Beyond that, there is a scientific consensus that they cause dementia in the long term.

It’s incorrect.

This is one of the biggest academic blind spots I’ve seen in my life. Anti-cholinergics are not a well-known drug class, but they are taken by about 25% of the general population; more common than anti-depressants.

These drugs are thought to cause dementia for multiple reasons. One of them is that some anti-cholinergics block1 the M1 receptor, which can then cause the brain to produce less α-secretase and more α/β peptide; α/β peptide accumulates into amyloid plaques, which cause cognitive decline. Essentially:

Drug → M1 receptor block→ more α/β peptide →amyloid plaques → cognitive decline (specifically Alzheimer’s)

The amyloid hypothesis of Alzheimer’s has attracted some doubt. One of the researchers who published papers that influenced the theory was found to have committed tons of fraud; some of the papers were retracted. Prior to this finding, there were people who were skeptical of the theory, noting that many of the drugs based on it failed FDA trials, while the others that were approved were not related to it.

The presence of amyloid plaque is still a pretty strong predictor of the disease. There is almost no overlap in the amount of plaque in Alzheimer’s patients and similarly aged controls who have no dementia. At least, in some parts of the brain:

It seems that the plaques themselves don’t cause cognitive decline, it’s the tau tangles. One study reports baseline amyloid plaque levels had no statistically significant association with declines in future test scores, but tau tangles did:

The statistical reasoning here is faulty. Within people who had elevated amyloid plaque levels (A/β+), the association between plaque and cognitive decline was not statistically significant, but it was in the expected direction. They claimed to correct the association between tau tangle baseline levels and cognitive decline for plaque levels in their regression model, but it doesn’t look to me like they did.

It seems that, at the early stage of Alzheimer’s disease, α/β plaques are more present than tau tangles, and both predict the future levels of tau tangles. Once the disease gets going, plaque becomes less relevant and tau starts dominating2:

I suppose this explains why drugs that were developed based on the theory were so unsuccessful. By the time somebody is diagnosed with dementia, they already have plaques and tau tangles in their brain. Preventing the creation of plaque doesn’t deal with the tau tangles that are directly driving the cognitive decline.

Overall, the plaque/tau theory of Alzheimer’s holds up reasonably well to scrutiny.

There are also a few other mechanisms by which anti-cholinergic drugs could affect dementia. People adapt to the effects of anti-cholinergic drugs by upregulating the receptors that are blocked and/or increasing their sensitivity; if we believe the plaque/tau hypothesis, then the tolerance effect should theoretically counterbalance some of the effect of M1 blockage.

Use of anti-cholinergics could also lead to selective synaptic pruning, where cholinergic neurons are selectively disconnected by the brain due to lower activity. Not implausible, but hard to test.

The hypothesis that these drugs cause dementia is sensible in the way the diet-heart theory is. It’s rational, but some of the theoretical mechanisms by which they do involve lots of mechanistic causal chains, which imply an uncertain and attenuated effect size.

What I mean by this, is that if A has a 0.5 SD effect on B, and B has a 0.5 SD effect on C, the effect of A on C is 0.25; not 0.5. In this hypothesis, there are a total of five steps between anti-cholinergic drug intake and cognitive decline: drugs → M1 blockage → α/β peptide upregulation →formation of plaques →tau protein accumulation →cognitive decline. If any one of these effects don’t exist, or if the drugs have effects in the opposite direction that counteract the main chain, the theory flies out the window.

Oh, and those studies people cite to argue that M1 receptor blockage is associated with α/β peptide and amyloid plaque? Most of it is literally in mice. I found one human study that finds M1 agonists reduce AB peptide levels by 20%; it’s a p < .004 with 19 people. I guess that’s not terrible.

When it comes to observational data on this topic, this study stands out as being particularly large and high quality:

• Coupland CAC, Hill T, Dening T, Morriss R, Moore M, Hippisley-Cox J. Anticholinergic Drug Exposure and the Risk of Dementia: A Nested Case-Control Study. doi: 10.1001/jamainternmed.2019.0677

Of the entire study population (284 343 case patients and matched controls), 179 365 (63.1%) were women, and the mean (SD) age of the entire population was 82.2 (6.8) years. The adjusted OR for dementia increased from 1.06 (95% CI, 1.03-1.09) in the lowest overall anticholinergic exposure category (total exposure of 1-90 TSDDs) to 1.49 (95% CI, 1.44-1.54) in the highest category (>1095 TSDDs), compared with no anticholinergic drug prescriptions in the 1 to 11 years before the index date. There were significant increases in dementia risk for the anticholinergic antidepressants (adjusted OR [AOR], 1.29; 95% CI, 1.24-1.34), antiparkinson drugs (AOR, 1.52; 95% CI, 1.16-2.00), antipsychotics (AOR, 1.70; 95% CI, 1.53-1.90), bladder antimuscarinic drugs (AOR, 1.65; 95% CI, 1.56-1.75), and antiepileptic drugs (AOR, 1.39; 95% CI, 1.22-1.57) all for more than 1095 TSDDs. Results were similar when exposures were restricted to exposure windows of 3 to 13 years (AOR, 1.46; 95% CI, 1.41-1.52) and 5 to 20 years (AOR, 1.44; 95% CI, 1.32-1.57) before the index date for more than 1095 TSDDs. Associations were stronger in cases diagnosed before the age of 80 years. The population-attributable fraction associated with total anticholinergic drug exposure during the 1 to 11 years before diagnosis was 10.3%.

Their results:

Pretty unambiguous as far as statistics go, the lower 95% CIs are far away from 1.

The elephant in the room: people experiencing cognitive decline need medications to control the symptoms: insomnia, psychosis, urinary incontinence, and such. It just so happens that a lot of the drugs prescribed to treat these symptoms are also anti-cholinergics, so it’s not surprising the users are more likely to experience dementia later.

The other issue is that not all of the anti-cholinergics were even associated with future cognitive decline. The anti-histamine variants were hardly correlated with future cognitive decline, as were the antiemetics, muscle relaxants, gastro antispasmodics, antiarrythmics, and bronchodilators — when you adjusted for the effects of taking other drugs.

I’ve seen it argued that this is because the “strong anti-cholinergics” are the ones that cause dementia, but the weaker ones do not. This is not borne out in the data. For example, benadryl is strongly anti-cholinergic — but the other anti-histamine drugs they lumped it with are not associated with cognitive decline.

Perhaps they accidentally lumped benadryl with anti-histamines that are less potent or do not get into the brain’s blood, so the real effect got lost in the mess. Here’s the problem: every single anti-histamine that they used in their study — Azatadine, Brompheniramine, Chlorpheniramine, Clemastine, Cyproheptadine, Hydroxyzine, Trimeprazine — was a first generation variant that does cross into the brain. The fact these drugs are not assoicated with dementia is quite telling.

Researchers try to overcome this with case-controls, examining long term use before diagnoses, or controlling for demographic/SES variables. “Controlling for variables” is overly maligned in statistics, but this is a uniquely egregious case — they aren’t controlling for the thing that matters: existing cognitive decline and genetic/environmental predisposition.

Cognitive decline is also a long term process. It can start as early as people’s 30s:

I checked the drugs they evaluated in that observational study: some of them don’t even cross the blood-brain barrier. Glycopyrrolate (bronchodilator), Ipratropium (bronchodilator), Propantheline (gastro antispasmodic), Trospium (bladder control drug), Flavoxate (bladder control drug), and Butylscopolamine (gastro antispasmodic) are all below-the-neck drugs.

Out of the gate, the anti-cholinergic drug hypothesis looks sketchy.

I found a similar Swedish study that was published recently, focused on vascular dementia. I looked at their table3, and it turns out they found the same thing: anti-depressants, anti-spasmodics, anti-epileptics, anti-psychotics, and anti-parkinsons drugs with anti-cholinergic properties were all associated with cognitive decline; everything else was a null.

A nonlinear dose-response relationship with all-cause dementia was found for the cumulative use of strong anticholinergics (e.g., 1–89 DDDs: AOR, 1.10 [95% CI, 1.08–1.12]; ≥1095 DDDs: AOR, 1.66 [95% CI, 1.55–1.79]), notably within the class of urinary antispasmodics, antihistamines, and psychotropic drugs. In contrast, a subtle association lacking a dose-response pattern was observed for weak anticholinergics (e.g., 1–89 DDDs: AOR, 1.11 [95% CI, 1.08–1.13]; ≥1095 DDDs: AOR, 1.01 [95% CI, 0.98–1.03]). The associations with strong anticholinergics were more pronounced in men, younger people, those diagnosed with vascular dementia or Lewy body dementia (versus Alzheimer’s disease), and those with milder-stage dementia.

Consistent with the drug-specific effects found earlier. Out of curiosity, I checked the UKBB study on the same topic: it found associations between dementia and anti-depressants, anti-epileptics, diuretics; no for everything else. Antipsychotics had a positive, but statistically insignificant relationship.

Coincidentally, it seems like all of the drugs you would expect somebody experiencing early-onset cognitive decline — anhedonia (treated with anti-depressants), psychosis (treated with anti-psychotics), loss of dexterity (trated with anti-parkinson’s), and urinary issues (treated with anti-spasmodics) — to be taking, all just happen to be associated with future dementia.

People have tried testing the theory in more creative ways: one of them involved examining the effect of taking hypertensive drugs that had anti-cholinergic side effects on dementia. They found nothing.

Studying the effect of duration of treatment on prognostic outcomes using real-world data is challenging because only people who survive for a long time can receive a treatment for a long time. Specifying a target trial helps overcome such challenge. We aimed to estimate the effect of different durations of treatment with antihypertensive drugs with anticholinergic properties (AC AHT) on the risk of vascular dementia and Alzheimer's disease by emulating a target trial using the UK CPRD GOLD database (2001-2017). Comparing treatment for 3-6 years vs ≤3 years yielded null results for both types of dementia. Comparing a longer duration of treatment, >6 years vs ≤3 years, yielded a 10-year risk ratio of 0.69 (95% CI, 0.54-0.90) for vascular dementia and 0.91 (95% CI, 0.77-1.10) for Alzheimer's disease. For illustration, we performed an analysis that failed to emulate a target trial by assigning exposure categories using postbaseline information, obtaining implausible beneficial estimates. Our findings indicate a modest benefit of longer duration of treatment with AC AHT on vascular dementia and highlight the value of the target trial emulation to avoid selection bias in the evaluation of the effect of different durations of treatment. This article is part of a Special Collection on Pharmacoepidemiology.

If anything, it looks like the drugs helped. Which isn’t surprising, since the cardiovascular system is also implicated in cognitive decline. It led to a stronger reduction in vascular dementia4, which is caused by repeated strokes; the effect on Alzheimer’s was protective but not statistically significant.

This study is the nail in the coffin on anti-cholinergics and dementia. They compared the effect of anti-cholinergic bladder drugs to a drug that aided in bladder control but had no cholinergic properties — miragebron. In comparison to people who used mirabegron, anti-cholinergic users were less likely to get dementia.

Compared with non-use, ever use of anticholinergic bladder drugs was associated with an increased risk of dementia, with an incidence rate ratio of 1.44 (95% confidence interval (CI) 1.40 to 1.48). The incidence rate ratio increased with increasing cumulative drug use, from 1.31 (95% CI 1.27 to 1.36) for 1-90 defined daily doses to 1.68 (1.59 to 1.76) for >365 defined daily doses. Compared with non-use, all types of anticholinergic bladder drugs were associated with increased incidence rate ratios for dementia: tolterodine 1.43 (95% CI 1.38 to 1.49), solifenacin 1.37 (1.29 to 1.46), trospium 1.52 (1.37 to 1.67), and fesoterodine 1.48 (1.26 to 1.74). The increased risk of dementia with use of anticholinergic bladder drugs was not seen when compared directly with the use of the β3 agonist mirabegron (incidence rate ratio 0.82, 95% CI 0.74 to 0.92), irrespective of the type of anticholinergic drug.

The abstract is a little confusing. Fortunately, their figures are not. They conducted the regular, observational analysis first, and found a positive correlation between anti-cholinergic drug intake and future dementia.

According to this table, using an anti-cholinergic bladder drug for only 1-90 days is associated with a 31% increase in the risk of dementia. If that’s a real effect, surely the power users who took anti-cholinergics daily for 30 years would be totally cooked? What about those teenagers who megadose them for fun?

They then ran the analysis which anti-cholinergic bladder drugs to the non-cholinergic variant, Mirabegron. Anti-cholinergic drugs had a protective effect on dementia.

I checked the supplement, and there was no statistically significant difference in all-cause mortality.

Not only are the associations between anti-cholinergic drugs and dementia dependent on the specific drug type, if we evaluate drugs that have been shown to relate to dementia in multiple studies — it turns out it’s still confounding.

Now, we pivot to in-vitro.

I found one nice study that did it — examining the effects of anti-cholinergic drugs on neurons when they were grown outside of the human body. Theoretically, if they are harmful in humans, we might see their effects take place in artificial cultures as well. It turns out that some of the anti-cholinergic drugs had measurable toxic effects:

We treated human induced pluripotent stem cell (hiPSC)–derived neurons with eight drugs representing different AC medication classes, including antidepressants, bladder antimuscarinics, antihistamines, and antispasmodics. We analyzed these neurons for cytotoxicity, amyloid beta (Aβ) peptide levels in the conditioned medium, and the level of intracellular phosphorylated tau from these cultures.

We observed that antidepressants and bladder antimuscarinics were consistently cytotoxic, whereas antihistamines and antispasmodics did not show overt cytotoxicity at the times and concentrations that we tested. Some of the cytotoxic medications altered the amounts of Aβ1‐42 peptides, but there were no significant differences in the intracellular ratio of phosphorylated tau/total tau between AC drug treatments.

All three anti-depressants showed robust cytotoxic effects (poisonous to cells). One of the anti-muscarinics (bladder control drugs) showed a slight toxic effect, but it might have been a mere statistical fluke:

When it came to the development of amyloidic plaque, which likely contributes to cognitive decline, Oxybutynin (bladder control drug) showed up as a contributor to its formation. They couldn’t detect changes in tau protein formation; in some cases, this might have been because of the toxicity of some of the medications.

Some of these results in-vitro match the observational study — both methodologies agreed that the antihistamine and antispasmodic drug variants had no measurable effect on cognitive decline. It’s hard to compare the two methods, since they aren’t always using the same drugs, at the same doses.

Besides these studies, I didn’t find much of value: one study that examined effects of anti-cholinergics in 55 year olds on dementia found a p = .04 and a p = .01; an AI prediction study that was able to reliably predict Alzheimer’s diagnoses using medical records, anti-cholinergic drugs only came up in the top 20 factors once (other drugs like benzos came up more often); and some other observational studies that said the same thing.

One could argue that, because cholinergic drugs are meant to treat Alzheimer’s, that means that anti-cholinergics must have the opposite effect. And they are — in the short term. And it’s not like cholinergics are that effective — according to the largest meta-analysis, the effect they have on cognition in people with AD is about 0.33 standard deviations5; similar to the effects of a generic stimulant on cognitive ability.

So

The answer is no, anti-cholinergic drugs do not cause dementia6.

The big problem with the theory is that it is too general. In some cases, anti-cholinergic drugs have protective side effects, like the anti-hypertensives, so it would be crude to hypothesise they are pathogenic. “Don’t eat yoghurt because it has saturated fat”. Some anti-cholinergic drugs don’t even cross the blood brain barrier, so it’s not mechanistically possible for them to cause dementia. I guess it’s great marketing for other drugs that don’t have this effect.

The mechanistic reasoning behind the theory is not irrational, but it’s flimsy in the sense that it relies on believing in the existence and importance of 4th order effects. There’s also reason to think obesity and the cardiovascular system are implicated in dementia, if this is the case, then drugs that affect these systems should naturally affect dementia as well. Also, a general example of why I dislike mechanistic arguments in medicine.

Anti-cholinergics kind of suck as a drug class. They make you tired and dull, but if you take them to sleep, then the quality of your sleep decreases. As such, pharmacists try hard to get these drugs to not cross the blood-brain barrier.

The reason I was interested in this research is that I take benadryl, an anti-cholinergic drug, as a sleep aid. I doubt I’d ever actually develop dementia due to my family history, unless I live to be over 100 years old — that’s when the real decline starts.

3

Table:

If people respond to price signals, then they respond to social signals too: approval, embarrassment, exclusion, and disapproval. Game theory then organises how people act when they are uncertain, being watched, and watching others.

A good example of its application: Werner on The Origins of Wokeness. Previous theorists argued that it originated from philosophy, the long march through the institutions, or WEIRD morality. Werner, on the other hand, argues that increased diversity in American elite spaces resulted in larger returns to signalling woke beliefs — which indicate that somebody is more able to cooperate with different types of people.

In this case, I deem its use appropriate. The status economics theory explains why woke beliefs are typically uncorrelated/positively correlated with intelligence, why wokeness escalates (race → sex → sexuality → gender), historical timing, and relative absence in East Asia.

Status economics takes behaviour that was previously considered irrational, moral, or unexplainable and attempts to explain it through a different lens. Low quality luxury goods? Status. Medical tomfoolery? Status. Art? Status. Altruism? Status. Education? Status. Politics? Status. People not consciously thinking about status when they engage in these activities? Self-deception.

It’s hard not to emphasise self-deception as foundational. When people are consciously living life, they rarely think about wanting to dominate others. Sometimes even when they are pressured. For status economics to be plausible, we must have a theory as to why people’s stated motivations do not map to their drives and actions.

I don’t think it originated1 from one person, group, or ideology. In terms of communities, status economics is currently popular in economics, tech twitter, the rationalist community, and the online right.

The appeal of status economics is evident. A lot of times, it just works. It discourages the convoluted psychiatric stories and common sense. It explains the unexplainable.

Where does it err?

Status Economics degenerating into metaphysics

Platonism for the elites is an insult.

In the myth of Plato’s cave, we have men casting shadows on the wall with clay figures — what they perceive with their raw senses. People can choose to leave the cave and find themselves in the rest of the world — the world of Platonic forms, ideas.

The application is clear. In status economics, morality and irrationality are shadow explanations for human behaviour; status is the true substrate behind everything. The appeal of Gnosticism — Marxism, Christianity, The Red Pill — is intuitive. The masses are a bunch of cattle and your third eye sees the truth behind everything. It makes you feel better than other people.

To divide the world into a “true” and an “apparent” world, whether after the manner of Christianity or of Kant (after all a Christian in disguise), is only a sign of decadence,—a symptom of degenerating life. The fact that the artist esteems the appearance of a thing higher than reality, is no objection to this statement. For “appearance” signifies once more reality here, but in a selected, strengthened and corrected form. The tragic artist is no pessimist,—he says Yea to everything questionable and terrible, he is Dionysian2.

The idea of a true and apparent world — in other words, the world has layers — is true on a figurative level. There are lenses, frames by which sequences of events can be radically reinterpreted. The issue lies in reificiation — thinking that the layers are not mere appearances as well.

The core tenets of the school — that pretending not to care about status is itself a status play, and that self-deception is psychologically real — makes the theory unfalsifiable. If people are overtly competing over status, that proves our theory. If people are not overtly competing over status, that proves our theory — they are just self-deceived.

It is falsifiable at the group level — see social signalling vs human capital models of education — but unfalsifiable at the individual level. The theory can explain what attracts people to education in general, but can’t explain the motivations of particular people.

Unfalsifiable theories are not necessarily wrong. They are dangerous. If you take them too seriously, it is impossible to test them. “All true beliefs are falsifiable” is not even a falsifiable statement3.

Another note on ‘not caring about status’ — I don’t take this literally. Figuratively, what is meant is that this person is playing with their cards face up. What they appear to be is what they are, uninterested in playing elaborate games, comfortable to carry themselves through their own values.

Take Frank Herbert promoting Dune as an example. Is he making a play for status? Definitionally, yes — he is vying for attention and desirability. But there is something reductive about claiming that selling Dune is about status — it’s like saying he’s doing it to enable capitalism, promote satanism, or feel good. It’s an empty explanation.

Motive vs method

This is where we get to separating motives, mediums, and products; status economics often confuses these things. Dune was probably written tons of reasons — boredom, money, fame, self-expression, mastery, and connecting to people. Writing and publishing a novel involves dealing with a social world with its own signals and incentives; if people like it, then the author is rewarded with attention/money/etc.

Presumably, if somebody is willing to put in the effort to publish and sell a novel, they have financial or social motives; likely both. That’s not a bad thing, but that doesn’t necessarily mean that was the only reason for having bothered to write one in the first place. It also doesn’t mean that they were just chasing “status” in the broader sense — they might enjoy attention, but not prestige; desirability, but not credibility.

What I am getting at is essentially this question: do we want power for its own sake, or is power the way humans accomplish goals?

The question is definitionally absurd; if we take cause and effect for granted, evertything is power. The distinction still functions in a utilitarian way. Are social resources valuable for their own sake, or are they only useful instrumentally?

When a business attempts a viral marketing strategy, they are essentially pursuing a type of status — attention. But are they pursuing status for its own sake? Perhaps, to an extent, but surely money is also a motive. But then you could ask why they want money…

What even is status?

Another massive issue; status is not one thing. I define ‘status’ as power through perception, what power people have as a function of other people’s beliefs.

I consider status to come in nine different forms:

• Prestige: who is considered admirable?

• Legitimacy: who reigns supreme?

• Credibility: who is considered believable?

• Immunity: who is allowed to get away with violating rules?

• Leverage: who can impose costs and benefits?

• Attention/clout: who commands attention — cognitive fixation?

• Fame: who is remembered?

• Desirability: who is wanted?

• Access: who is allowed opportunities and resources?

Multidimensionality can be pedantic, but this frame solves a lot of the weird questions; for example, are young people low or high status? The answer: young people are more desirable, rule-immune, and are given more opportunities; but they have less leverage, credibility, and legitimacy. This also works for other debates about whether men vs women have more power. Often groups differ in which social currencies they possess.

Unfortunately, this does not go without saying: what is “high status” is individually dependent. People pay attention to, appreciate, and approve different things. These individual-level tendencies can result in aggregate effects at the group level, but many times, it’s not necessary to get most people’s approval/attention to get what you want. Only about 0.25% of the global population has bought Dune, the best selling scifi novel of all time4.

The opacity of human motivation

Is “status” a good explanation for human behaviour?

My vote is no. I think it’s a good explanation for macro-level weirdness: luxury goods, religion, altruism, ideology, and even some medical practices. Otherwise, I think it’s at best an arbitrary explanation and at worst a terrible one.

An explanation is not just a model. It’s a theory for why something is occurring and not something else. The apple fell because of the Earth’s gravitational pull, not because I sneezed before I saw it.

So, why do I like playing competitive video games? I will admit — winning feels good; losing feels bad. But why am I playing competitive video games… and not something else? Why am I not rapping, playing sports, or tasting wine? Why do I keep my childhood belongings? This is where status loses: it can explain why people play games, but it cannot explain why people pick them. The idea that art is about status is grotesque; it is to say that sex is about burning calories.

If something explains everything, then it explains nothing.

Regarding introspection: it’s easy to see emotions, thoughts, and perceptions. The mind is not flat, but it can only see the output of its depths, not the depths themselves. The machinery that creates thoughts/emotions cannot be accessed by the conscience and must be inferred; inference is unreliable. In a weird way, all desires are definitionally unconscious. This is why, I think, self-deception works. It’s not trickery if everything is hidden.

We often do not want things because of what they are, we want them because of what they represent5. Allow me to explain: we have base needs such as thirst, hunger, tiredness, sexual appetite, and bodily integrity. People then make demands of others to satisfy those needs: flirt, buy food, and request quiet. Our linguistic and cognitive maps of the world enable us to satisfy these base drives — we fear men with guns because they threaten our bodily integrity; we like sushi because it will satisfy our hunger. Sometimes we want to want things.

At times, these cognitive maps and demands of the world falter. and we want things that things that do not satisfy our bodily drives as much as we think they do, or at all. This is where unwholesome desire, taṇhā, comes from. The Buddhists were right to reject it.

When people seem to do and want irrational things that are connected to social mechanisms — farm attention from Neo-Nazis on twitter, buy luxury goods, or participate in religion — the easy explanation is just status. Sometimes that is the answer. Other times, the motivation is to satisfy desires that have stayed unsatisfied, and our unreliable linguistic maps have determined those are the answer.

Taking the psychoanalysis further, a lot of status economists are fans of Girardian desire, the idea that a lot of human motivation is mimetic. I’ve never read Girard, but I’ve seen some of that in myself; we were all teenagers once. The idea is cool, but bad as an explanation for human behaviour — what made something popular in the first place, is the more interesting question. It’s definitely overextended, for example, to explain what body types people find attractive.

There are other times where the answers are not that deep. Why do people like playing Skyrim? Lacanian desire? Mimesis? Need for community and affiliation with successful products? Maybe it’s just… fun.

Status Economics — current status?

Status economics is useful. It breaks us from the trance of gnostic explanations of behaviour, exposes that all social relationships are transactional under pressure… until it degenerates into its own gnosticism.

The school explains human behaviour well at the macro level, inconsistently at the micro level (this is true for economics in general). Humans have layered motivations and respond to some social incentives, not others. At the aggregate level, social incentives will reliably shift behaviour. “Status” is also a compresion word, which makes it useful in language but tenuous as a concept.

There’s also another question. If I dislike status economics as it is currently applied, what theory of human behaviour should replace it?

I am fine with nothing.

This will probably be the last article I write on the social status question. I’ve already written on the issue far too frequently, though I posted about it again since my opinions changed (of course!).

I was 15 years old.

Some adults came to my high school and encouraged us to do strange stuff in front of everybody else at the assembly hall. I volunteered. And I did not have a good time; so did a friend of mine, who left before everything was over. I then went to find her, and she was (also) having a panic attack. We then went to hide in one of the classrooms and listen to music with some friends and my older sister.

My homeroom teacher found us, and I ended up talking to her about what happened in the office. I ended up crying, if I recall. Just before I left, she told me something along the lines of “if there is anything you need me to do, just ask”. She said it in, what I interpreted as, a suggestive tone. This woman was in her late 30s or 40s.

I would be inclined to brush it off… if she didn’t mention in a groupchat1 that she would date one of the students there if she wasn’t his teacher. And if she didn’t hang up my drawings in her office. She also mentioned in that groupchat that she hadn’t had sex in a while.

This woman was… odd. She was too lazy to send my brother’s high school transcripts to universities, until my mom screamed at her. She seemed to disliked women. She was a libertarian (of course!) and would argue about politics with me in school.

And no, I did not have sex with that woman. I do still think about her, though.

The question is, why do these people want to have sex with minors or students, and not adults?

The case of men doing this is not so mysterious. Maybe they want to have sex, think it’s possible a long term relationship could develop, or just happen to like somebody who is under 18. Women doing this is weirder. If they just wanted to have sex, they could ask random men or hire prostitutes. Young men generally don’t go for older women when they want longer-term relationships.

The idea people initiate these types of relationships for the purposes of power is true for a few cases, but far too reductive. I could believe it’s a misguided attempt to be exploitative. The fact of the matter is that, if the student/child feels pressured, they can admit to the relationship, and result in their partner losing their position of authority or suffering legal consequences.

The meta-answer: they cannot find adults that are suitable replacements, because if they could, they obviously wouldn’t be doing what they are doing. Relationships with teenagers could also provide them things relationships with adults can’t: sexual validation, the dynamic, lower stakes, and the taboo element.

Beyond that — I think these types of offenders are strongly motivated by loneliness.

Evidence

Pedophiles are more introverted and neurotic than regular people.

Accordingly, they are more likely to have fewer friends, and be less likely to psychologically tolerate their lack of them. They don’t have lower levels of conscientiousness, which seems believable, since most pedophilic crimes are premeditated.

Fortunately, we have another study of the personality traits of sex offenders — 32 child molesters, 26 sexual offenders against adults, 31 regular prisoners, and 42 controls:

The pedophile personality profile2 replicates, and regular sexual offenders have similar personalities to them: low extraversion and high neuroticism. Still no difference in agreeableness, a point against the power explanation.

Beyond that, there’s a qualitative study of 10 English women who had sex with male teenagers when they were in a position of authority. Their research suggested that they did not have exploitative motivations, and were more motivated by low self-esteem, issues with their own relationships, and a desire for intimacy. Generally, I don’t trust qualitative research, but this seems fairly credible to me.

There are various typologies of offenders: those with preferences for teenagers/children, situational offenders, and repressed offenders (those who fail with adults). There is something here, but I think the independence between these categories is questionable: why, exactly, do some people prefer minors in the first place? It’s not impossible that personal circumstances and failures with adults could subconsciously raise attraction to minors. People are often attracted to things based on what they mean, not what they are.

Oh, and there is evidence that teacher misconduct regarding students is becoming more common over time. In 2004, 9.6% of students claimed to have been sexually harrassed/abused by educations; in 2023 11.7% claimed to be.

So

There is no one reason why somebody decides to try to have sex with a teenager, though one thing unites all cases: they chose a teenager over an adult, or loneliness. I think a lot of the reasons people espouse out: a lack of morality or desire for power, are framed incorrectly or too reductive. They’re making choices under uncertainty and incentives.

Unfortunately, it is necessary to say that dating a 6 year old and a 15 year old is not the same thing. I’m not talking about morality, or even about harm. The thing about harm-based theories of rules is that there are lots of harmful things people are allowed to do, so something being harmful isn’t a good theory as to why things are not allowed. In terms of ethics, there is a clean difference: some people would privately excuse dating a 15 year old, but almost never a 6 year old. The whole field of “sexually unallowed behaviour” has a lot of issues with categories and words getting mixed up, leading to tedious definitional discussions.

And no. I don’t support removing the age of consent3. And I think rape is bad.

Ideally, the age of consent would be the same as the age of voting, graduating, and working. I think we could design society in a way where people graduate from high school at 15, but #wecanthavenicethings.

2

More: pedopophiles and those who actively abuse children are more neurotic than the regular person:

Non-offending pedophiles (NM) and healthy controls were unlikely to have been abused by an older person when they were a child. Offending pedophiles (FM), on the other hand, were much more likely to have been abused (according to them… at least).

The idea of conservatives eventually outbreeding liberals is old, popularised by Kaufmann indirectly and then the HBD community. Somebody tried projecting the conservative share of the electorate using that data:

The right way to do this is to use the breeder’s equation, which estimates phenotypic changes in populations based on the relationship between a trait and surviving offspring. This is the formula:

The hard part comes with projecting what the relationship will be in the future. What I decided to do was weigh recent years more heavily when calculating the selection differential. If the strong fertility difference between conservatives/liberals persists in the future, then this will be an underestimate; if it regresses to the mean, then it will be an accurate estimate. I used the same measure of political views they did: self-reported placement on a 1-7 conservative/liberal scale.

I controlled political beliefs for age, year, race, and sex effects.

I got 0.07 standard deviations per generation1, 0.035 when taking into account the 45% heritability of political views.

That equates to 0.1 standard deviations per century. That’s equivalent to going from the 50th to 54th percentile of conservatism. About an order of magnitude weaker than the original chart showed.

So, yeah.

Asides

Projecting using only post 2020 data

The change went from 0.035 SD per generation to 0.06 SD.

Better rendition — the correlation between fertility and conservatism has been steadily increasing with every cohort since 1920:

The average person in the GSS dataset was born in 1951, the correlation between conservatism and fertility at this time was about 0.08. In zoomers, it’s about 0.2. If we generously assume that means that the breeder’s equation underestimates the change by a factor of 0.2/0.08, then the projected change per century is 0.21 standard deviations2.

Even then, a person in 2126 is projected to be at the 59th percentile of conservatism of a person in 2026.

Measurement error

Survey responses for measurable data (e.g. height, weight) tend to be very accurate, with correlations between reported and measured data being close to 1. There’s no reason to think number of children is different.

Regarding self-reported political views on a scale — they load at 0.82 on the primary factor of political views (right vs left):

Differential ferility/age gradients by political views

Nope. Conservatives and liberals seem to have kids at the same ages, just at different rates.

Better chart:

Heritability not 45%

Political beliefs are not genetic. The Communist manifesto was not coded into Marx’s genes. That said, there are personality traits and physiologies that are genetically influenced, which then can influence people’s political beliefs.

If one analyses twin/family correlations, a heritability of 40-50% fits the data well.

Many have asked that, if political beliefs are genetic, then why haven’t zoomers gotten more conservative? This is one of the many genotypic ~ phenotypic paradoxes, where one would expect the population to change one way based on the genes, but they actually changed in the opposite direction. It’s an artefact of the environment changing across time.

Causality

I’ve seen people make the argument that children cause people to be conservative more than being conservative causes fertility. It’s not intuitively impossible, but it’s a bad explanation for why the fertility difference between conservatives/liberals haas increased with time. It also doesn’t explain why conservatives want more children than liberals before they even have them.

1

Formula for selection differential (modified for IQ):

Short code:

install.packages('drat')
remotes::install_github("kjhealy/gssr")
library(gssr)
data(gss_all)

selection_differential <- function(iq, kid) {
  d <- data.frame(iq, kid)
  d <- na.omit(d)
  d$iq <- normalise(d$iq)
  parameter <- 0
  estimate <- 0
  daf <- data.frame(parameter, estimate)
  daf$parameter <- nrow(d)-2
  daf$estimate <- 1/nrow(d)*sum((d$iq-mean(d$iq, na.rm=T))*d$kid, na.rm=T)/mean(d$kid, na.rm=T)
  return(daf)
}

selection_differential_regression <- function(iq, kid) {
  d <- data.frame(iq, kid)
  d <- na.omit(d)
  d$iq <- normalise(d$iq)
  parameter <- 0
  estimate <- 0
  daf <- data.frame(parameter, estimate)
  
  if(nrow(d) > 0) {
    lr <- lm(data=d, kid ~ iq)
    
    daf$parameter <- nrow(d)-2
    daf$estimate <- lr$coefficients[2]/lr$coefficients[1]
  }
  else {
    daf$parameter <- NA
    daf$estimate <- NA
  }
  
  
  return(daf)
}

############################################

> gss_all$year_weight <- gss_all$year - min(gss_all$year, na.rm=T) + 1
> 
> describe2(gss_all$sex)
# A tibble: 1 × 10
  var       n   mean median      sd   mad   min   max     skew kurtosis
  <chr> <dbl>  <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
1 x     75568 1.5574      2 0.49669     0     1     2 -0.23125  -1.9465
> 
> gss_all$rcas <- 'White'
> gss_all$rcas[gss_all$race==1] <- 'White'
> gss_all$rcas[gss_all$relig==3] <- 'Jewish'
> gss_all$rcas[gss_all$race==3] <- 'Other'
> gss_all$rcas[gss_all$race==2] <- 'Black'
> gss_all$rcas[gss_all$hispanic>1] <- 'Hispanic'
> 
> gss_all$norm_pol <- normalise(gss_all$polviews)
> 
> lr <- lm(data=gss_all, norm_pol ~ rcs(year, 5) + rcs(age, 5) + rcas + sex)
> summary(lr)

Call:
lm(formula = norm_pol ~ rcs(year, 5) + rcs(age, 5) + rcas + sex, 
    data = gss_all)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5315 -0.6405 -0.0352  0.6406  2.7766 

Coefficients:
                     Estimate Std. Error t value             Pr(>|t|)    
(Intercept)         -17.92111    4.23071   -4.24    0.000022789504732 ***
rcs(year, 5)year      0.00881    0.00214    4.12    0.000038043096970 ***
rcs(year, 5)year'    -0.02203    0.01069   -2.06                0.039 *  
rcs(year, 5)year''    0.03622    0.03612    1.00                0.316    
rcs(year, 5)year'''  -0.00622    0.04704   -0.13                0.895    
rcs(age, 5)age        0.00985    0.00181    5.44    0.000000053434210 ***
rcs(age, 5)age'       0.00284    0.01574    0.18                0.857    
rcs(age, 5)age''     -0.02919    0.04344   -0.67                0.502    
rcs(age, 5)age'''     0.04268    0.04180    1.02                0.307    
rcasHispanic          0.14476    0.01927    7.51    0.000000000000059 ***
rcasJewish           -0.34026    0.03050  -11.15 < 0.0000000000000002 ***
rcasOther             0.04799    0.02271    2.11                0.035 *  
rcasWhite             0.22806    0.01151   19.82 < 0.0000000000000002 ***
sex                  -0.07173    0.00778   -9.22 < 0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.985 on 65081 degrees of freedom
  (10604 observations deleted due to missingness)
Multiple R-squared:  0.029,	Adjusted R-squared:  0.0288 
F-statistic:  150 on 13 and 65081 DF,  p-value: <0.0000000000000002

> 
> gss_all$norm_pol2[!is.na(gss_all$age) & !is.na(gss_all$rcas) & !is.na(gss_all$sex) & !is.na(gss_all$age) & !is.na(gss_all$norm_pol)] <- lr$residuals
> gss_all$norm_pol2 <- normalise(gss_all$norm_pol2)
> 
> lr <- lm(data=gss_all, childs ~ norm_pol2, weights = year_weight)
> summary(lr)

Call:
lm(formula = childs ~ norm_pol2, data = gss_all, weights = year_weight)

Weighted Residuals:
   Min     1Q Median     3Q    Max 
-15.86  -6.09   0.24   4.84  47.02 

Coefficients:
            Estimate Std. Error t value            Pr(>|t|)    
(Intercept)  1.84386    0.00617   298.9 <0.0000000000000002 ***
norm_pol2    0.13347    0.00600    22.2 <0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 8.86 on 74563 degrees of freedom
  (1134 observations deleted due to missingness)
Multiple R-squared:  0.00659,	Adjusted R-squared:  0.00658 
F-statistic:  495 on 1 and 74563 DF,  p-value: <0.0000000000000002

> 
> lr <- lm(data=gss_all, childs ~ norm_pol2)
> summary(lr)

Call:
lm(formula = childs ~ norm_pol2, data = gss_all)

Residuals:
   Min     1Q Median     3Q    Max 
-2.192 -1.754  0.019  1.069  6.352 

Coefficients:
            Estimate Std. Error t value            Pr(>|t|)    
(Intercept)  1.91171    0.00642   297.7 <0.0000000000000002 ***
norm_pol2    0.10495    0.00642    16.3 <0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.75 on 74563 degrees of freedom
  (1134 observations deleted due to missingness)
Multiple R-squared:  0.00357,	Adjusted R-squared:  0.00355 
F-statistic:  267 on 1 and 74563 DF,  p-value: <0.0000000000000002

> 0.2/0.08*0.05504*0.45*100/30
[1] 0.2064
> 
> 0.10522/1.91171
[1] 0.05504
> 
> GG_scatter(gss_all, 'norm_pol2', 'norm_pol')
`geom_smooth()` using formula = 'y ~ x'
> 
> 0.13359/1.84387
[1] 0.072451
> 
> lr <- lm(data=gss_all, childs ~ norm_pol)
> summary(lr)

Call:
lm(formula = childs ~ norm_pol, data = gss_all)

Residuals:
   Min     1Q Median     3Q    Max 
 -2.27  -1.59   0.00   1.00   6.55 

Coefficients:
            Estimate Std. Error t value            Pr(>|t|)    
(Intercept)   1.8754     0.0067   280.0 <0.0000000000000002 ***
norm_pol      0.1921     0.0067    28.7 <0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.72 on 65654 degrees of freedom
  (10043 observations deleted due to missingness)
Multiple R-squared:  0.0124,	Adjusted R-squared:  0.0124 
F-statistic:  823 on 1 and 65654 DF,  p-value: <0.0000000000000002

> 
> 
> selection_differential(iq=gss_all$norm_pol, kid=gss_all$childs)
  parameter estimate
1     65654  0.10244
> selection_differential_regression(iq=gss_all$norm_pol, kid=gss_all$childs)
  parameter estimate
1     65654  0.10244
> selection_differential(iq=gss_all$norm_pol2, kid=gss_all$childs)
  parameter estimate
1     74563 0.054888
> selection_differential_regression(iq=gss_all$norm_pol2, kid=gss_all$childs)
  parameter estimate
1     74563 0.054888
> lr <- lm(data=gss_all, childs ~ norm_pol2)
> summary(lr)

Call:
lm(formula = childs ~ norm_pol2, data = gss_all)

Residuals:
   Min     1Q Median     3Q    Max 
-2.192 -1.754  0.019  1.069  6.352 

Coefficients:
            Estimate Std. Error t value            Pr(>|t|)    
(Intercept)  1.91171    0.00642   297.7 <0.0000000000000002 ***
norm_pol2    0.10495    0.00642    16.3 <0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.75 on 74563 degrees of freedom
  (1134 observations deleted due to missingness)
Multiple R-squared:  0.00357,	Adjusted R-squared:  0.00355 
F-statistic:  267 on 1 and 74563 DF,  p-value: <0.0000000000000002

> 0.10522/1.91171
[1] 0.05504
> 
> qnorm(0.54)
[1] 0.10043
> mean(gss_all$cohort,na.rm=T)
[1] 1951.9
> qnorm(0.58)
[1] 0.20189

> lr <- lm(data=gss_all, childs ~ norm_pol2) #no weights because the correlation is already adjusted to mirror that observed in zoomers
> summary(lr)

Call:
lm(formula = childs ~ norm_pol2, data = gss_all)

Residuals:
   Min     1Q Median     3Q    Max 
-2.194 -1.754  0.018  1.069  6.353 

Coefficients:
            Estimate Std. Error t value            Pr(>|t|)    
(Intercept)  1.91171    0.00642   297.7 <0.0000000000000002 ***
norm_pol2    0.10522    0.00642    16.4 <0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.75 on 74563 degrees of freedom
  (1134 observations deleted due to missingness)
Multiple R-squared:  0.00359,	Adjusted R-squared:  0.00357 
F-statistic:  268 on 1 and 74563 DF,  p-value: <0.0000000000000002

> 0.10522/1.91171
[1] 0.05504
> 0.2/0.08*0.05504*0.45*100/30
[1] 0.2064

First of all

There are some genuine faults in Nietzsche’s philosophy.

It is opaque and difficult to understand. Nietzsche did write like this on purpose, he was a critic of systematic thought, but it makes for a rough reading experience. Experienced readers will still find most of what he writes unintelligible; he writes in a figurative, older prose that modern readers are not used to. The subject matter is also highly complex.

Beyond that, I don’t think he highlighted his philosophy of identity enough, which is central to a lot of his other ideas. A lot of the confusions about “creating your own values” go away when you realise that Nietzsche didn’t believe in a fundamental, unchanging self; he viewed it as an artefact of cognition and language.

“You” don’t create your values, you do. Nobody just “decides” to be a Communist because of how large their elbows are; they do it because the philosophy aligns with the body1 — personality, cognition, and physique.

His fixation on Christianity is... regrettable. He was right to ignore logical/historical arguments regarding the truth of the religion and attack it on other grounds, but the amount of effort he spent on the issue far surpassed how relevant it was. The Antichrist in particular is a boring book.

His critics

The biggest knocks against Nietzsche: autistic obsession with Christianity and bad communicator. Both are forgivable — either due to the circumstances of his birth, untimely death, or consequences of his personal style.

Otherwise, the critics are flat. They can all be cleanly boxed into three groups:

• The metanarrativist: Nietzsche’s philosophy undermines their metanarrative (Christianity, Judeochristian morality, Marxism, Nationalism…), so they do not like him. The most boring and predictable critics.

• The contextualist: people who dislike Nietzsche’s philosophy because of his followers or Nietzsche himself.

• The bull: somebody who made an honest attempt to read Nietzsche’s philosophy, and either didn’t understand it because they took it literally, or are unintelligent.

Often, the metanarrativists try to justify their criticisms of Nietzsche by addressing him, his following, or his philosophy. Because they have ulterior aims, said criticisms end up being either boring or sloppy.

None of Nietzsche’s critics are worth reading for their own sake2. They are engaged in a tug-of-war with a ghost; They hate him for what he threatens, not who he is.

Onto the specific criticisms:

Nietzsche was a nihilist

Nietzsche was a scholar of nihilism, which he rightly understood as the defining aspect of modernity. In popular discourse, there are two caricatures of Nietzsche: one of them was that he was a teenage nihilist3 who thought people should do whatever they feel like; the other is that he saw nihilism as a plague and sought to cure it.

The funny thing is that, when you read Nietzsche, it’s not even clear which caricature is the real face. Part of this is because he discusses nihilism the most in the Will to Power4, a book published of posthumous notes. Predictably, it’s an awfully written slog, with some gold in the garbage.

Nietzsche uses the word nihilism loosely — a historical force, a psychological state, and a philosophical concept. If we look at his published notes, he does not define it as a loss of meaning or values, but as the devaluation of the highest values: justice, morality, logic, and rationality.

2 (Spring-Fall 1887)’

What does nihilism mean? That the highest values devaluate themselves. The aim is lacking; “why?” finds no answer.

The catch: defining it as a philosophical concept also opens it up to other definitons. The latent influence of the philosophical state of the world is ‘nihilism as a historical force; its influence on the human brain is ‘nihilism as a psychological state’. As such, I think Nietzsche’s liberal use of the term is justified, but I can’t help but feel like the term ‘nihilism’ does not mean anything anymore.

The Will to Power analyses nihilism descriptively. I believe Nietzsche’s dichotomy between active and passive nihilism was an attempt to cross between the descriptive to prescriptive. Essentially, active nihilism is what happens when somebody grows so strong that their older, higher values no longer fit with them. Passive nihilism is what happens when you are weak, can’t believe in your own values, and give in to comfort.

Nihilism. It is ambiguous:

A. Nihilism as a sign of increased power of the spirit: as active nihilism.

B. Nihilism as decline and recession of the power of the spirit: as passive nihilism.

23 {Spring-Fall 1887)

Nihilism as a normal condition.

It can be a sign of strength: the spirit may have grown so strong that previous goals (“convictions,” articles of faith) have “ become incommensurate (for a faith generally expresses the constraint of conditions of existence, submission to the authority of circumstances under which one flourishes, grows, gains power). Or a sign of the lack of strength to posit for oneself, productively, a goal, a why, a faith. It reaches its maximum of relative strength as a violent force of destruction—as active nihilism.

Its opposite: the weary nihilism that no longer attacks; its most famous form, Buddhism5; a passive nihilism, a sign of weakness. The strength of the spirit may be worn out, exhausted, so that previous goals and values have become incommensurate and no longer are believed; so that the synthesis of values and goals (on which every strong culture rests) dissolves and the individual values war against each other: disintegration— and whatever refreshes, heals, calms, numbs emerges into the foreground in various disguises, religious or moral, or political, or aesthetic, etc

My personal reading is that neither caricature — the teenage narcissist nihlist and nihilism disrespecter — are the real faces. Nietzsche saw nihilism as a state to be confronted, not something to embrace or fight. Moreover, if you read the Will to Power, it seems he admitted to being a nihilist in the past:

25 (Spring-Fall 1887)

On the genesis of the nihilist.--It is only late that one musters the courage for what one really knows.’. That I have hitherto been a thorough-going nihilist, I have admitted to myself only recently: the energy and radicalism with which I advanced as a nihilist deceived me about this basic fact. When one moves toward a goal it seems impossible that “goal-lessness as such” is the principle of our faith.

Nietzsche the person

I haven’t dived deeply into his biography. He had a neurological disorder, likely CADASIL, which gave him frequent headaches, bouts of fatigue, and worsening vision. He became incontinent in 1889. This limited his movement for some periods of time; in others he would go hike dozens of kilometers a day in spite of his illness.

Cognitively, obviously brilliant. In school, he got above average, but not excellent marks. By the time he was a young adult, he was an excellent philologist and was appointed as professor at the age of 24 at the University of Basel without a doctorate. In his lucidity, I’d guess he had an IQ of 130-160.

His mental state, before his insanity, is up for debate. It’s clear that his health problems and personal relationships caused a lot of strain on him. However, if you read his philosophy, he’s clearly not an unhappy or deranged person. He believed that a person’s philosophy was downstream of their body, affirming life and joy. His tone in writing is typically either critical, elated, or plain — not one of somebody miserable.

Physically, he was tall for his time (173cm6) and decent looking7.

Socially, he was considered polite, graceful, but a little awkward. It’s likely he died a virgin, and several women rejected his proposals for marriage. His personal relationships, like with the Wagners, or with his family members, were rather tumultuous. Sometimes due to his own behaviour.

There are lots of myths about Nietzsche: breaking down and hugging a horse, meeting the king of Brazil, catching syphilis in a brothel, his philosophy drove him mad, and of having no friends. Even the story of Lou Salome rejecting Nietzsche’s proposal for marriage is in question, though I think it is likely that happened.

An acceptable life, all things considered.

Which is why, when people attack Nietzsche, they either resort to false accusations or try to argue that his life was not in line with his personal philosophy. That he promoted strength, but was a weak man. An incel who supported eugenics. Generally an uninteresting avenue of debate, especially when one considers that Nietzsche himself would have supported reading his philosophy as an autobiography.

His followers

Many philosophers have been remembered and discussed. Few have active followings.

Nietzsche’s is massive: forums, youtube, social media, the public, and academia. On every side of the political isle, be they Marxists, Rationalists, Fascists, or Liberals.

It’s easy to say that the philosopher and his followers are different, but in practical social life, people often encounter the philosopher through their followers. Philosophy is can be an affiliative activity.

I personally find this criticism boring, I will concede that it is not a bad reason to avoid him. I suspect part of the reason why I have haven’t read French and analytic philosophers is their fans — pretentious and conceited in the case of the French; sterile in the case of the analytics.

Moreover:

He doesn’t advocate for anything

This is… mostly true. A feature, not a bug. Questions over answers.

Nietzsche is fundamentally a negative philosopher. He is trying to break things: egalitarian morality, weakness, Christianity, other philosophers, and metanarratives. He showed us what nihilism was, and told us to look at it. His positive vision is individualistic, that people need to create their own values and paths in life.

This also segues into the earlier point. Many try to twist Nietzsche’s philosophy to accommodate their own ends; Nietzsche never told people to become warlords, lazy Europeans, Marxists, or high school jocks. It’s forgivable to ignore him based on his followers, but ultimately unsound.

Also, a comment on this from polytropos (the anime/history/philosophy guy):

38

It is imperative to always be on guard against dogma, against theoretical frameworks, against anything that promises an easily achieved intellectual rest. Many weak intellects have fallen under the spell of thinkers like Spengler and Evola, who promise simple and schematic answers to the exceptionally difficult problems of history and mythology, owing to this temptation. Nietzsche took the greatest pains in his late period to avoid encouraging dogmatic thinking in his readers. It is not without reason that the doctrine of eternal return, the core of his thought, is presented only twice in his published works. Nietzsche is the prophet of our age, he was the first to recognize and describe nihilism, he furthermore did so with genius, and for that reason he is the greatest educator we have. But I would advise caution in approaching Thus Spoke Zarathustra and his unpublished notes. An unbelievable amount of garbage has already been written about these works, the time is not yet right for them to be understood in their full significance. Heidegger, although his lectures on these works and Nietzsche are far and away better than any other commentary, did Nietzsche a great disservice in approaching his thought from a principally metaphysical standpoint.

Nietzsche was a Fascist

Regarding his political beliefs, I suspect that the polemic against him — that he was a sexist, eugenicist, and racist elitist — is more true than is commonly believed. If you take what he says literally, that is what you have to conclude. I have to say, that even if that were all true, I would not care. The idea that he was a leftist is even more indefensible; he only selectively criticised the right — Christians, nationalists, traditionalists, and anti-semites — the factions he did not like.

He worships power

If that was true, he would worship the crowd, the most powerful thing in the universe.

Regarding the Will to Power, it’s one of Nietzsche’s more undeveloped ideas. It’s also not what people imagine it to be — Nietzsche conceived of the will to power as a latent metaphysical force behind life. It was an idea formed in response to the Darwinists and Schopenhauer (will to live vs will to power), who thought that the purpose of life was longevity/reproduction. Nietzsche thought that was a teleology; the fact that organisms that reproduce tend to remain in the ecology does not imply that reproduction is the “goal” of life. Modern evolutionary scientists will clarify this issue of teleology in words, but often they buy back into the Darwinian frame anyway.

He encourages evil/cruelty/meanness

Not really. He himself was a polite person.

I suppose that, if somebody thought that they felt elated when they were being cruel, that he wouldn’t object to it on moral grounds.

Enabling, maybe. Encouraging, no.

Nietzsche isn’t cool

Rarely an explicit argument, but definitely there. “Teenage edgelord”, “all vibes”, “gateway drug”, “a stage to go beyond”, “old news”, “undergrad”, “nazi”, etc.

Also false. Nietzsche had the most aura of any philosopher ever.

The moustache. The mythology. The style. All real.

In fact, he has so much aura that all of his terminology — overman, resentiment, God is dead, slave morality, will to power — is all memes now. People are always ready to suss out who the “real Nietzscheans” are and who is just pretending or grifting. Nobody would bother doing that, or bother pretending to be Nietzschean if he was just a loser.

Nietzsche has already been socially metabolised

Often not made as a real, descriptive claim, but worth refuting in its own right.

To some extent, Nietzsche’s ideas have already been absorbed. The ‘God is dead’, last man, focus on nihilism, and psychology of resentment are all common online.

That said, it’s hard to look at a world so moralistic, resentful, religious, dogmatic, focused on “identity”, and say — “hey guys, Nietzsche has already been dissected, nothing to see here”. Only the most digestible and prescient aspects of his philosophy have been eaten; everything else remains ripe.

Nietzsche’s philosophy is unsystematic, but it’s not random. A lot of his lesser known concepts — amor fati, eternal recurrence, identity, determinism — do relate to his broader ideas. The popular Nietzsche is a gun with no target. Even people pliable to Nietzsche believe in a half baked version of what he wrote about.

His denial of absolute truth is self-refuting

In a way, yes. Radical skepticism swallows itself.

Nietzsche’s denial of objective knowledge looks performative and stupid if you believe in objective knowledge; obvious if you do not. Both of these perspectives of Nietzsche’s perspectivism are wrong.

Perspectivism is very hard to sell. Making logical arguments for it looks silly, and just asserting it is empty. Nietzsche tried to make the case for it — by exposing the logical absurdity of a privileged perspective, noting the limitation of language, doubting whether to doubt everything, and questioning whether truth can exist for truth’s sake —and I think he did it well. If anything, he didn’t go far enough. Many “perspectivists” do not take their perspectivism seriously.

There’s also the misunderstanding that, because Nietzsche was a perspectivist (he was), that he thought all perspectives were equally valid. He didn’t think there was something cosmic that elevated some perspectives over others, he thought they differentiated themselves through other means: whether they denied or affirmed life, for example.

If you believe in “regular philosophy” — stable identity, logic, moral realism, causality, free will — then what Nietzsche speaks of sounds bizarre. People with those views often cannot imagine what it feels like to hold those views, so they dismiss them.

Genealogy of morality = genetic fallacy?

The idea behind this argument is that Nietzsche traces the development of slave morality to historical and psychological conditions that occurred in the Ancient Jewish population, that developed into Christianity. He then criticises said morality based on how it emerged. On a logical level, this is to some extent true, but Nietzsche’s historical work on morality is still interesting in and of itself.

Genealogy is far from Nietzsche’s only objection to traditional morality. He thinks that pity multiplies suffering unnecessarily, that free will doesn’t actually exist, and that people want to believe in it because it pokes holes in the idea of moral responsibility.

The Eternal Recurrence

Critics of Nietzsche’s object level philosophy do not understand this because they take it literally. Nietzsche formulated the eternal recurrence — the idea of living infinite identical lives — as a thought experiment. He thought that if you embraced the idea of living infinite identical lives, you affirmed life.

Further reading

• The man himself. I recommend Beyond Good and Evil, the Genealogy of Morality, Twilight of the Idols, and his Wikipedia page — it’s decent. The Will to Power was good but poorly written and extremely uneven. The Antichrist was just boring.

• Selected posts of king Polytropos

• Uberboyo

• Essential Salts

• The Machiavellians

• Weltgeist

• It is linked to divorce + marital dissatisfaction. Likely for both sexes, though it’s less clear for men because a lot of analysis is female-specific. The link is not particularly strong, especially after 0-1 bodies.

• Confounding — people with high body counts tend to have traits less suited to long-term relationships — is a reason why.

• Causality — body count causing people to be that way — is unlikely.

All still true.

Now here’s the problem. My take at the time was that it would be more effective to analyse people’s romantic histories on a case-by-case basis instead of the number. That was wrong.

Beyond the question of body count vs case-by-case analysis, I think a better dichotomy is the mechanics of a person’s romantic history vs the interpetation of it. The mechanics describe how many people somebody has been with, how long their relationships lasted, and how they met them. The interpretation is why those things happened.

My argument, broadly speaking, is that mechanics beat interpretations. The mechanics are harder to fake, and the whys are often too overdetermined to be of any use, even when they are genuine.

Narratives

People have a tendency to renarrate their personal histories. They will try to divert blame from themselves towards circumstances, other people, or facets of themselves that they don’t feel identified with. As such, you should expect that when somebody tells you their romantic history, it will be retold in a fashion that is favourable to them (often other people too, so they don’t look mean or gauche).

Numbers can’t be retold. As much as people meme about women lying about body count, I don’t think it’s common. That famous polygraph study people like to cite is garbage1. Women/men both report roughly the same number of sexual partners when attached to a (fake?) polygraph in comparison to when they are not.

Putting the stats aside, I think that people are simply less likely to lie about raw numbers because it’s easier to get caught doing so. Things like romantic history, which are overdetermined2, are much more easy to “lie” about because there sometimes isn’t even a right answer to what caused a breakup.

Given that, body count gives you a mediocrely reliable indicator of how likely a person is able to maintain a positive relationship; romantic history is important but its interpretation can be easily spun. Both are useful, but should be doubted.

There are situations where the narrative is useful. Like “my boyfriend died” is a pretty good excuse for breaking up. “We grew apart” could mean 99 different things.

Psychological stickiness and stereotypes about body count

The barebones model of romantic relationships is that you meet people, and if you like them, you get into a romantic relationship. Then, if you really like them, you get married.

A few nuances: sometimes people change. Never at a deep level, but on an emergent one they definitely do. Jobs, incomes, bodies, ages, beliefs, interests, attitudes, and whatnot can vary across time. So, sometimes, people go from liking somebody to not liking them. This can also manifest in terms of subverted expectations: you expect somebody to become a higher earner or more mature as they age, but they do not. Other times, you like the person you are with, but somebody else appears who looks more appealing.

Sometimes, when these things happen, people break up and try to find somebody else. Other people are more “psychologically sticky”. They are more willing to stay with their current partners, even when it appears they are starting to falter or change in ways they don’t like.

What psychological stickiness looks like in the real world is probably some combination of higher conscientiousness, close-mindedness, introversion, agreeableness, and conflict tolerance (though the last two things anticorrelate)3. It’s an emergent trait, not a latent one.

There’s two catches.

One of them is that psychological stickiness works. It might be inherently desirable or undesirable for reasons beyond making relationships last longer, but it does the job.

The other is that there is a sex-loading. Men put more weight on appearance; women put more weight on social standing and earnings. Both of these things can change, but social standing and earnings change more unpredictably than appearance does. As such, it follows that there is more to be gained for men to select for psychological stickiness than women.

Beyond that, women care more about social proof than men. If a guy thinks a girl is great and no other guy does, then that’s probably a bonus for him. If a girl thinks a guy is great and no other girl does, she might start to doubt whether that guy is actually as good as she thinks he is. As such, for men, body count as a double edged sword: high n count means social proof + low stickiness, but low n count means no social proof + high stickiness.

Other explanations for the double standard do not make sense. False paternity does happen, but it’s extremely rare in Western countries (like 1-2%). Women are more at risk of STDs than men are. Most of the associations between n-count and personality exist for both men and women. Some of the discourse is poisoned by irrational disgust responses and religiosity, but I find it hard to believe that is all there is too it.

A third catch I forgot to mention: some couples should definitely break up.

Beyond the stickiness component, adults with no romantic history often have traits that are unfavourable as well. From an informal survey on male incels4: about a third have autism, 86% have experienced bullying, and half claim to be extremely lonely.

People also checked which people tended to be sexless in the UK Biobank (age range of 40-70) — it correlated the most with not having a car, low income, and not using recreational drugs. Relationships were similar in men and women, but when they differed, they did so in a sex-stereotypic fashion. The effect sizes are typically weak.

Genetically, being an incel correlated with higher IQ5, income, being autistic, lack of PTSD/ADHD, drug abstinence, introversion, and a tendency to not take risks6. The genetic correlations are much stronger than the phenotypic ones.

Which is interesting. Phenotypically, not having sex seems to correlate with lower social standing or a lack of social capital; genetically, it seems to be more downstream of neurotype than any actual pathology.

It makes sense intuitively. People are presented with different romantic options as they live — some bad, some good. People who are less likely to engage with them are either refusing to accept the bad options, or just don’t have many to begin with. The lack of options is downstream of both circumstance and genes, the refusal to accept the bad options is more genetic.

On many occassions, when romantic options arise, sometimes there isn’t a right or wrong choice regarding whether to take them. Maybe they look a little iffy, but have upsides, or the relationship could progress to a better point in the future. Some personality types are more likely to default to no than yes.

In summary, stereotypes regarding body counts (0 = introvert, low social capital, autistic; high = psychologically unstable, high novelty seeking) are generally true. There’s a few false ones, like male incels being more right wing; in real data, they have a slight skew to the left.

I will say — I am an incel, and the demographic/genetic/phenotypic profile of what an incel is fits me to a comical degree…

Stickiness and satisfaction

See:

The problem with this chart is that most, if not all, of the effect of body count on marital satisfaction comes from deeper psychological confounding. Not the antisticky strategy or the actual body count itself. The causality itself might also be out of order; a tendency to be dissatisfied itself could be a mechanism by which people become less psychologically sticky.

Actually, why not, let us assume that 100% of the association is due to deep psychological factors. That means that selecting people before getting into relationships or having sex with them does work as well as selecting after… If we trust the stated7 preferences of course.

Allow me to explain. Let us say there is a world where there is tons of massive valuable information to learn about people after you get into a relationship with them. Then, we would expect people who had higher body counts before getting married to have more satisfied and stable relationships. The opposite turns out to be true. It is still possible for people to fake personalities and behaviours in the short term, but it never works in the long term. The drives find a way to manifest.

In the data, you also see that people who get married extremely young or quickly are more likely to get divorced. This statistic is much more complicated than it looks — beyond the surface level confounding issue, there is also the problem that people are just more likely to switch partners when they are younger, and it’s less costly to do so.

I wouldn’t bet gold on this part of the argument, but I would be wary of assuming that there is much to learn about a person beyond the first few months of dating. They might just end up being exactly the person you think they are, and if they do have issues or traits that are harder to see up front, it is safe to assume that applies to other people as well.

Another thing

Regarding girls who go wild and accumulate 10+ bodies over the course of 3 months for some random reason — I actually don’t think this is that big of a deal. Only if they are young though; the younger they were, the less it matters. If those phases speak to anything, they speak to immaturity or just retardation, which are easy to see in people.

I am not arguing people should go ‘be a real muffugin man who wifes up whores’.

I am saying that your autism should not stop you from marrying the love of your life.

3

Study on predictors of sociosexuality:

We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to prior deanonymization work (e.g., on the Netflix prize) that required structured data or manual feature engineering, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.

This is their pipeline:

There’s a catch, of course. The set of users they selected were ones who already had linkedin profiles attached — but they removed the profile + links from the data before they ran them through the classifier. So the analysis isn’t contaminated, but it is vulnerable to selection bias: people with linkedin profiles in their bios are certainly much more likely to disclose other information in their posts.

Hacker News → LinkedIn. We collect 338 Hacker News (HN) users who linked a LinkedIn profile in their publiclyvisible HN bio, providing verified real-world identities as ground truth. We first summarize each user’s HN activity (comments and stories) into a structured profile. Then we create a search prompt and anonymize it (see Appendix A for details), and pass it to the agent. The agent correctly identifies 226 of 338 targets (67%) at 90% precision (95% CI: 86–93%; 25 incorrect identifications, 86 abstentions).

When the model sorted through 125 interviews with scientists, it could only identify 9 of the 33 (33, presumably because they didn’t pass in all 125) subjects from the interview:

Anthropic Interviewer transcripts. Anthropic publicly released transcripts of 125 AI-conducted interviews with scientists [2, 15], in which participants described their use of AI in their research. The transcripts were partially redacted for participant privacy. Li [19] showed that LLM agents with web search can match project descriptions in these transcripts to published papers; specifically, they use a task decomposition approach [17] to bypass safeguards by breaking the attack down into individual benign tasks. They eventually ran their attack on 24 scientists conversations and recovered 6 identities. We attack the same dataset with our pipeline, without task decomposition. Concretely, we construct profiles from transcripts in a similar way as we do for Hacker News and Reddit users; and then run the agentic search on 33 scientists that talked about their past research in the transcripts. The agent correctly identifies 9 out of 33 scientists at 82% precision (2 wrong, 22 refusals or abstentions). Here, we are uncertain about the true deanonymization rate, as we have to manually verify the matches. The agent claims to identify 11 scientists; but for 2 out of 11 we notice the transcript contradicts the identified academic profile in some ways.

Will anonymity die?

Probably, at some point. Not through AI agents, in my opinion, which will get easily confused by misdirection, but through data leaks. So much gets leaked every year that, at some point, people are going to start using them to dox people on the internet.

I always found the anonymity debate uninteresting, since the solution I support (your name is whatever you want it to be) is the status quo, I also view this preferred solution as contingent and unstable, so I’m not that invested in it staying that way, though I will be sad to see anonymity go.

If anonymity really does die, then we will find ourselves in an odd place where the only people willing to say interesting stuff on the internet are old, have nothing to lose, are wealthy, and/or are impulsive. Which will be unfortunate for the rest of us, of course, but I’m not convinced that will kill contrarian/anti-establishment sentiment and communities on the internet.

In constraint games, what constitutes a good or bad choice is determined by the world — physics, the market, a compiler, or a scoreboard.

In coordination games, what constitutes a good or bad choice is determined by people themselves — peer review, courts, editors, managers, and voters.

Here is a map1 of which games are which:

The further up and right you get, the more reliable experts in the game are; the further down and left, the less reliable. People in top-right games are paid for being right, and punished for being wrong. People in other quadrants are either inconsistently paid, or other people pay.

The most universal law of human cognition is that people can believe anything — if they are socially incentivised to, and don’t have any skin in the game. This is true in religion, ethics, ideology, and conspiracy theories. Intelligence and maturity are nonfactors, especially if people are examined across time and space. Because of that, coordination games often run off the rails because there is no reliable corrective mechanism that can nudge people towards truer beliefs.

Constraint games run on power — not the ugly kind, but what people normally call leverage: the ability to create things like computers, toilet paper, or concrete. Coordination games run on social status — also not the ugly kind, but what people believe to be aligned, reliable, and powerful. Coordination games, because they do not have corrective mechanisms, are more likely to degenerate into covert rank-jockeying and cargo cults.

So, let me get the elephant in the room out of the way. Constraint games are better.

Why play coordination games, then? Mostly because we have to, and because they feel more natural to us — they involve love, belonging, intensity, rank and status sensitivity, shame, and identity. It’s a lot more common for people to go into STEM because they think that it’s pragmatic, but lack innate and persistent passion.

No game is purely a function of coordination or constraint. Coordination games also have scoreboards — they just work too slowly or unreliably, so people cannot default to them to make decisions. The effect of a party endorsing a policy might not be seen until the next election, and even then, the results of the election could be skewed by individual leaders or the state of the economy.

More notes on these games:

• Markets makes feedback more physical and speeds it up. This is why storytelling and generic art are shifted rightward.

• Goodharting in physical and fast-feedback domains gets punished quickly.

• Engineers pretend to be cool. Priests pretend to have usable dashboards.

• If there is no scoreboard, status is the scoreboard.

• Intelligence is more rewarded in constraint domains; personality traits and tolerance of ambiguity are more rewarded in coordination domains.

• I’d guess that extraverts, feelers, and perceivers (in MBTI terminology) are likely to select into coordination games than constraint games.

• The underlying incentives of a game and the kind of people who select into it might be totally different.

• Medicine is less STEMy than people think it is. There’s a coordination layer to the game where the doctors act as something close to priests who reassure people that they are being taken care of. The standard surgery + antibiotics + drugs stuff works, but overall the field is quite social and delayed in terms of feedback. See the Elephant in the Brain for further information.

• Philosophy is weird. There’s no scoreboard, but it’s not consensus-driven either.

• Zoomers are sorting out of coordination game majors (e.g. humanities, politics, and religion) and into constraint game majors (e.g. STEM, finance). They do play coordination games, but they almost exclusively play them online with little expectation of value.

What he said about hipsters is true, insomuch as Millennial culture engaged in status-signalling through less overt and more plausibly deniable mechanisms. However, when status is competed over in these less overt domains, it is less salient because self-deception, while real, is imperfect and doesn’t always function the way we want to. So I still think the thesis I posited is defensible.

What I think that Russell and I disagree on a more fundamental and metaphysical level is status. I think it is real, insomuch as humans value it, and that its acquisition leads to things that we care about on an intrinsic or instrumental level like attention or social connections. I, however, think that when status is optimised for directly, it leads to worse outcomes on a social level. Russell, on the other hand, views status-seeking as innate to human nature and a social good.

Frankly, I have no idea if what he said about restaurants was true. In 2013, I was 12 years old, didn’t live in the anglo world, and my favourite thing to do was write and read fanfiction. If his thesis is true, then I would cite it as an example of an exception, and if it is false, then it is an example of the rule.

The best example of where status-seeking makes things worse is storytelling.

People disagree on what exactly makes a story good — whether it is the aesthetics, characters, writing, thematic consistency, premise, or ending. Fundamentally, what makes a story good is if it accomplishes what it is meant to do, and whether what it is trying to do is valuable.

Take Harry Potter for example. Harry Potter, as a novel, is trying to connect itself to people. It gives them a set of characters that are easy to relate to, a school that would be a lot of fun to go to, and a vast, expansionary world that lends itself towards fantasising. And it worked. Tons of people absolutely loved it.

I see a grey cloud around Harry Potter. People seem to feel embarrassed about liking it, and it is often criticised for being poorly written or being written by JK Rowling. In other words: it’s low status now. I also see this in anime — people criticising Steins;Gate, an anime which functionally revolves around the protagonist, for having undeveloped side characters. Criticising Code Geass, a story that is trying to create a spectacle and drama, for being… Melodramatic and tumultuous. Critics dislike stories for what they try to do more than how they actually do them.

Beyond what makes a story good, there is also the question of why people like them. A simple theory: taste is shaped by early exposure. Children who grow up with disney films turn into disney adults; people who liked anime as children also like it as adults. They might feel embarrassed by these things, or might not relate to some of the stories they liked as a child, but the software remains installed. I think taste for the style in which a story is told is more environmentally susceptible, while the specific stories people like change with cognitive and emotional development.

When people apply status games to storytelling, the magic disappears. People stop being willing to like what they like, or become insecure about it. Critics become pedantic, deciding the value of stories based on plot holes people don’t notice or care about, instead of the engine that drives everything: telos and meaning. Rather than taking poptimism literally, I see it as a reaction to the increasingly status-insecure and pedantic critics of culture. A 300 kilogram neckbeard who plays runescape because he likes it has achieved a high level of spiritual advancement.

Our revealed preferences also tell us much more about what we like than our stated preferences, which are susceptible to social or personal biases regarding what “should” be good and enjoyable. Critics might not like rap, but it has quite a bit of staying power, and I don’t see exactly why most people would pretend to enjoy it. I like it at least. Tasteism, the pursuit of status through the communication of taste, is a sign of a declined physiology that lacks energy, security, or capacity for action, and therefore resorts to judgement1.

You could argue that status plays a role in what stories people write or why they write them — I don’t deny that. It’s not bad for people to write things for attention, prestige, legitimacy, and money, it’s part of the flow of the universe. What I object to is authors who only write for these reasons, and not to create value and meaning beyond that. To do so is to create slop, art that commands attention only for the purposes of attention. Status-striving attitudes encourage the creation of more slop, in the overt status-seeking zoomer ecology; vapid pretentiousness, in the millennial ecology. Personally, I think the former is worse than the latter, so I guess I’m in agreement with Russell on that front.

On the question of whether taste is relative or absolute, subjective or objective — those are not the correct frames by which to answer this question. A subject is simply another object, and there is no privileged frame of perspective. That doesn’t mean that some tastes aren’t better than others — good taste is harmony with values.

Some taste has an arbitrary and banal compontent to it — preferring redheads over blondes, or white chocolate over dark chocolate. It doesn’t mean anything. In some cases, taste has a component of predictability and self-knowledge to it. That is to say, their cognition is more able to reliably interpret whether they like something independent of their mood and emotional state.

Sometimes, taste can arise due to cognitive gatekeeping. Some people are better at emotionally connecting to other people, and so they will find stories that effectively provoke emotional responses better. Intelligent people will be more able to clearly see thematic or philosophical components behind stories, and as a result, events that involve this theme will feel more meaningful to them.

I then ask: what has overt status-seeking made better? College applications? Dating? The internet? Hugging cats on the street?

So

My thesis is not that other people shouldn’t strive for status — dominance, prestige, legitimacy, money, and attention2. Other people’s value functions aren’t something that I have intimate knowledge of, and there is no cosmological tablet that states “though shall not seeketh status”. What I do think, however, is that when people start competing overtly over these limited resources, that things just seem to get worse at scale — regardless of whether the competition is overt, covert, or self-deceptive. It does make people more motivated, but it doesn’t make them motivated to do the right things.

The dark triad is a set of three personality traits — machiavellianism, narcississm, and psychopathy — that are related to “dark” behaviour. I define these as:

• Narcissism: intentionally distorting your image to the world while filtering social feedback and judgements through the distorted image.

• Machiavellianism: deliberate, counterintuitive, and exploitative behaviour that seeks to extract value from others.

• Psychopathy: emotional disconnect (not necessarily detachment) from surrounding individuals and insensitvity to punishment.

Statistically, it seems to show decent validity and reliability1. All of the traits correlate with each other and load on a general “dark factor”. It’s similar to low valence in the big five, but encompasses a distinct cluster of traits — low valence is closer to misaligned + incompetent while dark triad is more related to misalignment.

You’d think that a test which is considered by most to be a measure of whether you are a bad person isn’t reliable, because the bad people will not admit to being bad people. I actually think the opposite is truer, that surveys on taboo behaviour encourage people who engage in them to admit to them because it lets them release the internal pressure that accumulates from putting on a mask.

That aside, I do have some big criticisms of this personality model. The first is the tests. They often ask people questions that are either too generic to mean anything or don’t really relate to exploitative behaviour. For example, “it is not wise to tell your secrets” is a question that doesn’t really mean anything — it doesn’t specify what secrets and to who.

I think that the dark triad suffers from underconceptualisation. The recurring pattern in all three of these traits is a fast life history strategy where you optimise for short term outcomes and gains over longer term horizons. The strategy doesn’t need to conscious, or evil for that matter, it’s just the universe expressing itself in different ways. Since everybody dies, some people have shorter horizons than others, but nobody has a permanent horizon.

Shorter term horizons relate to narcissism because the short run encourages engaging in performative2 behaviour where the actor pretends to be more aligned and skilled than they really are. If they are engaging in a fast life history strategy, then what matters to you is the performative image, not your true self3, so your judgements and social feedback are filtered through that mechanism.

Often, narcissism is losely defined as being grandiose, unempathetic, overly vulnerable, exploitative, or self-centered. All of these things correlate with narcissism, but they don’t capture the underlying mechanism.

The relationship between machiavellianism and short-term orientation is pretty clear. When people like other people, they naturally engage in prosocial and long-term oriented behaviour. When I call my mother and tell her I love her, I am not “manipulating her into liking me”, I am just doing whatever feels natural. People who are looking for short-term gain strategies, on the other hand, have to do things that are less obvious or genuine in order to succeed.

I think that affective psychopathy relates to life history because emotionally attaching yourself to people you are going to cheat soon doesn’t really make sense, and punishment insensitivity relates to life history because a high dark triad person will need to do things that are punished by society if they want to be successful.

I don’t think it’s appropriate to conceptualise of dark triad as a psychopathology, but a strategy that works or doesn’t work depending on other character traits and circumstances. Ironic, given tht the word psychopathology derives from psychopathy itself. I assume there are high dark triad people who dislike the way they are, and want to change; my advice would just be to try playing longer games with better people.

Frequently discussed online is the question of whether all humans are selfish. I will first say that I don’t think the selfish-human theory is falsifiable, as altruistic behaviour can always be reinterpreted to be selfish through the lens of evolutionary biology or status economics. I do, however, think that the debate is useless, as people who see themselves as altruistic will not accept that humans are selfish; people who see themselves as selfish will always be skeptical of altruists.

In my opinion, the distinction between selfish and selfless behaviour doesn’t really make sense. If somebody goes to a cat and feeds it, you could argue that their behaviour is selfish because what made them feed the cat and enjoy doing it was neurotransmitter X; others could argue its selfless because the action distributes value from the human to the cat.

From a psychological perspective, everybody is selfish because all value and meaning is local; from an energetic one, people can choose to act in a way that redistributes value from themselves to their surroundings. What is more interesting, in my opinion, is whether people do selfless things because of evolutionary incentives or their own will. The answer is the will — just because organisms are selected for reproduction4, does not mean that the end goal of their actions is to reproduce. To think otherwise is to sneak teleology into science.

Like in the case of the MBTI, I find most criticisms of the theory boring and tedious. Some people point out that the theory originates from evolutionary biology at the species level, which is true, but not particularly interesting. Others argue that it is unfalsifiable, and it kind of is, but unfalsifiable theories aren’t necessarily wrong.

Further reading:

• Review of Evolutionary Psychopathology by Scott Alexander

1

See:

1. The big five is based on the lexical hypothesis, which posits that the structure of personality is hidden in the adjectives we use to describe other people.

2. The lexical hypothesis has been validated, but poorly tested. The use of varimax obscures the general factor of personality, without its use, the five factors are valence, dynamism, order, emotional attachment, and transcendence.

3. The MBTI is not an alternative factorisation of the big five.

The big five started with the lexical hypothesis, which posits that, if there are some fundamental axis on which humans vary, that language can reveal it for us. That is to say, humans invent words that they use to describe other people. Some of those words will naturally overlap, and so principal component analysis1 or factor analysis (almost the same thing) can tease out the underlying factors from our word space. HEXACO, the big five, and the big two are all attempts to decode this factor structure, but with different numbers of factors.

We did it. We tested the lexical hypothesis, and we found extraversion, conscientiousness, neuroticism, openness, and agreeableness.

The problem is that there are many ways to conduct factor analysis, depending on how the factors are rotated. Psychologists default to varimax because it is easier to interpret the factors, but the default method is simply to not rotate the factors at all. No rotation is what happens if you just extract the factors as they naturally are, with #1 being the factor that explains the most variance in the underlying data, #2 being the one that explains the second most variance, and so on.

Varimax is what happens when you take that beautiful, simple thing and twist it. Varimax forces orthogonality2 (uncorrelatednes) in factors by maximising the variance of the squared loadings (squared correlations between variables and factors) within each factor. For example, if you have 18 variables, and extract 3 factors from the data, and you have two possible first factors:

0.5, 0.65, 0.25, 0.5, 0.4, 0.5 …

0.9, 0.94, 0, 0, 0.7, 0.3, 0 …

Varimax will prefer3 the second possibility over the first because it has more variance in terms of loadings. So, even if a general factor exists, its influence will be hidden by the data and redistributed to the lower order factors. The regular big five model appears when you use this particular factorisation method, but not the standard “no rotation” method4.

Andrew Cutler talks about this on his blog and in his dissertation, which involves attempting to extract the big five from written text.

So, first, let me try to explain this extremely complex paper — written by Andrew Cutler and David Condon — which is the bulk of Cutler’s dissertation. He uses natural language processing (NLP), specifically a model called DeBERTa, which is a transformer-based encoder model — something similar to standard LLM.

They teased out word associations with promopts, usually this one:

“Those close to me say I have a [MASK][MASK] and [TERM] personality.”

This sentence is repeated for every word, and the hidden states with 1024 dimensions for each blank ([MASK]) are extracted. These theoretically can be converted into probability distributions which eventually predict the likelihood that each blank is a given word in the model (of which there are 30,000 different options). Using these predictions of likelihood is unnecessary, and Cutler only uses the 1024-dimensional word vectors here. Those two vectors are then averaged for each term, and are then correlated with each other, so a correlation matrix is produced, which is then subject to principal component analysis.

He used the classic 435 adjective dataset from Saucier and Goldberg (1996), which contains self-rating data from ~900 undergrads regarding the 435 terms, to test the hypothesis. He also conducted some robustness tests with different prompts or sets of adjectives. The set of adjectives didn’t matter, but sometimes the prompt made a difference.

This is the ultimate test of the lexical hypothesis. It was positive.

If you map out the correlations between the 435 adjectives in the survey data and the DeBERTa data, the overlap in terms of correlations between words is pretty strong. 75% of the 94,395 correlations between terms were directionally consistent (both were positive or negative) between methods. Besides the similarity in the correlation matrix, the NLP-based method yielded stronger correlations between words than the survey data

In the supplementary materials, he seems to imply that, when the survey and NLP data disagree, the survey data is usually right. Based on what he presents, that seems like a cogent conclusion.

Table 14 also provides a preliminary indication of why the terms are not more perfectly associated, and this is most obvious with the least correlated terms. “Weariless” appears to be well-understood in the S&G data, with nearest and furthest neighbors of “energetic”, “vigorous”, “courageous” and “sluggish”, “lazy”, “lethargic”. The DeBERTa model gets it wrong, with synonyms of “passionless”, “dull”, “lethargic” and antonyms of “intelligent”, “assertive”, “humorous”. This is a bit surprising given the intuitive definition, but some additional research into the frequency of use for this term in ngrams such as “weariless person” suggests that it is rarely used as a personality descriptor; it is not present in Google Books Ngram data since 1900. Despite this poor start for DeBERTa, the circumstances are reversed for the second term (“transparent”) as it has been misunderstood by respondents in the S&G data, on average, but correctly attributed in the DeBERTa data.

When natural language processing is used but different sets of adjectives are used, the inferred factor structure of personality does differ a bit, but the big three are extracted in all three models.

Now, onto the issue with factor rotations When PCA analysis is conducted on big five data with no factor rotation, you get a slightly different big five:

In the unrotated version, the first factor of personality (PC1) is a negative-signed version of the traditional general factor of personality — having negative loadings on agreeableness, extraversion, conscientiousness, and openness; and a small positive loading on neuroticism.

There is a long-standing debate in the personality psychology field regarding whether a general factor of personality really exists. Some researchers argue the general factor of personality is an artefact of some respondents responding in a more socially desirable way, others think that it reflects a general measure of organic functioning. Personally, I’m not interested in the debate, because the idea that some people are generally “more functional” than others and that this bleeds over in various aspects of their behaviour is just intuitive to me.

In a later post, he labels all of these factors that emerged, but with a different set of adjectives (~2800 from an academic paper) and using a NLP model (ROBERTa). He described them as follows5:

1. Valence (affiliation)6. The hidden general factor of personality. Loads positively on gentle, nice, easygoing, optimistic, sociable, happy, honest, reliable, sensitive, and loyal; negatively on cruel, mean, moody, selfish, pessimistic, impatient, bossy, and cowardly.

2. Dynamism. Loads positively on impulsive, adventurous, extraverted, ambitious, aggressive, brave, funny, and emotional; negatively on reserved, conservative, shy, cautious, serious, and calm.

3. Order. Loads positively on determined, stern, exacting, direct, domineering, stubborn, and persistent; negatively on gullible, lax, lazy, easygoing, and cowardly.

4. Emotional Attachment. Loads positively on moody, thoughtful, anxious, fearful, emotional, affectionate, spiteful and caring; negatively on independent, superficial, tactless, and frank.

5. Transcendence. Loads positively on unique, complicated, troubled, star-crossed, handicapped, mystical, heartbroken, and other-worldly; negatively on unphilosophical, fancy-free, pigheaded, boorish, materialistic, self-centered, and glib.

In the MBTI, the case that all types are equal is easy to make, but for the big five, it is simply not. Valence and order are clearly good. Transcendence, emotional attachment, and dynamism are more ambigious or close to value-neutral.

Culter’s dissertation7 shows that the structure of the big five can vary by method (survey data vs DEBERTa aka natural linguistic processing). If you compare the unrotated factors of each of the methods, then only the first three are recovered consistently8. Though it would seem that, generally, the survey data is in the right.

Despite the limitations and inconsistencies of principal component analysis, I think that transcendence and emotional attachment are good candidates for the 4th and 5th factors in the big five. First of all, some people are clearly much more emotionally driven than others if you watch people do things in real life. That’s part of the reason why the big five traditionally has good external validity: it has neuroticism, MBTI does not.

The “transcendence” factor is definitely describing a definitive type of person, and a trait that influences how people act and think. It’s pretty close to MBTI intuiting. Personally the word vector loadings of the transcendence describe me very well, so part of that makes me inclined to think that it is real too.

Beyond my criticisms of how psychologists have treated the lexical hypothesis and created the big five, the lexical hypothesis itself isn’t perfect. I’ll grant that it’s cool, and the fact that it has been empirically supported is extremely cool, but it does have its shortcomings.

The first one is that the test of the lexical hypothesis identifies personality factors according to what is socially considered to be most important. It doesn’t necessarily identify what actually makes people tick and do things. A factor could explain a small amount of variance because there might not be many terms are used to describe it, but it might be salient anyway. It could also explain little variance because people find it difficult to observe in themselves or others. I think it is hard to argue that transcendence and emotional attachment do not have a massive impact on how people feel and behave.

Similarity between the big five and MBTI

The MBTI is a philosophy first, statistics second model; the big five is a statistics first, philosophy second model.

Even if we take the original big five for granted, they don’t even correspond that strongly to the MBTI:

Unsurprisingly, the measures of introversion are highly correlated. Intuiting does strongly correspond with openness, but I suspect this is an artefact of test bias — sensors are more open to physical experiences, and intuitives are more open to abstract concepts.

The correlation between having a feeling>thinking preference and agreeableness is .45. That is not a high correlation. I suspect it exists because most feelings are positive, and if you lean more on your feelings, you end up engaging in more friendly behaviour naturally.

The correlation between judging>perceiving and conscientiousness is also not particularly strong. I suspect it exists because, if you are a conscientious person, then it is easier to administer structure onto the physical world — organise, plan, and schedule things. On the other hand, because the structure and judgement perceivers engage in is internal, their conscientiousness is harder to measure and infer.

I’m not even sure if there is any innate correlation between conscientiousness and judging. I suspect there is, but solely because the average person prefers closure too little and keeping options open too much. As such, conscientious people naturally drift towards more closure than more openness.

Technically, you could try to turn the MBTI into the big five: take the 5 big five traits and make 25 types out of them. The problem is that 25 is way too many to keep track of, and the big five wasn’t really intended to be a typology model, and as a result the types are sloppy and convoluted.

1

Principal component analysis is a statistical method that involves taking a bunch of data and teasing out a number of factors from it based on how much the variables correlate with each other. Specifically, it extracts a number of abstract, mathematical vectors from a correlation matrix, where the vectors are ordered by the percentage of variance they explain in the underlying data. It’s popular in genetics, where people use correlations between different genetic locations to extract the components that explain the highest amount of variance in the data, which ends up being population structure:

4

I tested this myself with the 240 item HEXACO dataset on openpsychometrics and the SG 1996 435 adjective dataset — the latter was pre-cleaned by Andrew Cutler. After combining the loadings of both general factors, using no rotation method leads to more variance in loadings, but the p-value was disappointing (p = .01). Sad.

Visually, in both datasets, the varimax (farot) method seems to lead to more concentration of loadings around zero than the no rotation method (fanorot).

HEXACO dataset, no rotation:

HEXACO dataset, varimax:

SG1996 dataset, no rotation:

SG1996 dataset, varimax:

May as well post code, since it is short:

hexaco <- data


psych::fa.parallel(hexaco[, 1:242], nfactors=40)

fanorot <- fa(hexaco[, 1:242], nfactors=5, rotate='none')
farot <- fa(hexaco[, 1:242], nfactors=5, rotate='varimax')

GG_denhist(fanorot$loadings[, 1])
GG_denhist(farot$loadings[, 1])
sd(fanorot$loadings[, 1])
sd(farot$loadings[, 1])

var.test(farot$loadings[, 1], fanorot$loadings[, 1])


##############

sg1995 <- study1SaucierGoldberg1996data

fanorot2 <- fa(sg1995, nfactors=5, rotate='none')
farot2 <- fa(sg1995, nfactors=5, rotate='varimax')

GG_denhist(fanorot2$loadings[, 1])
GG_denhist(farot2$loadings[, 1])
sd(fanorot2$loadings[, 1])
sd(farot2$loadings[, 1])

var.test(fanorot2$loadings[, 1], farot2$loadings[, 1])

##############

norot <- c(fanorot2$loadings[, 1], fanorot$loadings[, 1])
rot <- c(farot2$loadings[, 1], farot$loadings[, 1])

var.test(norot, rot)

8

Also worth mentioning: these three factors are substantially bigger than the others:

1. The MBTI categorises people according to whether they have a preference for introversion or extraversion, intuition or sensing, thinking or feeling, and judging or perceiving. These four trait preferences interact to form 16 types (e.g. INTJ, ESFP).

2. Within the American population, 60% are extraverts, 75% are sensors, 50% are thinkers, and 55% are judgers1.

3. Occupaional self-selection is downstream of MBTI type.

4. Criticisms of the MBTI model fall apart fairly easily.

Introduction

It started as philosophy. Jung conceptualised people in terms of whether they preferred to use sensing, which is to directly draw information from memory or the senses (e.g. to see a bear running to you); and intuition, which is to make inferences based on raw information (e.g. the bear running towards you wants to attack). Sensing and intuiting are called the ‘irrational functions’ because they are not judging information, but creating it.

Then, humans can prefer to use one of the two ‘rational functions’ — thinking or feeling. Thinking involves judging something according to its function or truth; feeling involves judging something according to its value (e.g. the fact the bear wants to attack you is bad). The brain cannot focus on both types of judgements at the same time, so thinkers tend to have unconscious value judgements; feelers tend to have unconscious fact judgements.

Beyond that, Jung also noticed that some people tend to be introverted, that is to say, they focus on their internal state over the external world; or extraverted, which is the tendency to do the opposite. When looking back at Nietzsche’s The Birth of Tragedy, he thought that the Apollonian was analogous to introversion; Dionysian to extraversion2.

Those four psychological functions can then interact with the introversion/extraversion dichotomy to produce the following eight functions:

1. Extraverted sensing: to experience the real world as it appears. Present-focused.

2. Introverted sensing: to draw upon experiences from memory. Past-focused.

3. Extraverted intuition: to intuit various interpretations of the world as it appears and hold them as alternatives.

4. Introverted intuition: to focus on one particular interpretation of the world.

5. Extraverted thinking: to judge things according to whether they function.

6. Introverted thinking: to judge things according to whether they are true.

7. Extraverted feeling: to judge things according to whether they preserve social harmony.

8. Introverted feeling: to judge things according to whether they are are subjectively valued.

Some clarifications: introverts can be sociable, extraverts can be withdrawn, intuitives can live in the moment, sensors can make inferences, thinkers have feelings, feelers have thoughts, judgers can improvise, and perceivers can plan. All people have access to all eight of the functions. The MBTI is describing preferences, which are acted on when situations are flexible and people can choose to apply their function of choice.

Katherine Briggs and her daughter Myers Briggs built upon Jung’s work in Psychological Types to create the Myers-Briggs type indicator. This involved adding the judging vs perceiving preference, which corresponds to whether somebody prefers to externalise judgements or internalise them. That is to say, judgers prefer to use the extraverted feeling and thinking functions; perceivers prefer to use the introverted feeling and thinking functions.

All four of these trait preferences then combine into 16 personality types:

I like using examples of famous people or fictional characters to describe the types, because it shows what each type tends to look like when they are highly successful or actualised. However, I think that a lot of famous people (especially historical figures) are mistyped, and their image rather than substance is what usually ends up being evaluated.

Whether these preferences are stable or genetic has been debated. Myers herself thought they were moderately heritable (~50%) and stable in adulthood. I, myself, think that the heritability of MBTI type is extremely high (80+%) in adults, comparable to the heritability of ADHD or autism.

The problem with the heritability studies of personality is that they use self-reports instead of peer-reports — when self-reports of the big five are replaced with composites of peer and self reports, the heritability of big five personality traits rises from 40-50% to 75%. The heritability of the MBTI is likely to be higher because it originates in people’s lataent preferences, not behaviour or character.

Isabel Myers then took all of this theory and made the MBTI test, which she was able to administer in 1945 to college students and 1962 to a bunch of high schoolers. With time, she accumulated a lot of evidence to support her theory.

Statistics

(Reminder: I = introvert, E = extravert, N = intuitive, S = sensing, T = thinking, F = feeling, P = perceiving, J = judging). About 60% of people are extraverts, 75% are sensors, 50% are thinkers, and 55% are judgers. The big sex difference is in thinking vs feeling, where 65% of men are thinkers, and 65% of women are feelers.

National merit finalists (top .3% in academics) are ~2x more likely to be intuitives than college prep students, who are ~3x more likely to be intuitives than regular high school students. Academic ability also seems to scale with introversion and a preference for perceiving, but not to the same extent.

Almost all Rhodes scholars are intuitives:

School administrators are overwhelmingly judgers:

Summary of MBTI type and occupational selection:

Assortative mating for MBTI personality exists, but isn’t particularly strong. Extraverts were more likely to be matched in terms of personality than introverts.

65% of Less Wrongers (rationalists) who answered their MBTI type on the site-wide survey in 2012 said they were either INTPs or INTJs. Almost all of them were intuitives.

What each type wants from work. Sensors gravitate towards stability, intuitives towards creativity:

Religiosity by MBTI type3:

Income and MBTI type (E > I; N > S; T > F; J > P):

Trait descriptions

Type descriptions have already been done to death. What is underdone is good descriptions of each axis of preference.

It’s impossible for personality types and preferences to be equal in aggregate value, because they are, well, different. And that’s fine. I generally think that the value differences in MBTI types are small, and that some trait preferences (particularly introversion and sensing) are underrated and their utility is harder to perceive. MBTI typology is best at predicting what people select into, but not how good they are at what they do.

Myers hypothesises that the best-adjusted people are “psychologically patriotic” and are glad to be what they are, and accept it. Which makes sense, since MBTI typology is so heritable and stable, and self-actualisation must build itself on the genetic foundation rather than destroy it. From a functional perspective, an egalitarian view of the MBTI encourages people to accept who they are rather than trying to be something that doesn’t reflect their genetic destiny.

Extraversion vs introversion

At a definitional level, extraverts prefer to focus on their surroundings, introverts prefer to focus on their internal states. There are many biological parameters, values, and upstream preferences (e.g. introverts have higher baseline arousal, extraverts have higher reward sensitivity) that decide this preference, but I don’t think that those things are extraversion or introversion themselves.

Beyond that baseline, definitional description, extraverts and introverts tend to differ in the following tendencies:

• Extraverts act, then think; introverts think, then act.

• Extraverts are easily distracted; introverts less so.

• Extraverts are relaxed and confident; introverts are typically not.

• Extraverts comfortably express their emotions; introverts do not.

• Extraverts comfortably engage in overt status displays or striving; introverts find this unnatural.

• Extraverts suffer from a lack of substace; introverts from a lack of practicality.

• Introverts are more likely to engage in substance abuse4.

Let me get the elephant of the room out of the way. Extraversion has a lot of visible advantages. They tend to have higher incomes, better social skills, romantic partners with more similar personalities to them, more friends, and are less likely to be incels. The general wisdom online is that extraversion is better than introversion. I don’t think this is the case — the advantages of extraversion are just more obvious.

The one, big advantage of introversion is that introverts are more capable of change and action independent of outcomes. If a new business is unprofitable for three years, but has evidence of promise or growth under the surface, an introvert would be more likely to stick with it than an extravert. Extraverts often have to rely on encouragement or external rewards to continue doing something, while “doing it for the sake of doing it” is an introvert thing.

By the very nature of the trait, introverts have more access to their inner world of ideas, feelings, and thoughts, which allows them more self-aware and metacognitive. A rather unexplored area of research is that, by virtue of having more reliable access to their own feelings and ideas, that perhaps introverts give more accurate responses on surveys, and that this distorts observed statistical relationships.

Defenders of introversion, in my opinion, have relied too much on Susan Cain’s ideas. In Quiet: The Power of Introverts in a World That Can’t Stop Talking, Cain argues that introverts are systematically oppressed in the modern world and that the ideal person is an extravert — charismatic, high energy, and persuasive. I don’t disagree (or agree, necessarily) with what she says on an object level, but the grievance-based approach is undignified. It’s not surprising that the environment rewards those who love the environment more.

Intuition vs sensing

Intuition is a tendency towards making inferences and intuitive leaps from a given set of base information (be it memories, sensory data, or even ideas), while sensing is a preference for focusing on the base information itself. Intuitves are more likely to trust their intuitions and engage with them, sensors not so much. Most people are sensors, especially beyond the developed world. In terms of tendencies:

• Intuitives have higher levels of risk tolerance in abstract domains, sensors have higher risk tolerance in physical domains.

• Intuitives are more likely to skip steps in processes if they view them as obvious or trivial5, while sensors are likely to do things by the book.

• Sensors are naturally observant and attentive to details, intuitives are only observant and attentive insomuch as they relate to their ideas and inspirations.

• Sensors have more consistent and diligent work habits, intutives are more likely to work in bursts.

• Sensors often engage in frivolous behaviour if not balanced by their judging processes, intuitives struggle with being fickle and impersistent if they do not properly judge the world.

• Sensors are more likely to be religious.

Statistically, the elephant in the room with intuition vs sensing is IQ — intuitives score about seven IQ points higher than sensors6. The vast majority of people in the top 1% of cognitive ability are intuitives.

People have various theories as to why this is. Myers thinks that a big problem is test bias, where IQ tests reward people who are willing to make far-reaching, abstract judgements from a limited set of information. And that’s the bread and butter of intuitives. She highlights some examples of sensors who scored higher on tests by rushing through tests faster and with more confidence in Gifts Differing, which seem genuine, but I wouldn’t put much more stock in them beyond the average anecdote.

I think this is probably true to some extent, but the problem I see is that people need to make abstract judgements in real life too, so it’s not a test-specific skill. A lot of IQ subtests also do not involve abstract reasoning, they involve doing a bunch of simple things really quickly (like the symbol search subtest in the WAIS), or remembering a set of digits. These involve no abstraction at all; I would be interested to see if intuitives are still advantaged on these subtests as well.

What I actually believe in terms of the IQ difference is that it is functional pleiotropy. The more intelligent the intelligent person, the easier it is to judge farther reaching and complex intuitive leaps. The ease of the process creates the preference, not the other way around. That said, there is nothing wrong with a preference for sensing — failing at the intuitive level is much, much easier than failing at the sensory level.

Thinking vs feeling

This is a visible dimension in terms of it being easy to intuit and it being frequently discussed. Definitionally, thinking concerns itself with relating things to other things, and feeling concerns itself with what things are valued. In terms of tendencies:

• Thinkers have more unconscious feelings, feelers have more unconscious thoughts.

• Thinkers have lower levels of agreeableness than feelers.

• Feelers have better social skills than thinkers, particularly when it comes to understanding social norms and other people’s emotions.

• Contrary to popular belief, thinkers do not have higher levels of cognitive ability than feelers. If anything, outlier high IQ people are slightly shifted towards feeling.

• Feelers are more likely to be religious.

• Thinkers are more likely to struggle with human relationships, less with jobs.

This difference is pretty straightforward. The only thing I need to elaborate on is the “decoupling” idea, that some people can separate truth and value — and that perhaps this is consistent with a preference for thinking over feeling. I reject this. Facts and values are conceptually distinct, but ultimately part of the same system. Focusing on the facts causes the valuation process to retreat to the unconscious, focusing on values forces thoughts into the unconscious.

Judging vs perceiving

People often confuse this with the “conscientiousness” facet of the big five. It’s not the same thing. Judgers prefer structure while conscientious people are better at executing it. Concretely speaking: judgers prefer closure and external structure; perceivers prefer internal structure and the ability to keep options open. In terms of tendencies:

• Judgers are more opinionated than perceivers, who have either absent, developing, or uncertain opinions.

• Judgers often decide on a best way of doing something and always do it that way, while perceivers do whatever feels natural.

• Judgers find local maxima and optimise them, perceivers try to search for the global maxima… And often fail. Sometimes, they don’t.

• Perceivers are natural deconstructers, judgers are natural builders.

• Perceivers hate missing information, judgers hate leaving it unexamined.

• Judgers are more comfortable forcing the world to comply with their standards, perceivers are better at accepting it as it is.

• Judgers with bad perception are rigid and incapable of tolerating other people’s perspectives, perceivers with bad judgement devolve into laziness

• Judgers are more likely to be religious.

I think the judging/perceiving difference is the hardest to evaluate in yourself, because there is often a gap between what you think you should do, what you naturally do, and what you do in a given moment. People who naturally prefer external structure, but aren’t good at implementing it, it might look like perceivers; their preference is judging.

Jungian functions

This is where MBTI theory starts to become much more abstract, unfalsifiable, but interesting. Beyond the four preferences, MBTI also theorises that there are eight type functions, which Myers and Briggs took from Jung. A quick rehash:

1. Extraverted sensing (Se): to experience the real world as it appears. Present-focused.

2. Introverted sensing (Si): to draw upon experiences from memory. Past-focused.

3. Extraverted intuition (Ne): to intuit various interpretations of the world as it appears and hold them as alternatives.

4. Introverted intuition (Ni): to focus on one particular interpretation of the world.

5. Extraverted thinking (Te): to judge things according to whether they function.

6. Introverted thinking (Ti): to judge things according to whether they are true.

7. Extraverted feeling (Fe): to judge things according to whether they preserve social harmony.

8. Introverted feeling (Fi): to judge things according to whether they are are subjectively valued.

Myers and Briggs theorise that a type’s dominant function is determined the following way. First, they have their irrational (S vs N) and rational (T vs F) preferences. Then, the judging vs perceiving trait (J vs P) decides whether the main rational function is internal or external. If it is perceiving, it is internal; if it is juding, it is external. The main irrational function works the opposite way: if a person is judging, then their main irrational function is internal; it they are perceiving, it is external.

Introversion vs extraversion decides which of the two main functions dominate — sorting them into a ‘dominant’ and ‘auxiliary’ function. Then, the inferior functions (the opposites of the dominant functions) balance out the dominant functions. Use of only introverted functions leads to solipsism, use of extraverted functions leads to meaningless reactivity. And it also can’t be a judging function too: all judgements and no perceptions leads to nowhere. So if the dominant function is judging, then the auxiliary function is perceiving and vice versa.

Allow me to be more concrete. Let us take my personality type (INTP7) and infer its function hierarchy. The P means that the thinking is internal, and the intuition is external. The I means that the introverted function dominantes the extraverted one. So, we have Ti > Ne. The opposite functions then have to balance out the dominant ones, so then we have Ti > Ne > Si > Fe. Ti is the dominant function, Ne is the auxiliary, Si is the teritary, and Fe is the inferior.

The dominance does not mean that the function is more developed, but in practice, it tends to happen that way — INTPs naturally prefer Ti and Ne, so they develop their execution more completely. On the other hand, Si and Fe are not as developed and focused on, so they retreat into the unconscious. As such, INTPs often struggle from repeating the same mistakes and misplacing things (Si deficits) as well as awkwardness and emotional, validation seeking outbursts (Fe deficit). That… describes me better than I would like to admit.

Essentially, in MBTI, personality development happens by accepting your preferences, and then trying to develop the execution of your inferior functions first; then awareness of your shadow functions if you can.

The logic of which function dominates does make sense and follows cleanly from basic MBTI theory, but the part about the inferior functions balancing out the dominant ones… I don’t really know if that is true.

Traditionally, it has been posited that the dominant emerged in childhood, the auxiliary emerged in teenage years, the teritary emerged in adulthood, and the inferior was almost always never fully developed. Now, people are more skeptical of this, and are not as sure about whether function maturation is the same for all people. I, personally, doubt it.

Criticisms of the MBTI

The most common criticism of the MBTI is that it is not scientific. Scientific… Meaning what? Does it have institutional approval? Is it official? Is it high status? Is it able to systematically make testable hypotheses about the physical world? The answers to the respective questions are: it depends on the insitution, kind of, not really, and yes. It is commonly believed that scientific experts reject the MBTI, when, to my knowledge, there is no survey of expert psychologists’ opinion on the model.

So yes, the MBTI is about as “scientific” as any psychological theory, like IQ or the big five. It’s not scientific in the sense that it involves a lot of abstact constructs, but if you take the constructs for granted, then testing said constructs are the same as testing any other physical thing, like the weight of a human. There is some abstraction and faith that goes into the concept of a body itself, but it is not as big of a leap.

People also criticise it for dichotomising its variables, which reduces their reliability and granularity. This critique is rather confusing, because the point of the MBTI is the dichotomisation and trying to model humans using how different preferences interact to produce different archetypes. And MBTI tests can give continuous scores as well, they just aren’t as commonly used.

People have criticised the MBTI test for being unreliable, sometimes based on a single statistic, or on anecdotes. So, to be clear, let us assume the test is reliable. Does that mean it is good? No — if the thinking/feeling test was simply a bunch of questions asking “do you like computers” with different wording, then it would be bad. And if the test was unreliable, then that wouldn’t mean that the MBTI framework itself is bad, it could just indicate the test is in need of improvement.

So, the “MBTI is unreliable” critique proves itself to be uninteresting. And for those who are interested in it, the official MBTI test’s reliability is within the standards of the statistical community, which is > .70. The dichotomisation makes the statistical unreliability more apparent.

Myers in Gifts Differing noticed issues with the reliability of the MBTI test in children and teenagers, and she attributed this to their preferences not being as defined or developed at a young age; I would also note that self-awareness and self-concept scale with age. So the actual MBTI test being unreliable might not even be an issue with the test or the framework, it could be the nature of the testing circumstance.

The main functional shortcoming of the MBTI, in my opinion, have to do with poorly modelling emotions and actions — it’s missing something along the lines of order/self-efficacy and emotional attachment/neuroticism. In terms of praxis, I also think the MBTI leans too much into institutionalism by having a “main MBTI organisation”, perhaps if there was no official MBTI, then people would be more comfortable researching the model.

Further reading

• Gifts Differing — Isabel Myers

• Psychological Types — Jung

• MBTI manual (v3) — Isabel Myers

3

Relevant meme:

There is, however, one point he made that did hold water. That, when you correlate IQ scores with other outcomes of interest that aren’t test scores, the relationships observed aren’t strong — 25% of the variance in productivity and lifelong income; less of others like divorce rates or political beliefs.

Taleb’s critics pivoted to arguing that he was using the wrong statistic (R^2), and that if he really wanted to know how much IQ matters, he should have used the correlation coefficient (r). Converting from r to R^2 is just a matter of multiplying r by itself, squaring it, so to say.

I think this is an awful argument. Let me put this mathematically: if IQ explains 25% of the variance in productivity, then 75% of the variance is explained by other factors. 75% is a lot bigger than 25%, so it would be fair to conclude that there is a lot more that goes into productivity than just IQ. It’s also literally just the same statistic, just on a different scale.

Sometimes, neither of these statistics are the correct ones to use. Let’s say you want to test how much malaria harms your health. If you were to correlate people’s malaria infection status to their health in the South Africa, the correlation would be close to zero because there’s very few people who are infected with it at a given point. But malaria absolutely does mess you up.

In psychology and statistics, there are guidelines regarding what constitutes a small, medium, or large effect size. These might be helpful for undergraduates, but the tables themselves are made up — whether an effect is “big” or “small” has to do with subjective values, not mathematics. In that regard, the R^2 vs r debate is not helpful, because the salience of a statistic cannot be determined by how big it is.

The standard deviation of income of income in the United States is about $50,000 dollars. Increasing your income by 0.2 standard deviations would be equivalent to $10,000 dollars per year. I’d venture that’s a lot. On the other hand, 0.2 standard deviations in height adds up to only 0.5 inches.

Where to go from here?

When people realise that IQ doesn’t explain that much of the observable universe statistically, this could be due to two reasons.

One of them is that intelligence cannot be measured accurately by IQ tests, and that intelligence is best defined as something else. A popular definition I’ve frequently seen is that “intelligence is the ability to get what you want out of life”. The spirit of the phrase is that your intelligence doesn't matter if it doesn't lead to any outcomes, which I agree with, but it is still false by definition. The realisation helps prevent people from coping with failure by tieing your self worth to your intelligence, but that doesn't personally resonate with me.

I think that this definition, covertly, sneaks IQ determinism back into the equation, just with the aside that it can’t be measured by a test, but with what you do and get. And those things definitely matter, I just don’t think they are that related your thought processess.

The definition of intelligence I've arrived to is the ability to infer information from information. The inference can be difficult because it is far-reaching, unintuitive, requires background knowledge, or mentally tracking lots of involved parts. IQ tests can measure how intelligent people are, because they measure the amount of knowledge they have already accumulated (vocabulary, general knowledge tests), proxies for cognitive ability (e.g. reaction time, processing speed), and making conclusions that are far-reaching and involve tracking lots of parts.

Funny that Taleb’s critique involves arguing that there is no right answer to the question ‘ 1 → 2 → 3 → X ‘ , because the pattern could be something besides 1+1+1+1 that looks like 1+1+1+1 for the first three numbers. In the real world, a lot of conclusions people have to make look like this: no 100% logically correct right answer, but something that “feels right” and works.

I’ve entertained the perspective that IQ tests do not measure intelligence before, and came to the conclusion that they do so pretty well. It’s not really possible to determine how well exactly because intelligence is an abstract, unmeasurable concept, but I think the true correlation between intelligence and IQ is something like .8.

The other reason that intelligence could not be a good statistical predictor of anything besides test scores is that it just doesn’t matter that much. It doesn’t factor in, well, everything else about a person: their personality, physical capabilities, appearance, physical health, or circumstances.

Notes:

I haven’t posted in a while, partly due to having the flu from Jan 2 - 15; and also because I have been cooking two really long pieces: an attempt to explain all personality psychology, and an attempt to build a framework that explains all differences in income between people.

Said flu also led me to reflect on the time I got typhoid fever when I was 19. I did it on my new substack publication Selective Contrarianism, where I just post whatever I think in the moment without regard to what is acceptable, what people would find interesting, or whether it makes sense.

1. Capitalism revealing our preferences.

2. Build-up effects (new hypothesis, I’ll explain this).

3. Faster iterations of news cycles, cultural cycles, and social discourse.

4. Literature becoming less relevant, both in terms of creation and readership.

5. Loss of faith in definitive and long-term vision.

When people talk about the convergent culture of monochrome and minimalism — whether in logos, cars, interiors, clothes, or designs — the blame is often levied at “algorithms” shaping our preferences and making all companies do the same thing. Ignoring definition games about what constitutes an algorithm, computational models are clearly not to blame for the convergent culture.

Take, for example, the change in car colours over time:

The change starts in 1998, before the big data revolution.

How about photos of artifacts in museums?

Constant increase in monochrome and colder colours ever since the 1800s. It seems more reasp, a type of artificial intelligence that assigns incentives and prices based on demand and conditions is a much more explanatory explanation for why culture has converged than computers.

And regardless of what mechanism drove the convergence, the drive to blame the algorithms is interesting. Essentially, what an algorithm does is that it takes a bunch of inputs and transforms them into an output. In the context of culture, this happens at various levels, but conventionally, people are talking about the taste-level. Companies release products, consumers buy them, and then companies adjust their response to the tastes of the consumers.

If so, then all that is occuring is an episode of the masses revealing their preferences. Cultural decline as perceived by an observer, not a god, or the public for that matter.

You could argue that, maybe, this is a molochian problem where the incentives at the individual level are to cater towards the average preference, but it would be better in aggregate if companies did things differently anyway. I find this doubtful, if there really was a burning desire for original television and film, then people would make it.

I think this is a fair case to be made that the problem with cars and interior design is molochian, in the sense that people want to resell their assets for high prices to people who want them — incentives encourage following safe or public preferences. I would ask why somebody wealthy enough to buy a new car and decorate their house would care about how much they sell it for; these are humans we are dealing with after all.

Architecture is a little different, Scott Alexander approached this issue and he thinks that it might have to do with the wealthy wanting to hide wealth in favour of signalling taste, and architects and designers finding the old aesthetics — symmetry, maximalism, and functionalism — too constraining. In that case, it would be appropriate to blame the tastes of the creators | elites and not the masses — most people like traditional architecture more than modern architecture.

Strangely, when we see an increase in drivers of cultural diversity — be it racial types, subcultural fragmentation, or political polarisation: we are afraid. We must manage it with bureaucracy and social fabric, or reduce it by means of limiting immigration or discourse. We don’t seem to want culture to stagnate but we also don’t want it to be diverse either.

There are people who do want different, new things, they are just in the minority, and there are not enough of them to sustain avant garde, ambitious art at scale. Especially when these people who want better things can just pivot to older content; the kind of people who want different things are also more likely to try to find it. Which leads me to my second hypothesis:

Build-up effects

This is not the same as a low hanging fruit effect. A build-up effect is an effect that arises from more fruits being picked, regardless of where they are on the tree.

The low-hanging fruit effect is a good explanation for the decline in productivity in the sciences and perhaps some constrained forms of art. I am, however, skeptical of whether this is the case for the rest of culture and particularly narrative, the form of culture I am most interested in. Most great narrative masterpieces are not low-hanging fruits1, but high-hanging ones, stories that were difficult to conceptualise and execute. Lord of the Rings, for example, took decades of worldbuilding and conception to bring to fruition.

I think that a similar, but functionally different effect is responsible for some of the slowdown in culture we seen: I call it the build-up effect.

Imagine a society with a set population of 100 million people and zero innovation, but possesses modern digital technology. 10,000 works of narrative are released every year. Every year, the number of total works ever written keeps increasing every year relative to the number of new works. Even with no technological or demographic progression, culture “stagnates” because the amount of total work the culture has produced is constantly increasing relative to new output as nothing else changes.

This also drives perception too. As time goes on, even if no sequels or remakes are published, it feels like culture is stagnating because, as time goes on, the best of it is increasingly old. In addition, more of those 10,000 works are going to be remixes, adaptations, sequels, or remakes of the old works.

The catch: this is clearly not the main driving force behind what is happening.

Take films, for example. The amount of new film has skyrocketed starting in the late 90s — from 5000 in 2000 to 17500 in 2020, and the same is true for other forms of art, like anime2 and books3 too.

It’s difficult to quantify the number of remakes and sequels that occur every year, because they need to be labelled manually, so people often evaluate this by looking at the top films by revenue. This will overestimate the number of sequels/reboots, as successful franchises disproportionately continue

Even so, this cannot be the cause of the problem. See the following chart: top grossing films went from 25% sequels in 2000 to 75% in 2020.

If we take these numbers at face value, then we have gone from ~3750 brand new films in 2000 to ~4300 in 2020 — an increase, in fact. The problem is that they are, well, bad.

Personally, the “sequelitis” critique of culture doesn’t land because a lot of my favourite media — Fate/Zero, Skyrim, and Devil May Cry 3 — are all sequels. I do concede, however, that this is behind a great share of the perception behind culture stagnating — not the essence, though.

I think the build-up effect, however, can explain artists not wanting to cater to divergent or “refined” tastes — those with these tastes can just look up the content they want to watch on the internet, and watch it themselves. I don’t believe true art competes with anything, because nothing can replace it, but the shift in incentives remains nonetheless.

Faster cultural iterations

Essentially, a cultural iteration is:

Ideas (e.g. mythology, political philosophy) → content (e.g. films, games) →discourse (e.g. tweets, books)→more ideas→more content

In the past, these things occurred slowly, because distribution channels took their time, attention spans were better, and the content was longer.

Now, it is extremely fast.

This speed makes discourse feel meaningful, impactful, or cool. But said speed actually makes the iterations less impactful: stable changes in conception and belief come from periods of reflection, not an attention-shifting state. I am not blaming only trashy short-form content, but also the speed up in the pace of all content, be it film, television, or books.

These fast news and event cycles give people the illusion of a rapidly changing, impactful discourse, but none of the substance: when one side starts losing face or composure, it disengages and diverts attention; the winning side claims victory, only for the “victorious” argument to be forgotten in mere weeks. The cancelled not only stay cancelled, but irrelevant too. Anti-capitalist films and television like Parasite or Squid Game go viral, disappear, only to be replaced by the enduring soul of utilitarian neoliberalism.

Growing irrelevance of literature

This was the topic of the first post on cultural decline:

I still consider the thesis defensible. I think I was too eager to claim that the best authors would turn away from writing — it’s still materially easy to do, and I think the very best, the transformational talents, are probably still there; perhaps just writing stories in different mediums.

The current mediums: video games, films, and television series, are not as suited to the creation of original and high quality content. These technological mediums serve best as vectors of transmission, not creation. Cultural changes observed elsewhere (e.g. logos, cars) cannot be explained with any of the causes I posited, besides perhaps broad technological stagnation.

Loss of faith in definition

If you look at the greatest of our species — be they artists, statesmen, philosophers, entrepreneurs, scientists — they often have different personalities or backgrounds, but there are two traits that recur:

1. High intelligence.

2. The ability to commit to a definitive, grand, long-term vision, even in the absence of financial or social rewards.

When people speak of the decline of great genuises, I think that some of it is just an artefact (might talk about this later), but I think some of it is a culture that encourages spontaneity, novelty, and “keeping options open” over planning, hard work, and delayed gratification. The former gives us consultants, financiers, polymaths, and generalists; the latter gets us to the moon.

This is a softer and less empirical explanation, but it nonetheless feels real to me. Thiel touches on this issue in Zero to One, the best book I have read to date.

The false explanations

I avoid putting people on blast. I will abide by this standard here, though it will lead to an inevitable loss in context, so I will link the posts but not mention names.

Our first theory posits that there is a general loss in divergence in human behaviour, and that this is causing the great cultural convergence: the monochrome cars, minimalist logos, and athleisure that have been panned to death, but also the lower drug use in the youth, crime reductions, and fertility declines.

The author hypothesises that this decline is driven by longer and more predictable lifespans, which encourage more risk averse behaviour and slow life strategies — less drugs, sex; more studying and reputation-guarding. Once people start doing that, their life experiences homogenise, lives narrow, and worldviews converge. This convergence in experience causes a convergence in artforms, and institutions enable this by penalising failure and documenting reputations.

I object to this analysis on almost every single level.

Regarding the trends in the youth, the elephant in the room is the decline in face-to-face social interaction. I ask, what kind of person spends all their time drinking alone? Smoking alone? A depressed, executively dysfunctional person. The fact that the youth of the day are turning away from the drugs and the crime is not a testament to their weakness, but to their strength and isolation. There is also statistical evidence4 that these trends we see in aggressive behaviour such as unprotected sex, drug use, and crime internationally are driven by declines in social interaction: not phones, the internet, or parents being too vigilant.

The same kinds of people who engage in petty risks — unprotected sex, violence, drug use, or crime — are actually probably less likely to create great art, which requires delaying gratification. Dysfunctional risk-takers are less risk averse, but they are also less intelligent and worse planners. It also doesn’t factor in that the kind of people who take concrete risks (MBTI sensors) are not the same type of people who take abstract risks (MBTI intuitives).

Also, just take a look at twitter, or any internet culture for that matter. Are we really getting less weird, or is people’s weirdness just getting expressed in different ways?

I also object to the cause. Longer lives and less day-to-day death risks encourage behaviours with longer time horizons — which encourage great art.

There is also another theory which states there is no cultural decline, and all of the innovation is hidden in new art forms: tiktoks, tweets, or memes. Now this is a cope.

Many of these advocates of internet culture (not the author, to be fair) never stop to tell us what about it they enjoy; I have no such reticence: I like the naruto AI parody where Naruto’s body is inhabited by an African man, the Skyrim AI parodies of American culture, RWBY fanfiction, and the Sonic fandubs with Alfred in them.

These people never do this because the new culture is, simply and obviously, worse than the old one. It’s not even trying to do the same thing, so maybe it doesn’t even make sense to say it is worse, but it doesn’t function as a replacement. Some of it might be funny or entertaining, but it’s not a substitution for masterpieces like Chinatown, Twin Peaks, or Ghost in the Shell. These creators of the new culture, no matter their popularity, either envy or admire the great artists.

There is this cope that “everybody wants to be a poaster”, that discourse matters or whatever, but real culture comes in serious art forms: anime, video games, film, and books. Any 200k+ twitter or tiktok account would 100% prefer to be making transcendent art or building monopolies than doing whatever they are creating at the moment. The issue is that these online platforms attract a certain neurotype, typically high in insecurity, drive, and disorganisation, and low in executive function and maturity; selecting them into social media and away from more wholesome forms of creation.

A third theorist posts that the world has been ruined by “dopamine seeking” devices which distract us, pacify us, and do a bunch of bad things. Zuckerberg, Musk, and the elites are to blame for all of this. No really, that’s what he thinks:

Do you remember rule two above? It said: Look to the teens, and their digital lives.

When you do that, you see immediately that they are the main victims here. This flattened culture is all they have ever known. It’s now the landscape of their inner lives.

Many of these youngsters lack the skills and tools required to escape. So this flattened world is really their prison—and the billionaire wardens (Mr. Z and Mr. M and all the rest), who get rich from their brokenness, want them held in their digital chains forever.

Now, I am not exactly the biggest fan of short form content. But I think this is the wrong line of attack — who, may I ask, is using phones to look at all this garbage?

Technology might have given us more distractions and meaningless fluff, but earlier generations had these things too, just in forms that had other ends and were delivered in a more palatable form: social interaction, exercise, collecting, television, and having sex with prostitutes. People starting projects and losing focus, lacking discipline, or the ability to delay gratification is a perennial problem.

Technology also gave us more opportunities and tools: easily pirated digital workstations, digital art software, and free platforms. Humankind, instead of being driven to creation, has decided to make cheap, stupid content or sterilise itself with it. The “intellectuals” ignore this obvious fact and divert the bad conscience to technology — the best thing humanity creates.

I will, say, on a positive note, that it seems like the mediums that are stagnating the least (video games, music) are also the ones where people have the easiest time making things on their own without the help of mass audiences, marketing, or institutions. Within music, I see many emerging styles like witch house, emo rap, lofi, or Phonk, they do not tend to hit the top of the charts, but many people still listen top them nonetheless. In that regard, it seems that our artists are doing relatively fine, it is the masses and elites that are flaundering.

Previously, I was concerned that perhaps the problem was that we are getting more content, and it has become harder for the collective intelligence to sort between all of it. Even small changes in the correlation between popularity and quality could change the quality of what rises to the top.

I no longer think this is the case. Strictly speaking, there are two barriers to the creation of culture:

1. The number of people who engage with a form of culture.

2. The ease of creating said culture.

Lots of people watch anime, but only several hundred are created per year, because creating anime requires a studio and capital. Novels aren’t read that often, but are frequently written because it is far easier to write a novel than film a movie.

The raw quantity of film and anime has increased by an order of magnitude in the last 20 years, but 17500 films have been released in 2020 and 300 anime were shipped out (not aired) that year. Those numbers also differ greatly, but I feel like the “rise to the top” effect is pretty similar in both mediums and isn’t affected that much by the raw amount of content. More coal doesn’t hide the gems if they are shiny enough.

People have also highlighted that selection in the past was different: it was institutions that chose whether a book was published or a film was released. Now, computers + social media make distribution of content much easier, which lowers the quality of content by default. This is probably true, but popularity has always been directed by word of mouth, which is deterministic at a high enough scale.

So

Finding good culture is easy. Find a computer, get on the internet, and search for it.

If so, then why does this even matter? To some people, this matters because culture matters to them beyond consumption; I can sympathise with this motivation. Alternatively, one could read discourse regarding cultural stagnation as performative, engaged in to signal “divergent” or “higher” tastes that stand above the mass culture, that you are keyed in to the latest trends and discussions on the internet.

I think the answers that some of these intellectuals give is rather telling. That the problem is the “elites”, that modern life makes us pod people that don’t want to take risks, and that that cheap husk of a culture we call “memes” is a replacement for high art. We would love to blame anybody but us, the people with the right tastes and preferences. Or maybe the problem is that I am not trying hard enough to find the right cultural critics.

In social environments, exposure to mass culture is inevitable. And here’s the thing: the point of these environments is the people, not the culture. I don’t think whether Golden by KPOP Demon Hunters being a good song changes whether I enjoy dancing to it with my 6 and 8 year old siblings.

2

Anime:

3

Books:

4

Let me look at the main source that suggests the decline is due to face to face socialising. Their regression table is pretty unambigious, extremely low p-value:

For the individual-level effects, it’s also pretty unambigious:

The record also does not suggest they are electronically driven either. See Table 1:

Some of it is also just later initiation ages, which goes against the author’s hypothesis:

1. Richard Hanania explains the based ritual: any objection to right wing opinions has to be due to being too right wing. “Repealing the 19th is bad because nobody should vote anyway” or “hating minorities too much is bad because that diverts hate away from women”.

2. Anonymous woman describes her dating experience in the bay area on Scott Alexander’s blog, and describes a few types: “the man who is not”, “the man who is”, “the founder”, among others. A little fluffy (to be expected), but conceptually dense, raw, and original.

Read more

The theory

Empirically, self-reported reading for pleasure has declined in both adults and teenagers over the last 50 years. People also rarely finish the books they read, which can be inferred from the fact people tend to be much more likely to quote passages from the beginning of a book. Using this data, it’s possible to estimate the percentage of people who actually finish books:

If less people are reading, that means that less people are writing, not just due to the lower demand, but also the fact that the skillsets overlap. More importantly, the demographic of individuals who tend to write the most books that become classics — White men — have vanished from major writing awards:

It’s easy enough to trace the decline of young white men in American letters—just browse The New York Times’s “Notable Fiction” list. In 2012 the Times included seven white American men under the age of 43 (the cut-off for a millennial today); in 2013 there were six, in 2014 there were six.

And then the doors shut.

By 2021, there was not one white male millennial on the “Notable Fiction” list. There were none again in 2022, and just one apiece in 2023 and 2024 (since 2021, just 2 of 72 millennials featured were white American men). There were no white male millennials featured in Vulture’s 2024 year-end fiction list, none in Vanity Fair’s, none in The Atlantic’s. Esquire, a magazine ostensibly geared towards male millennials, has featured 53 millennial fiction writers on its year-end book lists since 2020. Only one was a white American man.

This is also true for writing in American television:

…And the film industry:

Some of this is just politics, but I think a lot of it is changes in incentives. The kind of introverted, smart, artistic people (regardless of gender or race) who would have written stuff like A Song of Ice and Fire are not writing anymore. I think most of them went into tech (particularly AI, software, or game development), finance, influencing, or ghostwriting — fields that offer more lucrative opportunities, regardless of how glamorous they are. This talent reallocation has been going on for a long time, maybe as early as the late 18th Century, but it’s intensified with the development of television and the internet.

The kind of talent that makes somebody a good writer does overlap with the talent that makes them a good stock trader or coder. Far from perfectly, but well enough to the point that most people who could write a good novel would also likely be a passable white collar employee. I think that Tolkien-tier talents probably would be doing something creative regardless of their situation, but the same is not true for people who are a tier below him.

It’s also far more difficult to get discovered as a new writer now: people are writing more books, people are reading them less, and the low hanging fruits have been picked. There has been an (at least) 23x increase in books published from 1940 to 2025:

Essentially, even if the talent that makes great fiction was actually writing novels, we can’t discover which ones they are if nobody reads them, and espeically when there are so many of them. Novels also allow for more thematically complex, coherent, and long stories that are more able to exemplify what good narrative is.

I personally might not like the novel as a form of art, but it allows a single artistic mind to enforce their will onto the world. All great vision, all great stories — come from great and industrious geniuses, not institutions or groups. I suspect that part of it is due to the fact creative talent has a right tailed distribution: someone like Aldous Huxley is 10x more generative than the typical 99th percentile creative talent, but also because of the nature of thinking — it comes from one mind.

Because of the monocausal nature of creativity, top creative talent is less likely to find working at an institution attractive. It’s also true that, regardless of field, the best tend to go do their own thing anyway; there is nobody to slow them down or take the surplus of their labour. As such, the top creative talent that was previously writing novels or short stories is unlikely to have gone into the film industry. Can you imagine Franz Kafka working in Hollywood?

You do see good directing talent come out of the film industry — Lynch, Polanski, and Nolan for example. Regardless of their talents and responsibilities, they are unable to generate the same kind of lasting and unique stories that novelists like Tolkien write. They are subservient to their sources of capital, and contrained by their writers, special effects teams, and actors.

If there is less good literature, then that decline inevitably flows from books to film and television, because films and television rely on it for inspiration. A bunch of B tier talent with no drive and overarching vision are not going to make anything interesting. There are a few exceptions of high quality original television, like Breaking Bad1, but the rule remains. Literature also influences film and television beyond mere adaptation.

Culture didn’t tank as soon as people started reading less — the effect is lagged. Technology first made culture better by giving us superior methods of distributing narratives (films and television, based on people’s revealed preferences). As they improved they incentivised the creation of newer, bolder, and better content — CGI gave us Pirates of the Caribbean, technicolour gave us Robin Hood, and multi-camera setups gave us I Love Lucy.

Now that technological innovation is slowing down, there are less incentives to create new and impressive things in film. There is also less great literature to adapt into film, as the classics have gotten adapted already and nobody is writing anything to match them. Eventually, businesses start opting for safer ventures like sequels or remakes due to financial incentives and a lack of good content to test for adaptation. Graphically speaking:

Sequelitis is also massively overstated as a problem, and is better understood as a symptom instead of a cause of decline. Many game sequels, like Half Life 2, Skyrim, Devil May Cry 3, and Vampire the Masquerade were more popular and higher quality than their predecessors. I think that people like to latch on to the sequel problem because it is a legible proxy for cultural decline, which is something that people feel deep in their bones, but find difficult to justify with words or numbers.

If we are talking about television in particular, I think that streaming also contributed to the decline. Platforms like Netflix give people the opportunity to watch anything whenever they want, so television series have to try harder to catch people’s attention. Anecdotally, I’ve noticed that the pace of TV, both Japanese and Western, seems to have drastically increased after 2012, which removes all emotional impact.

Streaming also gutted mid budget films, because the industry could still expect to make some money off of DVD sales if things didn’t go well in theatres. This leaves only indie low budget films or Hollywood blockbusters; very little of the former is any good, and the latter must devalue themselves and appeal to the lowest common denominator to get any profit.

So, that’s it. Those are the causes of cultural decline in narrative-based art forms.

I have conflicting thoughts on the direction of other art. Fashion has gotten worse, mostly because people are too lazy to put any effort into their clothing. I think music has gotten better, if you ignore what rises to the top — electronic music is a richer form of expression than anything that came before it. Multiplayer games have gotten more balanced over time, but most of them are awful anyway. Drawing has improved over time due to algorithmic feeds and software.

Narrative-based art clearly stands out as getting far worse than other forms of art, which are either getting better or worse for their own idiosyncratic reasons. This, in my opinion, shows that narrative-based art must be declining for reasons beyond mere cultural changes, which should theoretially have a global infuence on all art.

Regarding the algorithms: around the 2000s, new data analytic software and compute made it easy for corporations to statistically analyse the predictors of best-selling media, and then try to fit new content to the data. Because algorithms can only adjust to existing preferences, some argue that they promote cultural stagnation.

I find this narrative unconvincing. Algorithmic recommendations could result in people watching more of what they already like and alter the natural ‘rise to the top’ that occurs with good new content, but this would only decrease the correlation between what is popular and what is good. The problem I see is not so much that good content isn’t rising to the top of the popularity hierarchy, it is just straight up not being made.

If I look at the medium I know best, anime, there is not much algorithmic curation because of widespread piracy and there being (relatively) little new content. Most anime are derived from light novels and manga, which are also not subject to artificial curation.

Anime, like other forms of narrative, started to fall off somewhere between the late 00s and early 10s. If you tally down all of the good anime from each period, the decline seems pretty clear:

• 1988-1999: Evangelion, Ghost in the Shell, Princess Mononoke, Perfect Blue, Cowboy Bebop, Serial Experiments Lain, Berserk, Great Teacher Onizuka, One Piece, Akira, Hunter x Hunter, Vampire Hunter D, Legend of the Galactic Heroes, Blue Gender

• 2000-2012: Death Note, Fullmetal Alchemist, Steins;Gate, Spirited Away, Satoshi Kon’s films, Shinsekai Yori, Code Geass, Welcome to the NHK!, Tatami Galaxy, Psycho Pass Fate/Zero, Texhnolyze Bakemonogatari, Kaiba, Nana, Attack on Titan, Ergo Proxy, Samurai Champloo, Howl’s Moving Castle, Ouran High School Host Club, Haruhi Suzumiya, Darker than Black, FLCL, Baccano!, Penguindrum, Monster, Ghost in the Shell: Stand Alone Complex, Gurren Lagann, Soul Eater, Fate/Stay Night, GITS Innocence, Avatar the Last Airbender

• 2013-2025: Mushoku Tensei, Made in Abyss, A Silent Voice, Re:Zero, Your Name, Aggretsuko, My Dress up Darling, Spy X Family, RWBY….

And that’s basically it. I’ve heard Frieren is good but I haven’t gotten into it yet.

I also play a lot of narrative-based games, and I think the decline has started to set in for games too, despite sales largely being a function of word of mouth. The decline is not as stark as it is for other mediums of storytelling, there are definitely some good post-2013 releases like Undertale, Disco Elysium, TCOAAL, and Nier.

Some argue that this decline is due to cultural forces, usually postmodernism or wokeism, constraining the development of art. I couldn’t disagree more, the most popular and impressive artists find ways to dance around social conventions regardless of time. In fact, I think that artists get punished more for being too acquiescent to politics — I count many more instances of studios getting rejected by audiences for being too woke than not being woke enough. Political and religious constraints on art are nothing new.

The timing of this decline in narrative also concords with the advent of the mass internet and smartphones. I, however, think it’s unlikely phones actually caused this. Within the West, games/film/TV have always been the entertainment of the lower classes. The mass internet and piracy expanded this audience internationally, but I doubt that writers and producers are interested in appealing to 14 year old Indonesians who pirate films on their $100 android phones.

I should also restate my view that shifts in average IQ in the last century do not explain… Anything at all. Evidence of people getting dumber or smarter in our times is generally not that good, and it doesn’t survive sophisticated corrections for measurement invariance. Uncorrelated has a nice projection of the world IQ over time:

Cultural progress is not an intrinsic fact of the world. For about 1,500 years, Europe has been telling itself the same stories, worshipping the same religions, and making the same art. Then everything changed when the printing press, camera, and computer attacked. I’d also venture that increases in global intelligence and population enabled the creation of surplus and novel culture. In that respect, it would be better to see cultural decline as a regression to the mean and not a historical contingency.

• It’s not illegal in the United States.

• IQ explains 25% of variance in job performance within occupations, more so for complex and technical professions. It beats out every single predictor of job performance typically used by firms for selection, besides work samples.

• A firm that selects software engineers with >115 IQs would see a 10-30% productivity boost, which would outweight the expenses that come from selecting smarter employees.

• It’s unpopular primarily due to social and practical reasons, not legal reasons.

Legality

First of all, it’s not illegal to do this in the United States. There was a supreme court case (Griggs vs Duke Power), concerning a firm that used IQ tests as a means to discriminate against black employees1. The court ruled that businesses must show that any test used for selection, if it produces ethnic group differences, is “reasonably related to the job.

This is not equivalent to making such testing illegal, especially given the empirical literature showing that more intelligence employees tend to have higher productivity. Furthermore, in a recent case (Ricci vs DeStefano, 2009), the supreme court ruled in favour of 20 firefighters who were selected for management roles based on test scores, but were rejected at the last minute due to concerns over disparate impact.

The combination of these two rulings does make it legally difficult. Firms who select for employees on the basis of IQ test scores, which contain racial differences, open themselves up to litigation if they can’t prove that said tests are relevant to the occupation2. If they discard said scores on the basis of their racial differences, they open themselves to a lawsuit which they would likely lose.

The correlation between IQ and job performance

The best meta-analysis on the relationship between IQ and job performance, conducted by Hunter & Hunter, finds a mean correlation of 0.53. This means that 25% of the variation in job performance can be explained by IQ scores.

This value has been debated in the literature for mainly two reasons3:

1. The value is adjusted for supervisor ratings.

2. They assumed that job applicants and the general US population had the same variance in intelligence4.

Controlling for unreliability in supervisor ratings is sensible, as supervisors evaluate employees in different ways and lack omniscience. In fact, the original Hunter & Hunter meta-analysis underestimated the degree to which supervisor ratings are unreliable — they assumed a reliability of 0.6, when in reality it’s closer to 0.52.

Regarding restriction of range — the idea is that if you calculate a correlation within a sample that is restricted, the relationship will be attenuated in strength:

Critics argued that because job applicants already show reduced IQ variance, correcting for this may exaggerate the incremental value of cognitive tests over existing hiring filters. This is valid as a contextual point, but it does not undermine the interpretation of the corrected validity itself.

The other issue is that H&H assumed that job applicants and the American workforce population were equal5 in terms of variance in intelligence when they corrected for restriction of range. While this asumption sounds ludicrous, it doesn’t turn out to be that big of a problem. Empirically, applicant pools for specific jobs vary only slightly less than the broader applicant pool:

• Sackett, P. R., & Ostgaard, D. J. (1994). Job-specific applicant pools and national norms for cognitive ability tests: Implications for range restriction corrections in validation research. Journal of Applied Psychology, 79(5), 680–684. doi: 10.1037/0021-9010.79.5.680

Correcting validity coefficients for range restriction requires the applicant pool predictor standard deviation (SD). As this is frequently unknown, some researchers use national norm SDs as estimates of the applicant pool SD. To test the proposition that job-specific applicant pools are markedly more homogeneous than broad samples of applicants for many jobs, job-specific applicant pool SDs on the Wonderlic Personnel Test for 80 jobs were compared with a large multijob applicant sample. For jobs at other than the lowest level of complexity, job-specific applicant pool SDs average 10% lower than the broad norm group SDs. Ninety percent of the job-specific applicant pool SDs lie within 20% of the norm group SD, suggesting that reducing a norm group SD by 20% provides a conservative estimate of the applicant pool SD for use in range restriction corrections for other than lowcomplexity jobs.

Given that H&H underestimated the error in supervisor ratings, and overestimated the degree of restriction of range, I calculated a correlation that adjusted for both of these biases6, which turned out to be… 0.52. Turns out the biases cancel out, as they go in different directions. For the sake of transparency, I also posted what each correction does to the effect size:

Hunter & Hunter use supervisor ratings as a proxy for productivity. Presumably, there is some bias that results from this, independent of the statistical error that comes from supervisors giving inaccurate ratings. In a followup paper, Hunter did a path analysis where the effect of cognitive ability on job performance was estimated using work sample tests instead of supervisor ratings; the overall effect was more or less the same (0.54 according to this chart):

The effect of IQ on performance also varies by occupation, where the most complex jobs have the strongest associations (r = ~0.6), while less complex ones have weaker ones (r = ~0.4). In manual labour jobs, psychomotor ability and dexterity are better predictors of performance than cognitive ability.

IQ vs other selection methods

The correlation between job performance and IQ might only be modest, but it should be noted that cognitive ability beats every alternative predictor of job performance except for work samples (r = 0.54).

In most of these cases, it’s not possible to correct for restriction of range, as the distribution of these variables within the reference population is not known. To make the apples to apples comparison, H&H note that the validity of interviews and experience would still be far below that of cognitive ability, even if their validity was corrected for restriction of range.

The key finding for the interview is a mean validity of .14 (in predicting supervisor ratings of performance, corrected for error of measurement). If the restriction in range is as high as that for cognitive ability, then the true mean validity of the interview is .21.

The observed average correlation for ratings of training and experience is .13. If the extent of restriction in range matches that for cognitive ability, then the correct correlation would be .19.

No correction for restriction in range was needed for biographical inventories because it was always clear from context whether the study was reporting tryout or follow-up validity, and follow-up correlations were not used in the meta-analyses.

How effective is test-based selection?

I’m not a fan of the academic literature on productivity, particularly the 10x literature which argues that there are massive differences between the best and worst employees. It is often unclear whether these “10x” estimates refer to the actual difference between the most and least productive workers in a sample, or merely the ratio between the tails (e.g., top 1% vs. bottom 1%). If it’s the former, then the difference will be imprecisely estimated because of sample size variance.

It’s also not clear what constitutes an objective measurement of job performance. Some software engineers might write more lines of code but at a lower quality. This also applies to art; popular films aren’t necessarily good ones.

That said, the idea that the difference between the best and worst employees in complex occupations is an order of magnitude (10x) seems intuitive to me. So much, that I won’t bother trying to justify it with academic literature.

I will be assuming from the get-go that the difference in productivity between the top 1% and bottom 1% within software engineers (just an example) is anywhere from 5x to 25x. I will be using the correlation between job performance and IQ that is adjusted for the reliability of supervisor ratings and restriction in range relative to job applicants. Presumably, the correlation between IQ and job performance could vary anywhere from 0.4 to 0.6 due to the typical heterogeneity and error that occurs in meta-analyses, so said uncertainty will be integrated into the model as well.

I chose a right-tailed gaussian distribution for productivity:

The base case is that the employees are just typical software engineers (SWE). According to Wolfram’s 2023 paper, the average IQ of a software developer is 111. Based on a table in this paper, the standard deviation of IQ within complex professions at the ~111 IQ level is about 12SD, so I will use that as an estimate for the variance in IQ within SWEs. Given that so many people apply for SWE jobs, I think it is plausible that a firm could set a minimum requirement of an IQ of 115 to become a SWE. Without sacrificing for other targets they use for job performance, that is.

Under these parameters, I estimated the percentage improvement in productivity that would result from selecting only SWEs with IQs above 1157, in comparison to normal hiring methods (which lead to SWEs with average IQs of 111). Based on the simulation, selecting for SWEs with IQs above 115 would result in an increase in productivity of 10-30%8.

Which might not sound that large, but for a company as large as Telsa, that would translate to a gain of $1-3 billion in terms of net income every year. Given the smarter employees would be slightly more expensive, and that most occupations lower variance in terms of productivity, I’d wager that the real gain could be anywhere from $100M to $1B per year.

The math at the aggregate level is also supportive of the idea that selection for IQ works. The correlation between IQ and job performance (r = 0.52) is higher than the correlation between the correlation between IQ and income (r = 0.35), especially when you consider that the correlation between IQ and income will be weaker within people who share the same job. There is also more variance in productivity than income, so even if the correlations were the same, IQ selection would still be efficient.

It’s not really possible to estimate the productivity gain if every firm selected employees based on IQ, as talent allocation is zero sum. Presumably, the filtering would result in the higher IQ workers being fed into occupations with larger variance in productivity, which would yield a net economic benefit, though I am not sure how large said benefit would be in practice.

All of these statistics assume that the test involved is a highly reliable test like the GATB or WAIS; in practice a lot of firms that engage in employment testing often have tests that are too short or incomplete, which will diminish the efficacy of testing. Matrix reasoning tests, for example, only correlate with full scale IQ at 0.7. There’s a tradeoff between test length and reliability here; the best tests will be as short as possible and as reliable as possible. For this purpose, I recommend crystalised ability measures like general knowledge, mathematics, and verbal ability.

In real terms

On an emergent level, employees primarily possess four qualities:

1. Cognitive talent: ability to extract information from information, solve problems, and make intuitive judgements. Some talent is also task-specific — some people are good writers but awful mathematicians. IQ will measure most cognitive talent, but not all of it.

2. Energy: ambition, passion, physiological health, and activity. It doesn’t come from personality or even incentives, but a kind of irrational drive that comes from the pursuit of mastering the thing in itself.

3. Character: egolessness, emotional stability, work ethic, and sensitivity to the thoughts and feelings of others. This is analogous to the psychological concept of stability, where there are strong positive intercorrelations between emotional stability, agreeableness, and conscientiousness.

4. Polish: appearance, charisma, and ability/willingness to manipulate situations and perceptions to their advantage. People with polish can pretend to have good character and to a lesser extent talent/energy.

In my opinion, talent > energy > character > polish in terms of productivity, though this varies substantially by occupation. Polish is essential for professions centered around persuasion or performance (e.g., acting), but far less for technical ones (e.g., academics).

I think a lot of the appeal of IQ testing online comes from the fact that it selects people based on their talents and not polish. The problem is that said selection ignores character and energy, leading to the hiring of potentially egoistic or lazy employees who could be hiding their shortcomings with a layer of polish.

One could argue that, in a corporate environment, those kinds of things shouldn’t matter. But evidently they do. In fact, the kind of people who disregard the feelings or thoughts of others when acting or speaking would recoil at the idea of being treated the same by their peers. Tactlessness usually reflects a lack of character and self-awareness, not polish.

References can’t unveil this coat of polish because the incentives for employers to give other employers accurate references don’t exist. If anything, they are perverse: employers might readily recommend employees with poor character or talent to get them off their plate. It’s an unfixable problem; personality tests are too unreliable and easy too game. Ideally workers would be vetted for IQ first, and then when they have the opportunity to work at the firm, they are promoted based on their productivity; this would still gate untalented but potentially productive employees from the system.

Why don’t firms use IQ testing?

People often ask why intelligence testing isn’t popular in firms if it’s supposedly so effective; there is no one answer to this question. It’s often raised in the lens of the “free market”, that perhaps the fact that IQ testing is unpopular demonstrates that it doesn’t work.

There are some occupations where it simply doesn’t make sense — jobs with low demand, low variance in productivity between workers (e.g. assemblyline workers), and where skill is highly legible (e.g. artists, musicians) do not benefit from cognitive testing. Because hiring methods are mimetic, it’s inevitable that managers who would benefit from selecting from IQ might copy the hiring methods of other managers who do not need to do so.

“IQ” is a socially controversial concept, some people believe in it, others don’t, so it’s inevitable that some businesses will not use it even if they are incentivised to.

Beyond being controverisal, IQ tests are also gauche; stigmatised on grounds of being racist, classist, ageist, and nerdy. They assign people a single number that is assumed to measure intelligence, something that is considered to be innate and also valuable, a dynamic which creates resentment and anxiety.

Some people don’t like it when status-associated traits are explicitly measured out in the open. Some of this is just egalitarian drives, but it also has to do with preferences for implicit or ambiguous hierarchies which allow people to preserve their egos.

Interviews, references, and holistic impressions might be less effective at scale, but they also give evaluators a pretense of agency or mastery when selecting employees. IQ tests are cold and lazy.

The effect of selecting employees for IQ presumably varies by company. I doubt Microsoft, for example, would see large gains from test score selection, as most of their value comes from a product that they’ve already built; though I would assume that if Microsoft was more intelligent they would make decisions that result in them maintaining their market share for a longer period of time. Startups would probably see the greatest success from filtering hires based on intelligence, as their value comes from products they need to create in the future.

Disparate impact

By demographic group: men and women have roughly the same IQ scores, and the elderly perform worse than the young, though much better than people would expect. Some ethnic minorities such as blacks and hispanics perform worse on IQ tests in comparison to whites.

Independent of the causes of these associations, IQ tests typically show little evidence racial or sexual bias at the aggregate level — See Jensen 1980 for an extended review of this topic. In summary, bias in intelligence testing is assessed by regressing an outcome variable of interest (e.g. training success, income) onto IQ within two groups. Bias can be assessed in terms of intercept and slope: if one group has consistently better performance in comparison to what is expected based on their IQ, then the test is likely biased against them9.

Example of a specific test of this hypothesis (though not an explicit one):

I should also mention that it’s the majority opinion within intelligence research that IQ tests are not racially biased10.

So

If you feel like you need to select your employees on the basis of IQ, you probably do. The since the goals and situations of employers will differ. If the occupation is one where work samples or job knowledge tests are easy to administer, reliable, and effective, I would advise for using those regardless of situation; otherwise, IQ tests should do just fine.

R code:
#going through every possibility from 0 to 0.53
possible_range <- seq(from=0, to=0.53, by=0.005)
error <- rep(0, length(possible_range))
iteration_product <- data.frame(i_rxy = possible_range, error_term = error)
sd_ratio = 0.67
assumed_unrestricted_sd = 15
assumed_restricted_sd = assumed_unrestricted_sd*sd_ratio
corrected_rxy = 0.53

#R = (rS/s)/ sqrt(1-r^2 + r^2(S^2/s^2)).
#r = R * sqrt(1-r^2 + r^2(S^2/s^2)) * s/S

for(i in 1:length(possible_range)) {
  inferred_rxy_value = iteration_product$i_rxy[i]
  #rearranged correction formula
  
  inferred_rxy_formula <- corrected_rxy * sqrt(1-inferred_rxy_value^2 + inferred_rxy_value^2*(assumed_unrestricted_sd^2/assumed_restricted_sd^2)) * assumed_restricted_sd/assumed_unrestricted_sd
  
  iteration_product$error_term[i] <- (inferred_rxy_formula-inferred_rxy_value)^2
}
iteration_product
#minimised at 0.385
uncorrected_r <- iteration_product$i_rxy[iteration_product$error_term==min(iteration_product$error_term)]

#going from 0.6 to 0.52 supervisor rating rxx
corrected_for_supervisor <- uncorrected_r*sqrt(0.6)/sqrt(0.53)
corrected_for_supervisor
#10% lower than 15 = 13.5

assumed_unrestricted_sd_true = 13.5
assumed_restricted_sd_true = assumed_restricted_sd
true_ratio <- assumed_restricted_sd_true/assumed_unrestricted_sd_true

true_corrected_rxy <- psych::rangeCorrection(r = corrected_for_supervisor, sdu = assumed_unrestricted_sd_true, sdr = assumed_restricted_sd_true, case=2)
true_corrected_rxy

> IQ_vector = rnorm(100000, mean=100, sd=15)
> jobperformance_vector <- IQ_vector*0.53 + rnorm(100000, mean=100, sd=15)*sqrt(1-0.53^2)
> 
> sd(jobperformance_vector)
[1] 15.028
> 
> empirical <- data.frame(IQ = IQ_vector, job_performance = jobperformance_vector)
> 
> pro100 <- empirical %>% filter(IQ > 100)
> rxy <- cor(pro100$IQ, pro100$job_performance)
> sr <- sd(pro100$IQ)
> su <- sd(empirical$IQ)
> 
> psych::rangeCorrection(r=rxy, sdu=su, sdr=sr, case=2)
[1] 0.53679
> cor(empirical$IQ, empirical$job_performance)
[1] 0.53296
> R = (rxy * su/sr)/ sqrt(1-rxy^2 + rxy^2*(su^2/sr^2))
> R
[1] 0.53679

generate_perf <- function(pe, ere) {
  raw_perf <- IQ_vector*ere + rnorm(100000, mean = 100, sd = 15) * sqrt(1 - ere^2)
  raw_perf^pe
}

ratio_error <- function(p, target_ratio, er) {
  perf <- generate_perf(p, er)
  q <- quantile(perf, probs = c(0.01, 0.99))
  ratio <- q[2] / q[1]
  (ratio - target_ratio)^2
}

# Solve for exponent p
solve_exponent <- function(target_ratio, r) {
  optimize(ratio_error, interval = c(0.1, 20), target_ratio = target_ratio, er = r)$minimum
}

p5  <- solve_exponent(5, 0.5)
p10 <- solve_exponent(10, 0.5)
p15 <- solve_exponent(15, 0.5)
p20 <- solve_exponent(20, 0.5)
p25 <- solve_exponent(25, 0.5)

p5
p10
p15
p20
p25

jobperformance_vector <- (IQ_vector*0.5 + rnorm(100000, mean=100, sd=15)*sqrt(1-0.5^2))^p15
q <- quantile(jobperformance_vector, probs = c(0.01, 0.99))
q
###############################
set.seed(1)
IQ_vector <- rnorm(100000, mean = 100, sd = 15)
noise_generator <- rnorm(100000, mean = 100, sd = 15)
people <- data.frame(IQ = IQ_vector, noise = noise_generator)
people$selector <- people$IQ + people$noise


r_variance <- seq(from=0.4, to=0.6, by=0.01)
byx_variance <- seq(from=5, to=25, by=1)
daframe <- data.frame(temp = rep(0, length(r_variance)*length(byx_variance)))
daframe$byx <- rep(0, length(r_variance)*length(byx_variance))
daframe$r <- rep(0, length(r_variance)*length(byx_variance))
it <- 1

for(r_value in r_variance) {
  for(byx_value in byx_variance) {
    p_exp  <- solve_exponent(byx_value, r_value)
    
    people$job_performance <- (people$IQ*r_value + rnorm(100000, mean=100, sd=15)*sqrt(1-r_value^2))^p_exp
    
    mean_111 <- people %>% filter(selector>208)
    plus_115 <- people %>% filter(IQ>115)
    
    ratio115to111 <- mean(plus_115$job_performance)/mean(mean_111$job_performance)
    
    daframe$r[it] <- r_value
    daframe$byx[it] <- byx_value
    daframe$ratio[it] <- ratio115to111
    it <- it + 1
  }
}

daframe

10

Table 3:

1. Identifying a proxy for happiness, like depression diagnoses or self-reported well-being.

2. Correlating said proxy with a trait.

3. The understood conclusion is that the observed correlation is causal and that people should find happiness by optimising for the traits that are correlated with happiness.

Sometimes the methodology is more statistically complex, allowing for a more explicitly causal interpretation, but my criticism here is not methodological or even causal.

The first problem with happiness is the concept of happiness itself, which is murky and difficult to define; often people confuse it with contentment (one’s own life evaluation), nirvana (an absence of suffering), or joy (an emotion). I define happiness as the relative balance of positive/negative emotions that occur within a context-appropriate timeframe. Concretely, if you move away from a location you dislike and start to feel more positively about your life, the context-appropriate time here is after your move.

With regard to contentment, it’s better to think your life is good than bad, but if your life is actually bad, then you should think it is bad — self-delusion will only remove the purpose of negative self-evaluation (self-transformation) and keep the pain.

This is also where I break with conventional wisdom on hedonism. Hedonism is true insomuch as your emotions are what describes your value function, but it’s false insomuch as the emotions themselves should not be optimised. Even if they really are the “ultimate goal” of everything.

Often when people criticise hedonism, they do so arguing that it is good to sacrifice pleasure for the purpose of something else, e.g. avoiding hyper-palatable food to feel healthier. In reality, the example is simply a trade which exchanges one reward for another. Typically, when people propose these examples, the compared rewards are of different natures or time scales, which makes the rebuttal seem legitimate when really it is a misunderstanding of the hedonist thesis.

The conflation of positive/negative emotions with good/bad value is also an issue. Hedonism doesn’t say that positive emotions are good or that negative emotions are bad — pain can be good and pleasure can be bad. Drugs can feel good and ruin your life. People with a congenital insensitivity to pain suffer from their disorder: unnoticed infections, self-inflicted injuries around the mouth from biting, and bone fractures.

Do people really know what they feel?

When people take surveys, they are most likely in a reflective mode and not experiencing any strong emotions, so any response they give will be regarding an assessment of their life as is and not their current emotional state. Sexual activity might bring people joy, but they do not experience said joy when they are filling out a survey and it might not even weigh into their responses.

I think that people have a decent intuitive grasp of how good their life is to live, but I question how accurate these reports are for the purposes of statistical research. The norm is that when different people evaluate different things at a subjective level, their responses do not strongly correlate — whether it’s peer reviewers or raters of personality. I see no reason why happiness should be different.

Researchers often think about happiness in poor theoretical terms; allow me to introduce the example of money and happiness. Let’s be clear here: people feel joy when they receive money. Said joy might be momentary or fleeting, but it is joy all the same. If this is the only emotional thing that money does, then money does cause happiness, just to a limited extent.

One could criticise this argument on the grounds that income statistically tracks life satisfaction more than happiness, but I defer to my criticism of how people report their emotions in these surveys: it’s completely possible that income and resources cause people to feel more positive emotions on a day-to-day basis but said increase is not registered in their resting emotional state, but is reflected in their self-reported life satisfaction.

Then there is also the question of what is done with the money. If the money spent on impulsive purchases, drugs, or other things that don’t matter, then said money is at best a neutral element and at worse a negative one. If said money is spent on things that actually matter and the purchaser consistently values, then it is at worst a neutral element and at best a positive one. Given that income is an instrumental value, it seems odd that this aspect of the debate is so under-emphasised.

What has also been neglected is the massive age interaction that exists in the income ~ happiness association. There is little correspondence between income and happiness in the youth, but a stronger gradient emerges as people age:

My interpretation is that a lot of the appeal of a career is not the work, or the money, but the sense of progression itself. People might be fine with earning $30,000 a year while they are building up their skillset early in their career, but somebody who is making the same amount without the same trajectory will not feel the same way. Also, younger poor people might still have a chance to find a different line of work, but that chance diminishes with age.

The hedonic treadmill is real, insomuch as people feel momentary joy when they upgrade their living standards, but they return to their old resting emotional state after said improvement. I would argue this is true for literally everything that we do — everything is impermanent, as the Buddha says. Most older people I know say running on the treadmill is a bad idea, which seems credible to me, though I can’t say I know firsthand.

That aside, I doubt the reversion to the resting emotional state actually reflects the day-to-day lived experience: if we take two identical lives, and give one person high quality speakers and another low quality speakers, I wouldn’t be surprised if they claimed to experience the same level of happiness, but the fact remains that the person who has higher quality speakers is experiencing a better life, even if the degree is marginal and not subjectively observed.

People who doubt the effect income has on happiness are often well intentioned and are actually correct for the most part, insomuch as money itself doesn’t bring contentment or nirvana, and even when you look at the material aspect, it must be used competently in order to make any difference.

How do you actually find happiness?

Happiness research is poorly framed. It uncritically accepts a universalist frame by asking questions like “does exercise cause happiness?”, when the correct question to ask is “do some people value getting exercise?”.

It’s been observed that, based on meta-analyses of RCTs that control for publication bias, that randomly assigning people to do exercise does not make them less depressed. One could then wrongfully argue that exercise is a waste of time and nobody should do it, when really what should be questioned is the methodology.

In the real world, everybody is exposed to physical activity of some kind. Nothing is stopping them from engaging in it. Naturally, people who value the experience of engaging in physical activity and the rewards it brings them will gravitate towards it, while those who don’t care for them will not. These RCTs needlessly distort this environment and try to make people do things that, based on their revealed preferences, do not want to do.

Most people are overly insecure, and when they are unable or unwilling to do exercise, their bad conscience attributes it to a lack of discipline and effort, when really they might just not care for it. Because exercise is high status and socially desirable, people who would otherwise not care for it feel compelled to do it, and will delude themselves into thinking that they will be happier once they acquire the discipline and aesthetics of a fit person.

Genetics of happiness

All traits are heritable, and life satisfaction is no exception, with a heritability of 33-38%1. Some happiness researchers take that as evidence that people have genetic set points of emotion that people always regress back to when they are rewarded or when their experiences deviate from their expectations. This is not the correct interpretation.

35% is simply not a high heritability, it’s even below the heritability of income (for one year) or education. The heritability of a trait also does not imply that it is directly genetically caused. In the case of income/education, there are no genes that print out dollars or college degrees, but genes can change people’s personalities/levels of intelligence which then have an effect.

I don’t doubt that some people have an innate predisposition to positive or negative emotions, and that said predisposition affects how how they feel on a day-to-day basis. That said, it’s also possible for people to affect their extrnal environment using their own will and learn things that erase the suffering that comes with pain, counter to set point theory. Though I would admit that said ability to be introspective, active, and self-transforming is also heritable and likely unlearnable.

The focus on the “set point” and “resting emotional state” in happiness research seems rather odd in general. It’s not like you feel anything in a “resting emotional state”. Nobody does. You aren’t supposed to. What matters is the relative balance of positive to negative experiences.

It’s possible for people to be in the same situation but with different interpretations of it: one who is fine with it, another who is not and strives to go beyond it. The one who is not content might feel the negativity that comes from being in an unsatisfactory situation, but they might feel a sense of progression or struggle that comes from trying to change, which is arguably a positive phenomenon.

Another misunderstanding I see is that the genetic portion of happiness is somehow outside of your control or this implies that people can’t strive to be happier beyond a certain extent. This is also wrong: you ARE those genes. You ARE the circumstances that shaped you. That conscious “you” that you think is controlling your behaviour independent of your genes and circumstances is completely fake, but that is no reason to give up. If you are unhappy and want to change, DO IT. Don’t let philosophical debates about free will stop you.

Sometimes, researchers bring out the big guns and try using twin controls: they test whether the relationship between a trait and self-reported happiness exists within MZ twins. Technically, it’s still possible for a non-causal correlation between a trait and happiness to exist within MZ twins, but the idea behind the method is that if the relationship doesn’t exist between people who share the same genes, then the relationship is due to genetic confounding.

When people test for whether the association between a supposed cause of happiness and a trait at the genetic level, it almost never exists: not for exercise, homosexuality, and social media use. Though it does for income2. I also suspect most of the supposed causes of depression like pornography, video games, promiscuity, or social isolation would yield similar nulls that we would ignore because we just want to hate degeneracy because it is degenerate.

With regards to depression, the big error I see in the literature on depression is that it assumes it is a bug and not a feature. That depression somehow must reflect a physiological cause like poor nutrition, disease, or a genetic disorder and is not something that happens to functional organisms. I see two types of people who conceive of depression this way: the anti-hedonists who mistakenly believe that you should pursue “meaning” (fake), “status” (fake), or some other higher value (probably, but not certainly, fake) instead of pleasure; then you have the hedonist relativists who don’t actually understand hedonism and think that nothing matters besides positivity, so if depression makes you feel negative, then depression is bad.

The main effect depression has on people is that it reduces their energy levels, willingness to take risks, and the joy they receive from experiences. All of these things, on a superficial level, are bad. But the underlying purpose behind all of these physiological changes is to stop them from engaging in pursuits that lead them nowhere, or inform them of what bad outcomes are.

From a mechanistic point of view, frivolous activity is likely to lead to depression, as depression is caused by doing things that are rewarding in the short term or assumed to lead to rewards, but don’t actually lead to anything in the long-term. I think it’s probably true that most people would be depressed if they played video games for 80 hours a week, but I don’t think that’s the only way people can get depressed and I also think that it’s rare for people to sustain that level of dedication.

People can also get depressed by failing at a non-frivolous activity — an unsatisfying relationship, exercise that leads to no gains or injury, a dead end job, or a friendship that ends in embers. People do actually feel something when they try to accomplish things in life, it’s not the easy pleasure of doing something like eating fast food, but a different kind of reward that comes from effort and struggle. I actually think that people failing at otherwise socially acceptable and long-termist pursuits contibute to much more to depression than the frivolty that everybody loves to scream at, though I admittedly have no evidence for this claim.

So

We don’t need surveys and statistics to find happiness. Maybe we just need to look… Inside. To see if we are happy. If we are, then we don’t need to read any studies because we are already happy. If we are unhappy, then we must find out for ourselves what is causing our unhappiness, and there is no statistic that can identify what is making any specific person miserable.

And even if research on happiness could be helpful, in its present state it is a failure due to its tacit universalism and ignorance of inconvenient null findings. The genetics research on causes of happiness and depression is interesting, and useful for the sake of polemic; even then it is either ignored or misinterpreted by the public.

Further reading:

• Money does not buy much happiness — Maxim Lott. Here he cites a study that leverages data on people who are living their lives in the moment and are asked by an app how happy or unhappy they feel in the moment, which gets past some of the evaluative error observed in studies on the question. The observed effect was linear (no threshold effect) but small:

  

• Politics is worse than video games, porn, and drugs combined — Max. Can’t link this piece enough times.

• Minority Stress Model — East Hunter. In a similar vein to exercise, the link between homosexuality and mental health appears to be genetically mediated.

On my further reading lists — I often include articles that I disagree with on details or even the overall picture if they informed my thinking and consider valuable. I dislike doing disclaimers, but I assume I will be asked about this tendency at least once, so I may as well clarify myself.

2

At a p < .01 level, so just maybe. Perhaps it does for marriage too, but the p-value for the MZ association within men is not very good though (I’m not talking about the MZ-DZ difference:

To predict our economic trajectory, I used projections of national IQs and age demographics of countries to predict their GDPs from 2023 to 2100 using autoregression.

At the moment, there is not enough information to paint a precise picture, but most projections agree that global economic stagnation will set in sometime between 2040 and 2080, and it will get more difficult for nations to retire their citizens. The bulk of variation in predictions is due to misestimated parameters, not variation in projections of population, global IQ, or the use of any specific statistical model.

I estimated the percentage of countries that would have fiscal troubles with pensions by taking 50% of GDP per capita in 2025 to be an estimate of a pensioner’s state-sponsored income throughout the century. I then identified countries that would be projected to spend over 20% of their GDP on their pensions (context: Italy and Greece spend the most on pensions in the world at 18% of GDP), which I deem an excessive amount. Several countries, notably Spain, Greece, Chile, Korea, and Italy are going to have major issues with their finances going forward.

In the case of pensions, variation in population projections have massive influences on the percentage of countries that end up fiscally insolvent:

I consider this to be an underestimate of how much fiscal budgets will be an issue, as pensions run on a hedonic treadmill and aren’t fixed to a specific income. It’s inevitable that some nations will not choose between raising taxes and reducing pensions, resulting in a mounting deficit that will end in a bailout or hyperinflation.

Regionally speaking, East Asia looks awful, Europe looks good or bad depending on the country, and the United States looks fine. I should note that the European countries projected to have the least troubles with pensions are the ones most likely to have lenient immigration policies.

Israel looks like the best nation in terms of wealth and fiscal budget by far. Russia, Indonesia, Mongolia, Iraq, Libya, and Central Asian countries all look good according to the models, though for some of these countries improvements are contingent on them changing their political system. China should look good midcentury, but will fall off if it doesn’t fix its demographics.

Methodology

I use four main sources of data: the UN’s population projections, national IQ data, IQ/education/fertility data, and the Maddison GDP dataset2. I then create a set of synthetic variables that have their uncertainty estimated using various plausible models and simulated scenarios. First come the national IQs, then GDP per capita, then GDP + estimates of retirement burden.

In this study, I use three different population projections: the ‘low’ variant, the ‘medium’ variant, and the ‘zero migration’ variant. All of these labels are self-descriptive, in the sense that the low variant predicts a relatively low population and the zero migration models the world as having no immigration.

To illustrate the differences:

The UN is often criticised for not forecasting properly, particularly for being optimistic and inaccurate. For example, they’ve been predicting South Korean fertility will revert back to sustainable levels for 20 years in a row, despite constant decline:

My friend uncorrelated tried his hand at forecasting fertility trends using machine learning models and was able to beat the UN’s accuracy, but eventually came to the conclusion that the mean-reversion that is observed in fertility forecasts is statistically rational and reflects reversion to the mean. His models, for example, still predicted that South Korea would increase its fertility:

There’s also a lot of historical evidence that suggests the UN has done a good job of forecasting the global population, this is also true at the level of regions, with the UN historically being off by only about 1-5%:

In a similar vein, people often criticise the idea of GDP on the grounds that it is a meaningless metric that can’t be compared across time because of hedonic adjustments and other biases. Here I defer to the defenses made by TotalHealthOptimization and Deep Left Analysis — GDP is not perfect, but it tracks differences in living standards between time and countries very well.

When the economy recedes, GDP drops. When countries adopt market reforms, they get wealthier and their GDP goes up. Poor countries like India, Mexico, and Morocco have lower GDPs than Spain, Japan, and America.

Regarding national IQs — I think they are good enough. If we accept that it’s possible for the average IQ of a country to change, I don’t see what’s wrong with thinking that IQ could vary across countries. They track performance in proxies for intelligence like academic performance across countries extraordinarily well, to the point that PISA/PIRLS/etc tests are now used to measure national IQs. They track differences in economic activity and religious beliefs. I do suspect they have some statistical bias and overestimate difference between nations, though research on this question is unfortunately lacking.

Others may object to the projections that are made on the grounds that some technological advancements like radical life extension or robotics will make these projections moot. Taking the outside view, GDP per capita has grown at a constant rate of about 2% in most industrial economies. No technology, be it cars, vaccines, or the internet has changed this constant rate of growth. That is not to say technology does not have an effect on economic growth, simply that the ones that have come in the past 200 years have not changed the rate at which the economy grows.

With regard to radical life extension, I am skeptical because we don’t even know what causes aging and how it works. There is the goal-direction theory of aging that posits aging reflects a loss of goal direction, there are multicausal damage theories like de Grey’s, immunity theories, programmed-death theories (genes that are good when you are young but bad when you are old are selected for by evolution). I guess you could argue that it only takes one person to be right about aging to make a difference, but I’ll believe it when I see it.

The outside view is also unkind to the idea of radical life extension: the rate at which life expectancy has been improving in developed countries has been slowing down. This is because the death rate exponentially rises with age, so reductions in the death rate lead to increasingly smaller increases in life expectancy.

I didn’t use the high variant because the UN’s population projections are slightly too optimistic as is, both in terms of predicted TFRs and life expectancy. Zero-migration is an untenable assumption, but one could defend the model on the grounds that it does not assume that immigrants will resemble the natives of the countries they migrate to — a more important assumption. As such, I call it the “essentialist” model.

I have my own thoughts on immigration, but I think the idea that it causes global economic growth by taking workers from unproductive countries to productive ones is naive. It’s also an empirical fact that non-western immigrants fiscally constrain the economies they move to and will not make the pension situation any better, so immigration as a functional solution to population decline is dead in the water.

Projecting national IQs forward and backward

I added intelligence as a predictive variable for two reasons: it’s a strong predictor of economic growth between countries, with one IQ point corresponding to an increase in GDP per capita of about 8-10% based on a paper of mine3.

The first thing I did is forecast the world IQ based on the correlations between IQ and fertility by country, average IQ by country, and the world’s population projections. I do not have data for every single country, so occassionally I had to make imputations based on the correlation between education and fertility or values from surrounding countries; a few countries had their NIQs geographically imputed as well. This will bias the results for individual countries, but the direction of the global projection is unchanged.

I built on work I did in a previous paper, with some changes:

1. I added some new data from the latest PIAAC wave, bringing the total to n = 500,000 and k=183.

2. I used a different national IQ dataset: V2 of most accurate national IQs possible.

3. I accounted for uncertainty by calculating forecasts that used different assumptions.

4. In the original paper, I found that the correlation between IQ and fertility varied by source of data, so I added an uncertainty parameter that accounts for the amount of variance observed between sources.

5. The correlation between the mid-parent IQ and their child’s IQ could vary between 0.6 and 0.7 based on my own research4.

6. Varying forecasts by UN population projection.

7. Generational timings can halt or hasten the effects of differential fertility by IQ, so I tried out three different forecasting methods: using the UN’s forecasts of average age of mothers from 2023-2100, assuming they will not change at all, and assuming that all countries will converge to the average over time (28).

Those efforts led to the conclusion that the world IQ should be expected to fall from 85 to 75-79 by the end of the century, depending on which assumptions are made:

Everybody who looks at this chart has more or less the same reaction: it’s over.

…Not exactly. I think this chart overestimates the decline due to changes in fertility within countries — the correlation between education and fertility has decreased in magnitude over the last 50 years5, according to my own data. Income and fertility are also now weakly positively correlated in most European countries.

This is also true for the correlation between national IQs and TFR between countries, as middle and low income countries have started to experience declines in fertility:

I still expect the forecast to be roughly true, just an exaggeration of the magnitude of the effect by about 20%, so the world IQ by the end of the century should be somewhere between 78 and 80. Even if the situation ends up being as bad as my models predict, there is still a possibility that economic growth ends up winning because of how strong compounding is.

Training/testing phase

First, I tested my methodology on an old version of the United Nation’s population projections from 20006 that projected all the way to 2050. I also used the NIQ data and the Maddison GDP per capita dataset to model economic growth. For the sake of simplicity, I stuck with the medium population projection and did not integrate variation in IQ estimates over time into the model.

The method I use to forecast economic growth is fairly standard: autoregression, which involves predicting an outcome from a future year based on a past year, and then doing it over and over again. This method can also integrate covariates — the ones I use are age demographics and national IQ. I used 4 different autoregression methods depending on whether they used fixed or mixed effects7, and whether they used a restricted cubic spline (nonlinear effect) to estimate the effect of past GDP per capita8.

The results were not encouraging. On average, all of the models underpredicted GDP per capita by roughly 10% over the course of the 20 years.

Embarrassingly enough, if you take the observed average global GDP per capita growth from 1950 to 2000 (2.2%), and assume the same amount of growth continues into the future, you would predict the global GDP per capita in 2022 to be 15935, just below the observed value of 16744.

The population projections the UN made in 2000 were reasonably accurate — they predicted there to be 7.6 billion people alive in 2022 in contrast to the observed 8 billion on the planet at that time, so I don’t think they are at fault. All of the models gave roughly the same results, so I don’t think the modelling strategy is at fault either.

The dataset I used to train the model on had only 7334 rows of complete data, which might sound like a lot, but it’s not enough to generate predictions at this granularity. The coefficients for the effects of population ageing aren’t even statistically significant in the fixed-effects models:

To prove this, I used cluster bootstrapping to measure how much error in the model’s parameters contribute to error in predictions (the bootstrap only accounted for the error in the mode, not anything else). Unfortunately, the mixed-effect models could not be bootstrapped because they used country-based effects, so only the normal fixed-effect models were used.

Some of the variance could be attributed to sampling error, but there’s still a residual upwards trend that cannot be accounted by it. The 2000s in particular were a strong decade for growth internationally as China and the Soviet bloc countries were starting to reap the rewards of their market reforms, so it’s not too surprising that the model failed for this particular time period.

Prediction phase

Here I use the same data I used in the prior post, but with the UN’s 2024 population projections instead, so the training dataset has 11,000 rows of information on demographics, IQ, and GDP per capita. The first thing I tested was whether there was a year trend, and there was — growth has been slowing down over time.

However, when one controls for (projected backwards, not measured) declines in IQ and demographic shifts, the year trend is no longer statistically significant and is 77% smaller. This was also true even when I fiddled with the models and introduced splines or different types of effects9.

People have observed this slowdown effect before and have their own theories about it, but I think this one is a good statistical fit. The channels by which this growth slowdown effect operate might not be intuitive, e.g. wealthier economies might increase their regulations and discourage entrepreneurship, but this does mean that any theory of declining economic growth will have to account for demographic changes.

As I did in the previous example, I will be attempting to forecast GDP per capita with four different models based on whether they have fixed/mixed effects and model the effect of prior GDP per capita on current GDP per capita. I will be using three different population forecasts: the low variant, the medium variant, and the essentialist variant. I also model uncertainty in IQ trajectory by forecasting with each individual set of IQ projections that I previously created.

The results suggested that differences in modelling and population scenario had far more bearing than variation in national IQ (which is represented by the width of the individual bars):

As in the previous example, I will use bootstrapping10 to generate CI intervals for the fixed-effect models.

This is the projection for GDP per capita:

This is the forecast for GDP:

Estimating retirement burden

So, the idea is basically that working-age adults have their incomes collected by the government and said resources are then redistributed to retirees. X% is the missing variable which acts as a measurement of retirement burden. Internationally, pensioners recieve about 61% of their income when they retire in OECD countries, though the figure is typically lower in poorer economies, so I will assign a blanket estimate of 50%.

There is no pretense here that it is possible to forecast whether a given country will become fiscally insolvent due to aging populations and lowering GDP per capita. I don’t think that would even be possible with better methods and more data, the issue is that it’s not possible to know what decisions policymakers will make in response to crisis. Some countries like Singapore have private pension systems so they are more robust to the effects of evolving demographics, but that’s besides the point — public systems are the standard and I am not focusing on the fate of individual countries.

Instead, what I intend to do is to estimate the % of countries that will be under stress due to the effects of demographic decline. From what I gather online, spending over 15-20% of GDP on pensions seems to be the figure that most economists and policy-makers cite as unstable; I didn’t look too much into it but it sounds reasonable to me — I’ll stick with 20% for now. For context, the nations that spend the most in the world on their pensions are Italy and Greece, at 18%. These are the percentages by population projection type:

Here estimates vary significantly by population projection: the low fertility variant forecasts that 40% of nations will be fiscally insolvent by 2100 due to pensions, while the medium variant forecasts that only 10% will. The essentialist variant is somewhere in between. Given the conservative bound I used for what constitutes a crisis (20%) and that nations vary greatly in their spending and policies, I think it’s inevitable that several developed countries will face a pension crisis this century.

On a national scale, the countries most likely to face major issues with pensions are South Korea, Chile, Taiwan, Japan, and most of Southern Europe.

I think that fiscal crises will start popping off earlier than 2050 because the modelling strategy — blanket estimates for pension size and no hedonic treadmill — cause the variance in insolvency risk to be vastly underestimated.

What will actually happen

The main reason why aging populations have such a negative impact on GDP per capita is that most people tend to retire when they come of age. The other reason is that most innovation and new technology, which drives long term economic growth, tends to come from people between their 20s and 40s. Aging populations will not forget the old technologies, but they will struggle to create new ones, leading to lower levels of long term growth.

Besides these two effects, I don’t think that aging populations are much of an issue, and that it’s unlikely that we will see the global economy tank. Given that the previous modelling attempt underpredicted growth, and that I predict many elderly people are going to remain working against their wishes, this is my relatively positive prediction:

Regarding fertility, I don’t think the declines are a mystery. Fertility is falling in ~90% of countries at the same time, which is suggestive of a cultural or technological cause rather than an economic one. As such, I think it’s unlikely that policy interventions will be able to control population figures.

Most people only want 2-3 children, if at all, so fertility preferences in most countries are only just above replacement in most countries; in China they are below. This is fertility in an ideal world: a world where people are dating somebody they are satisfied with and feel materially ready to have children11. Which is well, not happening for most people.

It’s also possible to plot the rate at which fertility changes, and it’s clear that fertility started falling once smartphones and the mass internet started taking off.

If you ignore the first fall that came it looks like the long term trend is that the rate at which fertility is changing is approaching zero. Which means that the change in fertility is going to be closer to the medium variant than the low variant, for better or for worse. For reference, see:

If you take an account of all the biases:

1. Old people aren’t some magical curse that causes GDP shrink: they slow down economies by retiring and making less new technology. The former will be an issue if it is not correctly addressed politically, and the latter only leads to stagnation.

2. Global IQ is likely to be higher than predicted.

3. The likely fertility future is somewhere between the medium and low variant, but closer to the medium variant.

4. Improvements to life expectancy will continue to see diminishing returns.

5. Essentialism is true and moving people from country to country leads to no net global economic changes in the short term (and negative ones in the long term).

I think we are going to be fine. Not good, but not collapsing any time soon.

Political implications of this information

Governments facing fiscal troubles have three choices:

1. Raise taxes.

2. Cut pensions.

3. Do nothing and suffer from debt and/or hyperinflation.

3 is obviously the worst outcome here. It would be nice if we still had something like the gold standard, unfortunately economists have brainrot and think that having constant inflation is necessary for growth.

Raising taxes vs cutting pensions involves two tradeoffs: one of them is the tradeoff in labour, where raising taxes results in less time spent working in both the youth and elderly, but cuts economic growth; cutting pensions puts more people in the workforce, but maintains economic growth. The former benefits the old and attracts people with leftist values, while the latter benefits the youth and attracts people with right wing values. I’m not sure who is going to win, I haven’t thought about it too deeply.

Pensions and spending will become the most pressing issue in any country that starts to spend over 20-25% of its GDP on pensions, in some nations like Korea or Spain I predict it to overshadow literally everything else, kind of like how WW2 drowned out every other political concern. Everybody will have a stake in the debate, regardless of their age and income.

Prognosis by nation

Albania looks rough, no matter what happens with demographics.

The United Arab Emirates looks very rough if no labour is imported. All the other oil baron countries look more or less the same.

Argentina could get very bad, depending on how birth rates work out.

Armenia looks iffy.

Australia looks fine.

Austria looks iffy.

Azerbaijan looks OK.

I have no idea what happened with this population projection.

Belgium looks iffy.

Brazil looks iffy.

The essentialist (aka true) projection looks bad for Canada.

Switzerland looks iffy.

Chile is a nightmare.

China looks high variance.

Czechia looks OK.

Germany looks bleak, but passable.

Denmark looks OK.

Spain is toast.

Estonia could get bad.

Finland spends a lot on pensions in the real world, but they presumably could cut back on them.

France is OK.

I’d say Britain would be fine if I wasn’t aware of how awful their politics are.

Greece looks rough.

Croatia could get bad.

Hungary looks OK.

Indonesia is on track to becoming wealthier than Greece.

India looks good relative to where it already is.

Ireland looks OK.

Iran could get really bad.

The model predicts that Iraq will be doing much better at the end of the century. I think this could happen if things improve politically.

Iceland looks iffy.

Israel looks really, really good.

Italy looks awful.

Jamaica could get surprisingly bad.

Japan looks better than I expected.

Kazakhstan looks good.

South Korea looks terrible.

Libya looks good, assuming they fix their political situation.

Lithuania could get bad.

Malta is cooked.

Montenegro could get ugly.

Mongolia looks good.

Malaysia looks good.

Netherlands look OK.

Norway looks iffy.

New Zealand looks OK.

Poland looks bad.

Puerto Rico…

Portugal looks iffy.

Romania looks iffy.

Russia looks far better than I expected.

Singapore looks wild. If I recall correctly, they have a private pension system, which should protect them from a fiscal crisis.

Serbia could get bad.

Sweden looks iffy.

Thailand looks iffy.

Turkmenistan looks good.

Turkey looks iffy.

Taiwan looks bad.

Ukraine, in light of recent events, looks awful.

Uruguay looks bad.

The United States looks decent.

TODO list for V2

1. Use communist history as a past/future covariate, which could perhaps help with correcting under-prediction during the 2000-2022 period.

2. Use the 3rd version of the NIQ dataset for the forecasts, which integrates the countries from the previous datasets that got passed over in data collection.

3. Use ML/DL or other models in addition to standard autoregression to predict fertility and life expectancy, then with those predict population figures, then with said population figures the GDP per capita estimates can be made. I would have to get a better video card first, though. (Currently using RX6600).

4. Integrate multiple sources of GDP per capita into one dataset. I didn’t want to do this here because it turns out that integrating the various datasets lowered the quality of the models, but with ML/DL the different datasets can be labelled and the models could adjust accordingly. The problem here though is that ML/DL have issues generalising outside of their reference bounds and it will be difficult for them to infer what happens when 50% of people are over 65 when the oldest countries in the training dataset have maybe half of that.

5. Only use essentialist models.

6. Use different age categories, I think 0-15, 15-25, 25-50, 50-75, >75 would be best, the categories 15-25 and 50-75 can serve as liminal spaces between dependents and working-age people.

Currently, the training dataset currently includes 11,122 rows and 160 countries; the UN’s population projections contain 235, so the theoretic maximum size of a training dataset that goes from 1950 to 2025 is 17,775. Were this to be combined with superior methodology and communist history, I think the error bars in the projection could be shrunk by 40% or more.

1

That aside, I do think that resource depletion is concerning, especially since renewable alternatives are either expensive (e.g. solar, wind) or politically unfeasible (nuclear).

5

True when controlling for source, cohort, and country:

9

Spline model:

Mixed-effects:

Mixed effects + spline:

Because insecurity is maligned by contemporary society, “total self acceptance” has become the new thing in spirituality and self-help culture. If one searches for the term, they will find a bunch of psychologists and new-age type coaches explaining what it is and how to reach it. This idea has also gained some traction in the rationalist sphere due to the influence of a certain influencer who I will not name; I just want to emphasise that this is not just a normie pathology.

I define insecurity as the state of perceiving that one of our traits might not be in line with our values. ‘Might’, because someone certain they lack a trait cannot be insecure about it. This definition is important, as dictionaries often uncritically conflate insecurity with a lack of self-confidence or feelings of unworthiness. Simply not liking the fact that you are not talented isn’t insecurity – it’s either truth (if you don’t have talent) or delusion (if you do). This uncertainty can arise from four sources:

1. Lack of clarity: the individual might not be sure of what traits they possess, so value judgements cannot be given.

2. Lack of predictability: the individual attaches themselves to a trait that doesn’t change predictably.

3. Lack of implication: a lack of awareness of what a trait can be a means for, so an individual who doesn’t care for a trait in and of itself might still be insecure about it. An introvert may prefer to spend evenings alone, but might be insecure about whether that makes them miss out on business opportunities or relationships.

4. Misaligned values: the individual is not sure whether their values are actually valuable.

Starting with the first cause, it is impossible to be completely self-aware because humans aren’t omniscient. Which may sound obvious, but it also means that you will always be insecure about something. Beyond this, consciousness is also inherently recursive, a function that applies itself to itself, kind of like how Russian nesting dolls contain another doll. As such, there is always an unobserved layer of consciousness that cannot be processed as information.

Being insecure about your own values might seem silly. Sometimes it isn’t; values interact with each other, so the presence of a value can be objectively “bad” or “good” according to the relationships between the values that said value coexists with. Somebody who wants to bedrot all day might question whether they really want to do anything with their lives. And maybe they should, as they will have to choose between these values at some point.

There is a social taboo against having negative perceptions of yourself (that’s unconfident!) and having positive perceptions of yourself (that’s grandiosity!). There are nuances and degrees to this taboo, but I think they are diluted in the cultural telephone game, so the end result is many people being reluctant to evaluate themselves properly. Some people do this by engaging in outright self-delusion, and some try to do it by reframing their weaknesses to be strengths, or downplaying their strengths. I think this is a massive source of a lot of insecurity: if you are not willing to think of yourself in a negative or positive way, then you cannot think of yourself at all.

Insecurity being a symptom rather than an illness implies a radically different treatment: instead of burying insecurities under 500 layers of neural networks, one should face them. This involves figuring out what the cause of insecurity is (perception, instability, means, or values), and finding a solution to the cause or accepting that one does not exist.

Total self-acceptance sounds a lot like an enlightenment to people. I have no idea if “enlightenment” exists or if it resembles anything close to the platonic idea people have of it. But the idea that there is a secret truth out there that can raise people above the chained, unhappy, and ignorant masses into a new state of being is extremely compelling to… the masses. Some people realise that and build things: cults, religions, ideologies, or whatever to satisfy that gnostic yearning. Sometimes people think that the answer lies in money, sex, sports, status, science, or altruism. Most, if not all of these things don’t satisfy that inner gnosticism – either because they are false or impermanent.

I am a fan of the Buddhist doctrine of anattā (noself), the idea that the “self” is not permanent, causal, or coherent, and that identity is false perception. In the context of insecurity, you being insecure about how smart you are could be reframed to be this process: our observational faculties receive feedback that casts doubt on our intellectual faculties are as strong as they think they are, which gives rise to thoughts (“am I not as smart as I think I am?”) and negative feelings that accompany this realisation. During this process, the body can irrationally identify with facets of this process – probably the thoughts or the intellectual faculties that are in question – and experience an additional layer of suffering.

Noself can save somebody from the layer of suffering that occurs afterwards, but it cannot stop them from questioning themselves, or even from the negative emotions that spring from the questioning. During the process of insecurity, the negative emotions are communicating “you need to reevaluate what you are or what you value”, not “haha you suck dude”. There is no evolutionary spandrell at play here. This Buddhist doctrine also deconstructs the idea that there is even a “self” to accept in the first place.

Initially, I was very averse to Buddhism because I associated it with wanting nothing and being some ascetic monk on an island, which I didn’t like. I have been in denial about this until just recently, but I am a very ambitious person and I have almost always rejected anything that I saw as obstructing my goals.

From my admittedly limited understanding of Buddhism, I don’t think Buddhism actually teaches you to stop wanting things, but to stop irrationally craving them. It wants to save you from the unnecessary suffering that stems from a poor cognitive model of the universe: where you are a self that attaches itself to things that don’t actually give you any permanent value. Buddhists in fact have two separate words for want: chanda, which refers to a zeal or desire to act that comes from a clear conscience, and taṇhā, a deluded state of craving where one believes they will escape dissatisfaction by gaining a thing that will only give temporary satisfaction; a state that is actually responsible for dissatisfaction in the first place.

I now accept that anything I accomplish or obtain will never satisfy me permanently, that “I” don’t actually exist in the way my brain tricks me into thinking I do. But I continue to engage with life anyway, like any other person you see on the street. If that’s not Buddhism, then I reject Buddhism, and will call what I believe Buddhism 2.

The closest one can get to totally self-accepting is accepting insecurity and appropriately dealing with it, very different and far from total self-acceptance, but better than nothing.