grant, I’ve learned to code with LLMs and have built an MVP of the project. The app is being considered for further development by staff at a large international organization. 73: Further C’s Political Career C’s political career is going well, but he continues to think it wouldn’t be strategic to give more information publicly at this time. Lessons Learned I'm most impressed with our lobbying/advocacy organizations. In particular, Good Ancestors has gotten the Australian government to sign onto an international AI safety declaration, partner with various x-risk-related organizations, and (possibly) extend charity tax deductions to some EA causes that previously didn't have it - I think this on its own goes a substantial way to paying back the cost of all ACX Grants. Coalition to Modify NOTA has a kidney donation bill in front of Congress that the (very illiquid) prediction markets give a 45% chance of passing; if it works, it could save thousands of lives. The Georgists are partly responsible for bills making land value taxes slightly easier to implement in a handful of states. Good Science Project seems to have significantly improved science. Are lobbying organizations a better bet than other types of nonprofit (within the constraints of ACX Grants)? I'm not sure. It could just be that lobbyists are (naturally) better at playing themselves up and sounding successful than (for example) scientists, or that politicians are good at people-pleasing and make people feel heard and encouraged in a way that might not change overall policy later. Also, I recently talked to some grantmakers who funded a lobbying organization that superficially seems excellent, but they expressed concern it was net negative (!) by taking away oxygen and spotlight from potentially more effective orgs. So I am encouraged but wary. Animal welfare organizations were another standout success. Again, I don't know how to think about this - while I think our grantees were exceptional, there's also an issue where the scale of animal welfare challenges is so great, and work on them so neglected, that lots of organizations can save a million chickens here, or a million fish there, without particularly making a splash. On the one hand, this is exactly what effective altruism should be doing - exploring grants that are very high in linear utility even if they don't feel satisfying. On the other, they're unsatisfying - and also hard to assess retroactively. How many chickens should a good animal welfare grant save? Any realistic number will both be overwhelmingly large in absolute terms and far too small in relative terms. I'm most ambivalent about our science grants. Many of them say they are successful and can point to published papers which explain the science they did. But it's hard to judge whether anything useful has changed based on the science getting done. I know it's important to fund basic research and not just last-mile technology startups, but it's hard for a mini-grants program like this one to evaluate these kinds of abstract interventions. One disappointing result was that grants to legibly-credentialled people operating in high-status ways usually did better than betting on small scrappy startups (whether companies or nonprofits). For example, Innovate Animal Ag was in many ways overdetermined as a grantee - former Yale grad and Google engineer founder, profiled in NYT, already funded by Open Philanthropy - and they in fact did amazing work. On the other hand, there were a lot of promising ACX community members with interesting ideas who were going to turn them into startups any day now, but who ended up kind of floundering (although this also describes Manifold, one of our standout successes). One thing I still don't understand is that Innovate Animal Ag seemed to genuinely need more funding despite being legibly great and high status - does this screen off a theoretical objection that they don't provide ACX Grants with as much counterfactual impact? Am I really just mad that it would be boring to give too many grants to obviously-good things that even moron could spot as promising? Someone (I think it might be Paul Graham) once said that they were always surprised how quickly destined-to-be-successful startup founders responded to emails - sometimes within a single-digit number of minutes regardless of time of day. I used to think of this as mysterious - some sort of psychological trait? Working with these grants has made me think of it as just a straightforward fact of life: some people operate an order of magnitude faster than others. The Manifold team created something like five different novel institutions in the amount of time it's taken some other grantees to figure out a business plan; I particularly remember one time when I needed something, sent out a request to talk about it with two or three different teams, and the Manifold team had fully created the thing and were pestering me to launch a trial version before some of the other people had even gotten back to me. I take no pleasure in reporting this - I sometimes take a week or two to answer emails, and all of the predictions about my personality that this implies would be correct - but it's increasingly something that I look for and respect. A lot of the most successful grants succeeded quickly, or at least were quick to get on a promising track. Since everything takes ten times longer than people expect, only someone who moves ten times faster than people expect can get things done in a reasonable amount of time. In almost every case where I thought to myself “this is a cool idea, but I don’t know how it’s going to really pay off, as opposed to reaching a cool intermediate accomplishment and then stagnating”, this was a correct criticism, and I should have taken it more seriously. But I can’t rule out that these were good in vague and hard-to-measure ways that I should take more seriously. This one is really self-serving, but in general when people were good communicators (or even bloggers) and wowed me with the writing-composition of their application, they turned out to be a good bet. And when people were hard to understand and annoying to communicate with, even if their ideas seemed good, they were less likely to pan out. Overall Thoughts The total cost of ACX Grants, both rounds, was about $3 million. Do these outcomes represent a successful use of that amount of money? Very naively, startups originating from ACX Grants have about $50 million in value1. If ACX Grants is equivalent to a pre-seed funder, and pre-seed funders usually get ~5%, then if we were VCs we would have a portfolio worth $2.5 million. About 1/5 of ACX Grants were attempting to be market-valued startups, so if we assume the charitable portion did about as well as the startup portion, then the charity portion is “worth” $10 million. There’s some reason to expect this is too high, since much of the startup value came from one successful outlier. But there’s another reason to expect this is too low, since we were aiming at charity rather than market cap, and any actual market cap that our grantees got was an unexpected side effect. I’m treating this as a sanity check rather than as a real number. It’s harder to produce Inside View estimates, because so many of the projects either produce vague deliverables (eg a white paper that might guide future action) or intermediate results only (eg getting a government to pass AI safety regulations is good, but can’t be considered an end result unless those regulations prevent the AI apocalypse). Because we tend towards incubating charities and funding research (rather than last-mile causes like buying bednets), achieved measurable deliverables are thin on the ground. But here are things that ACX grantees have already accomplished: Improved the living/slaughter conditions of 30 million fish.
Inline links: Progress and Poverty Substack, Climb Against Time, here, 1
Book Review: Arguments About Aborigines
July 15, 2025 · Original source
The Dugum Dani of western Papua New Guinea were so notoriously warlike that when an Australian police post was introduced to the area, anthropologist Karl Heider predicted that it would do little to stem the Dani’s endemic violence. In fact, though, they quickly abandoned their warfare as soon as the presence of the colonial authorities gave them a plausible coordinating mechanism, and many later expressed relief that they were free of the cycle of violence and retribution. Similarly, highlanders who had practiced brutal initiation ceremonies “in which they were forced to drink only partly slaked lime that blistered their mouths and throats, were beaten with stinging nettles, were denied water, had barbed grass pushed up their urethras to cause bleeding, were compelled to swallow bent lengths of cane until vomiting was induced, and were required to fellate older men, who also had anal intercourse with them” gave them up after only minimal contact with outside disapproval. Some later told anthropologists they felt “deeply shamed” by their treatment of their own sons and were relieved to stop.
The Australian Aborigines are a tempting battleground for this conflict. Even as we’re not supposed to dub them noble savages, so we definitely aren’t supposed to call them “the oldest society in the world” with a “fifty thousand year history” - just because they arrived fifty thousand years ago doesn’t mean their culture has been stagnant during that time. Still, certain decamillennia-old rock art appears to depict some of the same beings mentioned in Aboriginal mythology during colonial times and into the present. And on a very literal interpretation of cultural evolution, the longer you’ve been in a specific niche, the more adapted to it you get. We are citizens of an industrial society that gets five or ten years to adopt to each new paradigm before the technologists throw out something new to knock us off balance again, heirs to a Judeo-Christian tradition barely three thousand years old and a Greco-Roman-Indo-European tradition hardly any older. What does something really ancient look like? The Aborigines, whose culture can seem impossibly complex at times (is this an illusion? we’ll discuss that later!) give a feeling of something over-optimized, a genetic algorithm run for 999,999,999 epochs until it ends up at weird edge cases that break the reward module and get assigned infinite utility.
Australian Aborigines are a polygynic gerontocracy with infant betrothal.
Meetups Everywhere 2025: Times and Places
August 29, 2025 · Original source
Asia-Pacific (including Australia)
Contact: Jan Contact Info: hkacxmeetup[a t]gmail[period]com Time: Sunday, October 26th, 6:00 PM Location: The Catalyst art gallery, 218 Hollywood Road Coordinates: https://plus.codes/7PJP74PX+63 Australia BRISBANE Contact: Laura Contact Info: laura[period]leighton94[a t]gmail[period]com Time: Wednesday, October 1st, 6:00 PM Location: Fourth Monkey Bar and Grill, 58 Mollison St, South Brisbane/West End. We will be in the back courtyard or on the back deck where it tends to be quieter. We will have ACX meetup signs up. Coordinates: https://plus.codes/5R4MG2F6+6W Notes: This event is co-hosted with Effective Altruism Brisbane.
Inline links: https://plus.codes/7PJP74PX+63, https://plus.codes/5R4MG2F6+6W
Links For September 2025
September 04, 2025 · Original source
43: China think tank assessment of how in control Xi is: still very in control, maybe not infinitely in control. 44: Related - did you know (h/t xlr8harder) that if you ask AI to write a science fiction story, it will very often name the protagonist “Elara Voss” (or some very close variant like Elena Voss), and this remains true across various models and versions? Related: Chelsea Voss of OpenAI is having a baby and has the opportunity to do the funniest thing. 45: “Hector (cloud) is a cumulonimbus thundercloud cluster that forms regularly nearly every afternoon on the Tiwi Islands in the Northern Territory of Australia…[he is sometimes called] Hector the Convector”. 46: British allergy sufferers who want to know the ingredients of things demand that British cosmetics stop listing their ingredients in Latin. “For example, sweet almond oil is Prunus Amygdalus Dulcis, peanut oil is Arachis Hypogaea, and wheat germ extract is Triticum Vulgare.” 47: Text-based RPG about being an NYT journalist at the Manifest prediction market conference. I make a brief appearance. 48: Study uses supposedly-random variation in doctor assignments to test whether the marginal mental health commitment is good or bad for patients, finds that it is quite bad. Freddie de Boer is violently skeptical (maybe literally so?) and makes some good points about how a single quasi-experimental study is never absolute proof. But I don’t think he quite justifies his opinion that the paper was irresponsible and should never have been published; it’s just a normal quasi-experimental study that we should nod and say “huh” at but not overweight as the culmination of all possible research that overcomes all possible priors. My prior is that the marginal commitment is pretty useless (many commitments are just “well, since this person arrived at our ED for some reason, it would look bad from a medico-legal perspective to just let them go, so let’s keep them a few days to evaluate” - and yeah, you should be upset about this) but I’m still surprised by how many outright negative (as opposed to zero) effects the researchers found. The strongest argument for negative effects is that it will make some people miss work and maybe lose their job. But this study found that commitment ~doubles the risk of near-term suicide (admittedly only from 1% to 2%), which would have been outside my confidence intervals for how bad it could be. I suspect confounding, but only on general principle, and I wouldn’t be too surprised either way. 49: This tweet is probably bait, but I found it a thought-provoking question: I think there’s a boring answer, where the law is more complex than just a single number and whatever kind of weird trafficking Epstein was doing is worse than whatever normal relationships these European laws are permitting. But assuming that there’s a substantive difference even after taking that into account, I think my answer is something like - we’ve got to divide kids from adults at some age, there’s a range of reasonable possible ages, we shouldn’t be too mad at other societies that choose different dividing lines within that range - but having decided upon the age, we’ve got to stick with it and take it seriously (in the sense of penalizing/shaming people who break it). This is more culturally relativist than I expected to find myself being, so good job to Richard for highlighting the apparent paradox. 50: Dilan Esper describes his experience as one of Hulk Hogan’s attorneys in the Gawker lawsuit (X). Parts I found interesting: none of the lawyers knew Thiel was funding the lawsuit; Gawker probably could have won if they had been slightly competent but kept "shooting themselves in the foot"; and Gawker probably could have won if they had just pixelated the private parts in the video. 51: Amazing concept and poems (link on X): I tried to see if AI could do this, and it did something that technically met the requirements but had zero artistic merit - using a lot of words like “nowhere” and “outside” in one, then separating them out to “no where” and “out side” in the other. I didn’t invest much energy in creating a clever prompt telling it not to do that, so feel free to report if you get better success. 52: New study claims consultants are actually good, at least for profits: "We find positive effects on labor productivity of 3.6% over five years, driven by modest employment reductions alongside stable or growing revenue" 53: A Polish team tries to test Peter Turchin’s equations for predicting political unrest on recent Polish history, has to make some changes but claims mostly positive results. 54: New big multi-author Substack, The Argument, trying to be a sort of center-left version of the model pioneered by The Free Press and other high-production-value ideological Substack properties. Excited to see Kelsey Piper is involved, and she starts off strong with a post on the latest round of First World basic income studies, which find few positive effects. This is surprising, because recipients didn’t waste the money on alcohol or gambling or anything - they paid down debt and got useful goods. Still, it didn’t even affect things that should have been obvious, like stress level. It’s not even clear that amounts of money large enough to help with rent made homeless people more likely to get houses! Matt Bruenig criticizes the article, accusing Kelsey’s studies of being downstream of Perry Preschool style dreams that exactly the right welfare program will have massively compounding effects that cut poverty out at the root and turn everyone into elite human capital; he thinks giving people money won’t do this, but it will increase equality and give the poor better lives. I assume he’s not a strong hereditarian, but his argument makes even more sense from that perspective, and I’ve certainly criticized dumb outcome measures like infant brain waves which we have only tenuous reasons to think are related to anything we care about. But Kelsey reasonably responds that the outcome measures she’s talking about include stress level and life satisfaction. To defuse this critique, Bruenig either has to argue that our construct “life satisfaction” doesn’t really measure whether someone’s life is satisfactory, or else claim that giving poor people satisfactory lives isn’t really what we’re going for - which I think would require more explanation on his part. There’s some further (impressively acrimonious) debate on X, but I don’t see anything that addresses my core concern. GiveDirectly, a charity involved in basic income experiments, has a presponse here; they say that some studies are positive, and that the ones that aren’t might have tried too little cash to matter, or been confounded by COVID making everything worse. They also point out that basic income is harder to study than traditional programs like giving people housing, because if you’re giving housing you can measure housing-related outcomes directly and have a pretty good chance of getting enough statistical power to find them, but since everyone spends cash on different things, the positive effects might be scattered across many different outcomes (and therefore too small to reach significance on each). Everyone involved in this debate wants to emphasize that the poor results are for First World studies only, and that studies continue to show large benefits to giving cash in the developing world. 55: Related: I was less impressed by The Argument’s first foray into housing policy, which follows an all-too-familiar pattern: Some people say they don’t like noise and disorder and try to make rules against it in their apartments.
Inline links: China think tank assessment of how in control Xi is, xlr8harder, Chelsea Voss of OpenAI is having a baby, Hector (cloud), demand that British cosmetics stop listing their ingredients in Latin, Text-based RPG about being an NYT journalist at the Manifest prediction market conference, finds that it is quite bad, violently skeptical, literally so?, This tweet, https://substackcdn.com/image/fetch/$s_!S9fU!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa558c09b-7fb6-40a8-a8a0-27b658a2c876_576x687.png, describes his experience as one of Hulk Hogan’s attorneys in the Gawker lawsuit (X), link on X, https://substackcdn.com/image/fetch/$s_!zyh7!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F75e9f0f6-d794-4ea2-b24b-5d4803bf28dc_590x478.png, New study claims consultants are actually good, tries to test Peter Turchin’s equations for predicting political unrest on recent Polish history, The Argument, a post on the latest round of First World basic income studies, criticizes the article, infant brain waves, debate on X, has a presponse here, first foray into housing policy
ACX Grants Results 2025
October 13, 2025 · Original source
Greg Sadler, $65K, for Good Ancestors Australia. Our first grants round in 2021 supported ACX commenter Nathan Ashby beginning policy work in Australia. His work eventually evolved (it’s complicated) into GAA -now one of Australia’s most influential AI safety organizations, working with the public, MPs and their staffers to incorporate the x-risk/alignment perspective into Australian AI policy and legislation. We are excited to fund their continued operation. Australia is also a key base for building influence in tiny Pacific Island nations; although these may not have cutting-edge AI industries, they collectively form a powerful bloc in one-country-one-vote forums like the UN.
Inline links: Good Ancestors Australia
Eli Elster, $13K, to research traditional psilocybin use in Africa. Psilocybin, aka magic mushrooms, is in the process of being integrated into mainstream psychiatric practice; it is already approved for treatment-resistant depression in Australia, and undergoing (currently promising) FDA trials in the United States. Much of what we know about the preparation and administration of psilocybin - including widespread ideas about “set and setting” and “integration” - comes from traditional use by the Mazaetec Indians. In 2023, anthropologists discovered that traditional healers in Lesotho, Africa also use psilocybin mushrooms - the first time such a practice has been found in the Old World - and that they seem to prepare and administer it differently from the Native Americans. Eli and his collaborator Betsy Sethathi conducted the first in-depth fieldwork on the topic earlier this year; our grant funds a return trip to Lesotho to further investigate their ethnobotanical practices and see if we can learn anything from them.
Inline links: Eli
Highlights From The Comments On Vibecession
December 31, 2025 · Original source
The OECD also produces consumer confidence surveys and the US is pretty middle of the pack compared to other advanced countries for the last three years - US, Australia, western europe, UK, japan, are all in the -1 to -1.5 z score range historically. China is the worst, around -2 z scores. Interestingly, Mexico is one of the few places with high consumer confidence right now.
"All Lawful Use": Much More Than You Wanted To Know
March 01, 2026 · Original source
Mass domestic surveillance of Americans, American companies, and US permanent residents (or for that matter generally their counterparts in other Five Eyes partners – UK, Canada, Australia, and New Zealand) is more complicated. The current law is (roughly) that it’s illegal to seek this kind of data, but legal to “incidentally obtain” it. So for example, if the US was looking for al-Qaeda communications, it might tap a major undersea cable, and if tapping that cable happened to incidentally give it data on millions of Americans, it could keep that data. But after “incidentally obtaining” the data, it may only query the resulting database in a targeted way. So the government might take its trove of citizen data that it “incidentally” collected looking for al-Qaeda, and search for a specific citizen’s history if it thinks (for example) that this citizen might be a spy.
Inline links: generally their counterparts in other Five Eyes partners, may only query the resulting database in a targeted way
Meetups Everywhere Spring 2026: Times & Places
April 01, 2026 · Original source
Asia-Pacific (including Australia)
Contact: Tegan Contact Info: teganspeaking[@]gmail[.]com Time: Sunday, April 19th, 10:00 AM Location: We’ll be in the back room at Our Local on Kloof Street and will have an ACX Meetup sign, and a pile of boardgames. Coordinates: https://plus.codes/4FRW3C85+XC Group Link: https://discord.gg/UYv [remove this bit] 3v69h Notes: Please RSVP on partiful so that we know how many people to book a table for: https://partiful.com/e/MKKZ5ElzjrABYmju5aKC?c=1HJeZdS8 Asia-Pacific Australia CANBERRA Contact: Declan Contact Info: declan_t[@]hotmail[.]com Time: Monday, May 4th, 6:00 PM Location: Outside table at Grease Monkey Braddon, will have ACX Meetup sign. Coordinates: https://plus.codes/4RPFP4GM+R3 Notes: RSVP’s appreciated so I can book a big enough table. I will book from 6pm but get to the bar earlier if you want cheap happy hour drinks/snacks.
Inline links: https://plus.codes/4FRW3C85+XC, https://partiful.com/e/MKKZ5ElzjrABYmju5aKC?c=1HJeZdS8, https://plus.codes/4RPFP4GM+R3
Contact: Bianca Peterek Contact Info: biancaczatyrko[@]gmail[.]com Time: Saturday, May 16th, 1:30 PM Location: Coffee Club William Street, Shop 11, 140 William St, Corner of Murray St Mall, Perth, WA, Australia, 6000. I am totally blind. Please look for the ACX meetup sign and announce yourself when you arrive. Thanks and looking forward to seeing you there! Coordinates: https://plus.codes/4PWQ2VX4+2X
Inline links: https://plus.codes/4PWQ2VX4+2X