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 Russian Reaction Blog / ScienceTeasers

This blog post by Sarah Constantin has an impressively comprehensive tally of performance trends in AI across multiple domains.

chess-elo-humans-vs-computers Three main things to do take away:

  • In games performance, e.g. chess (see right, based on Swedish Chess Computer Association data) “exponential growth in data and computation power yields exponential improvements in raw performance.” So the relation between them is linear.
  • This relationship may be sublinear in non-game domains, such as natural language processing (NLP).
  • “Deep learning” only created discontinuous (but one time) improvements in image and speech recognition, but not in strategy games or NLP. Its record on machine translation and arcade games (see below right) is ambiguous.

arcade-games-human-vs-computer So “deep learning” might not have been as transformational as the tech press would have had you believe, and as Miles Brundage observed, has largely been about “general approaches for building narrow systems rather than general approaches for building general systems.”

And we also know that Moore’s Law has been slowing down of late.

If this is basically accurate, then the spate of highly visible AI successes we have been seeing in quick succession of late – peak human performance in go in 2016; in No Limit poker with multiple players a couple of months ago – could end up being a one-off coincidence that will be followed by another AI winter.

And we will have to do something cleverer than naively projecting Kurzweil’s graphs forwards to get to the singularity.

• Category: Science • Tags: Artificial Intelligence, Futurism 


Ashburn-Nardo, Leslie 2017
Parenthood as a Moral Imperative? Moral Outrage and the Stigmatization of Voluntarily Childfree Women and Men


Nationally representative data indicate that adults in the United States are increasingly delaying the decision to have children or are forgoing parenthood entirely. Although some empirical research has examined the social consequences of adults’ decision to be childfree, few studies have identified explanatory mechanisms for the stigma this population experiences. Based on the logic of backlash theory and research on retributive justice, the present research examined moral outrage as a mechanism through which voluntarily childfree targets are perceived less favorably than are targets with children for violating the prescribed social role of parenthood. In a between-subjects experiment, 197 undergraduates (147 women, 49 men, 1 participant with missing gender data) from a large U.S. Midwestern urban university were randomly assigned to evaluate a male or female married target who had chosen to have zero or two children. Participants completed measures of the target’s perceived psychological fulfillment and their affective reactions to the target. Consistent with earlier studies, voluntarily childfree targets were perceived as significantly less psychologically fulfilled than targets with two children. Extending past research, voluntarily childfree targets elicited significantly greater moral outrage than did targets with two children. My findings were not qualified by targets’ gender. Moral outrage mediated the effect of target parenthood status on perceived fulfillment. Collectively, these findings offer the first known empirical evidence of perceptions of parenthood as a moral imperative.

The author herself doesn’t seem to be happy with her own findings:

Practically speaking, the present findings have some troubling potential implications for howpeople transition to parenthood. For example, the present findings, obtained with college students in the Midwestern United States, suggest that many young people view children as a necessary ingredient for fulfilling lives. Thus, they may feel tremendous pressure to have children, not only from others as this literature suggests (Mueller and Yoder 1999), but also internally. Ironically, these perceptions have absolutely no basis in reality. Meta-analyses reveal that parents report significantly less marital satisfaction than do non-parents, and as their number of children increases, marital satisfaction decreases (Twenge et al. 2003).

That maybe so, but reality definitely seems to have a basis in those perceptions.

For instance, people without those perceptions didn’t tend to pass on their genes.

• Category: Science • Tags: Demographics, Paper Review, Psychology 

Fundamentally solve the “intelligence problem,” and all other problems become trivial.

The problem is that this problem is a very hard one, and our native wit is unlikely to suffice. Moreover, because problems tend to get harder, not easier, as you advance up the technological ladder (Karlin, 2015), in a “business as usual” scenario with no substantial intelligence augmentation we will effectively only have a 100-200 year “window” to effect this breakthrough before global dysgenic fertility patterns rule it out entirely for a large part of the next millennium.

To avoid a period of prolonged technological and scientific stagnation, with its attendant risks of collapse, our global “hive mind” (or “noosphere”) will at a minimum have to sustain and preferably sustainably augment its own intelligence. The end goal is to create (or become) a machine, or network of machines, that recursively augment their own intelligence – “the last invention that man need ever make” (Good, 1965).

In light of this, there are five main distinct ways in which human (or posthuman) civilization could develop in the next millennium.


(1) Direct Technosingularity

kurzweil-singularity-is-near The development of artificial general intelligence (AGI), which should quickly bootstrap itself into a superintelligence – defined by Nick Bostrom as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest” (Bostrom, 2014). Especially if this is a “hard” takeoff, the superintelligence will also likely become a singleton, an entity with global hegemony (Bostrom, 2006).

Many experts predict AGI could appear by the middle of the 21st century (Kurzweil, 2005; Müller & Bostrom, 2016). This should quickly auto-translate into a technological singularity, henceforth “technosingularity,” whose utilitarian value for humanity will depend on whether we manage to solve the AI alignment problem (i.e., whether we manage to figure out how to persuade the robots not to kill us all).

The technosingularity will creep up on us, and then radically transform absolutely everything, including the very possibility of any further meaningful prognostication – it will be “a throwing away of all the previous rules, perhaps in the blink of an eye, an exponential runaway beyond any hope of control” (Vinge, 1993). The “direct technosingularity” scenario is likely if AGI turns out to be relatively easy, as the futurist Ray Kurzweil and DeepMind CEO Demis Hassabis believe.

(2) The Age of Em

The development of Whole Brain Emulation (WBE) could accelerate the technosingularity, if it is relatively easy and is developed before AGI. As the economist Robin Hanson argues in his book The Age of Em, untold quintillions of emulated human minds, or “ems,” running trillions of times faster than biological wetware, should be able to effect a transition to true superintelligence and the technosingularity within a couple of human years (Hanson, 2016). This assumes that em civilization does not self-destruct, and that AGI does not ultimately prove to be an intractable problem. A simple Monte Carlo simulation by Anders Sandberg hints that WBE might be achieved by the 2060s (Sandberg, 2014).


Deus Ex: Human Revolution.

(3) Biosingularity

We still haven’t come close to exhausting our biological and biomechatronic potential for intelligence augmentation. The level of biological complexity has increased hyperbolically since the appearance of life on Earth (Markov & Korotayev, 2007), so even if both WBE and AGI turn out to be very hard, it might still be perfectly possible for human civilization to continue eking out huge further increases in aggregate cognitive power. Enough, perhaps, to kickstart the technosingularity.

There are many possible paths to a biosingularity.

The simplest one is through demographics: The tried and tested method of population growth (Korotaev & Khaltourina, 2006). As “technocornucopians” like Julian Simon argue, more people equals more potential innovators. However, only a tiny “smart fraction” can meaningfully contribute to technological progress, and global dysgenic fertility patterns imply that its share of the world population is going to go down inexorably now that the FLynn effect of environmental IQ increases is petering out across the world, especially in the high IQ nations responsible for most technological progress in the first place (Dutton, Van Der Linden, & Lynn, 2016). In the longterm “business as usual” scenario, this will result in an Idiocracy incapable of any further technological progress and at permanent risk of a Malthusian population crash should average IQ fall below the level necessary to sustain technological civilization.

As such, dysgenic fertility will have to be countered by eugenic policies or technological interventions. The former are either too mild to make a cardinal difference, or too coercive to seriously advocate. This leaves us with the technological solutions, which in turn largely fall into two bins: Genomics and biomechatronics.

The simplest route, already on the cusp of technological feasibility, is embryo selection for IQ. This could result in gains of one standard deviation per generation, and an eventual increase of as much as 300 IQ points over baseline once all IQ-affecting alleles have been discovered and optimized for (Hsu, 2014; Shulman & Bostrom, 2014). That is perhaps overoptimistic, since it assumes that the effects will remain strictly additive and will not run into diminishing returns.

Even so, a world with a thousand or a million times as many John von Neumanns running about will be more civilized, far richer, and orders of magnitude more technologically dynamic than what we have now (just compare the differences in civility, prosperity, and social cohesion between regions in the same country separated by a mere half of a standard deviation in average IQ, such as Massachussetts and West Virginia). This hyperintelligent civilization’s chances of solving the WBE and/or AGI problem will be correspondingly much higher.

The problem is that getting to the promised land will take about a dozen generations, that is, at least 200-300 years. Do we really want to wait that long? We needn’t. Once technologies such as CRISPR/Cas9 maturate, we can drastically accelerate the process and accomplish the same thing through direct gene editing. All this of course assumes that a concert of the world’s most powerful states doesn’t coordinate to vigorously clamp down on the new technologies.

Even so, we would still remain “bounded” by human biology. For instance, womb size and metabolic load are a crimper on brain size, and the specificities of our neural substrate places an ultimate ceiling even on “genetically corrected” human intellectual potential.

There are four potential ways to go beyond biology, presented below from “most realistic” to “most sci-fi”:

Neuropharmocology: Nootropics already exist, but they do not increase IQ by any significant amount and are unlikely to do so in the future (Bostrom, 2014).

Biomechatronics: The development of neural implants to augment human cognition beyond its peak biological potential. The first start-ups, based for now on treatment as opposed to enhancement, are beginning to appear, such as Kernel, where the futurist Randal Koene is the head scientist. This “cyborg” approach promises a more seamless, and likely safer, integration with ems and/or intelligent machines, whensoever they might appear – this is the reason why Elon Musk is a proponent of this approach. However, there’s a good chance that meaningful brain-machine interfaces will be very hard to implement (Bostrom, 2014).

Nanotechnology: Nanobots could potentially optimize neural pathways, or even create their own foglet-based neural nets.

Direct Biosingularity: If WBE and/or superintelligence prove to be very hard or intractable, or come with “minor” issues such as a lack of rigorous solutions to the AI alignment problem or the permanent loss of conscious experience (Johnson, 2016), then we might attempt a direct biosingularity – for instance, Nick Bostrom suggests the development of novel synthetic genes, and even more “exotic possibilities” such as vats full of complexly structured cortical tissue or “uplifted” transgenic animals, especially elephants or whales that can support very large brains (Bostrom, 2014). The terminal result of a true biosingularity could might be some kind of “ecotechnic singleton,” e.g. Stanisław Lem’s Solaris, a planet dominated by a globe-spanning sentient ocean.

Bounded by the speed of neuronal chemical reactions, it is safe to say that the biosingularity will be a much slower affair than The Age of Em or a superintelligence explosion, not to mention the technosingularity that would likely soon follow either of those two events. However, human civilization in this scenario might still eventually achieve the critical mass of cognitive power needed to solve WBE or AGI, thus setting off the chain reaction that leads to the technosingularity.


(4) Eschaton

Nick Bostrom defined existential risk thus: “One where an adverse outcome would either annihilate Earth-originating intelligent life or permanently and drastically curtail its potential.(Bostrom, 2002)

We can divide existential risks into four main bins: Geoplanetary; Anthropic; Technological; and Philosophical.

In any given decade, a gamma ray burst or even a very big asteroid could snuff us out in our earthly cradle. However, the background risk is both constant and extremely low, so it would be cosmically bad luck for a geoplanetary Götterdämmerung to do us in just as we are about to enter the posthuman era.

There are three big sources of “anthropic” existential risk: Nuclear war, climate change, and the exhaustion of high-EROEI energy sources.

Fears of atomic annihilation are understandable, but even a full-scale thermonuclear exchange between Russia and the US is survivable, and will not result in the collapse of industrial civilization ala A Canticle for Leibowitz or the Fallout video games, let alone human extinction (Kahn, 1960; Kearny, 1979). This was true during the Cold War and it is doubly true today, when nuclear weapons stocks are much lower. To be sure, some modest percentage of the world population will die, and a majority of the capital stock in the warring nations will be destroyed, but as Herman Kahn might have said, this is a tragic but nonetheless distinguishable outcome compared to a true “existential risk.”

Much the same can be said of anthropogenic climate change. While it would probably do more harm than good, at least in the medium-term (Stager, 2011), even the worst outcomes like a clathrate collapse will most likely not translate into James Lovelock’s apocalyptic visions of “breeding pairs” desperately eking out a hardscrabble survival in the Arctic. The only truly terminal outcome would be a runaway greenhouse effect that turns Earth into Venus, but there is simply nowhere near enough carbon on our planetary surface for that to happen.

As regards global energy supplies, while the end of high-density fossil fuels might somewhat reduce living standards relative to what they would have otherwise been, there is no evidence it would cause economic decline, let alone technological regression back to the Olduvai Gorge conditions as some of the most alarmist “doomers” have claimed. We still have a lot of fat to cut! Ultimately, the material culture even of an energy-starved country like Cuba compares very positively to those of 95% of all humans who have ever lived. Besides, there are still centuries’ worth of coal reserves left on the planet, and nuclear and solar power have been exploited to only a small fraction of their potential.

By far the biggest technological risk is malevolent AGI, so much so that entire research outfits such as MIRI have sprung up to work on it. However, it is so tightly coupled to the Technosingularity scenario that I will refrain from further commentary on it here.

This leaves mostly just the “philosophical,” or logically derived, existential risks. For instance, the computer simulation we are in might end (Bostrom, 2003) – perhaps because we are not interesting enough (if we fail to reach technosingularity), or for lack of hardware to simulate an intelligence explosion (if we do). Another disquieting possibility is implied by the foreboding silence all around as – as Enrico Fermi asked, “Where is everyone?” Perhaps we are truly alone. Or perhaps alien post-singularity civilizations stay silent for a good reason.

We began to blithely broadcast our presence to the void more than a century ago, so if there is indeed a “superpredator” civilization keeping watch over the galaxy, ready to swoop down at the first sign of a potential rival (e.g. for the simulation’s limited computing resources), then our doom may have already long been written onto the stars. However, unless they have figured out how to subvert the laws of physics, their response will be bounded by the speed of light. As such, the question of whether it takes us half a century or a millenium to solve the intelligence problem – and by extension, all other problems, including space colonization – assumes the most cardinal importance!


Vladimir Manyukhin, Tower of Sin.

(5) The Age of Malthusian Industrialism (or, “Business as Usual”)

The 21st century turns out to be a disappointment in all respects. We do not merge with the Machine God, nor do we descend back into the Olduvai Gorge by way of the Fury Road. Instead, we get to experience the true torture of seeing the conventional, mainstream forecasts of all the boring, besuited economists, businessmen, and sundry beigeocrats pan out.

Human genetic editing is banned by government edict around the world, to “protect human dignity” in the religious countries and “prevent inequality” in the religiously progressive ones. The 1% predictably flout these regulations at will, improving their progeny while keeping the rest of the human biomass down where they believe it belongs, but the elites do not have the demographic weight to compensate for plummeting average IQs as dysgenics decisively overtakes the FLynn Effect.

We discover that Kurzweil’s cake is a lie. Moore’s Law stalls, and the current buzz over deep learning turns into a permanent AI winter. Robin Hanson dies a disappointed man, though not before cryogenically freezing himself in the hope that he would be revived as an em. But Alcor goes bankrupt in 2145, and when it is discovered that somebody had embezzled the funds set aside for just such a contingency, nobody can be found to pay to keep those weird ice mummies around. They are perfunctorily tossed into a ditch, and whatever vestigial consciousness their frozen husks might have still possessed seeps and dissolves into the dirt along with their thawing lifeblood. A supermall is build on their bones around what is now an extremely crowded location in the Phoenix megapolis.

For the old concerns about graying populations and pensions are now ancient history. Because fertility preferences, like all aspects of personality, are heritable – and thus ultracompetitive in a world where the old Malthusian constraints have been relaxed – the “breeders” have long overtaken the “rearers” as a percentage of the population, and humanity is now in the midst of an epochal baby boom that will last centuries. Just as the human population rose tenfold from 1 billion in 1800 to 10 billion by 2100, so it will rise by yet another order of magnitude in the next two or three centuries. But this demographic expansion is highly dysgenic, so global average IQ falls by a standard deviation and technology stagnates. Sometime towards the middle of the millenium, the population will approach 100 billion souls and will soar past the carrying capacity of the global industrial economy.

Then things will get pretty awful.

But as they say, every problem contains the seed of its own solution. Gnon sets to winnowing the population, culling the sickly, the stupid, and the spendthrift. As the neoreactionary philosopher Nick Land notes, waxing Lovecraftian, “There is no machinery extant, or even rigorously imaginable, that can sustain a single iota of attained value outside the forges of Hell.”

In the harsh new world of Malthusian industrialism, Idiocracy starts giving way to A Farewell to Alms, the eugenic fertility patterns that undergirded IQ gains in Early Modern Britain and paved the way to the industrial revolution. A few more centuries of the most intelligent and hard-working having more surviving grandchildren, and we will be back to where we are now today, capable of having a second stab at solving the intelligence problem but able to draw from a vastly bigger population for the task.

Assuming that a Tyranid hive fleet hadn’t gobbled up Terra in the intervening millennium…

2061su-longing-for-home, Longing for Home

The Forking Paths of the Third Millennium

In response to criticism that he was wasting his time on an unlikely scenario, Robin Hanson pointed out that even if there was just a 1% chance of The Age of Em coming about, studying it was well worth his while considering the sheer amount of future consciences and potential suffering at stake.

Although I can imagine some readers considering some of these scenarios as less likely than others, I think it’s fair to say that all of them are at least minimally plausible, and that most people would also assign a greater than 1% likelihood to a majority of them. As such, they are legitimate objects of serious consideration.

My own probability assessment is as follows:

(1) (a) Direct Technosingularity – 25%, if Kurzweil/MIRI/DeepMind are correct, with a probability peak around 2045, and most likely to be implemented via neural networks (Lin & Tegmark, 2016).

(2) The Age of Em – <1%, since we cannot obtain functional models even of 40 year old microchips from scanning them, to say nothing of biological organisms (Jonas & Kording, 2016)

(3) (a) Biosingularity to Technosingularity – 50%, since the genomics revolution is just getting started and governments are unlikely to either want to, let alone be successful at, rigorously suppressing it. And if AGI is harder than the optimists say, and will take considerably longer than mid-century to develop, then it’s a safe bet that IQ-augmented humans will come to play a critical role in eventually developing it. I would put the probability peak for a technosingularity from a biosingularity at around 2100.

(3) (b) Direct Biosingularity – 5%, if we decide that proceeding with AGI is too risky, or that consciousness both has cardinal inherent value and is only possible with a biological substrate.

(4) Eschaton – 10%, of which: (a) Philosophical existential risks – 5%; (b) Malevolent AGI – 1%; (c) Other existential risks, primarily technological ones: 4%.

(5) The Age of Malthusian Industrialism – 10%, with about even odds on whether we manage to launch the technosingularity the second time round.

There is a huge amount of literature on four of these five scenarios. The most famous book on the technosingularity is Ray Kurzweil’s The Singularity is Near, though you could make do with Vernor Vinge’s classic article The Coming Technological Singularity. Robin Hanson’s The Age of Em is the book on its subject. Some of the components of a potential biosingularity are already within our technological horizon – Stephen Hsu is worth following on this topic, though as regards biomechatronics, for now it remains more sci-fi than science (obligatory nod to the Deus Ex video game franchise). The popular literature on existential risks of all kinds is vast, with Nick Bostrom’s Superintelligence being the definitional work on AGI risks. It is also well worth reading his many articles on philosophical existential risks.

Ironically, by far the biggest lacuna is with regards to the “business as usual” scenario. It’s as if the world’s futurist thinkers have been so consumed with the most exotic and “interesting” scenarios (e.g. superintelligence, ems, socio-economic collapse, etc.) that they have neglected to consider what will happen if we take all the standard economic and demographic projections for this century, apply our understanding of economics, psychometrics, technology, and evolutionary psychology to them, and stretch them out to their logical conclusions.

The resultant Age of Industrial Malthusianism is not only something that’s easier to imagine than many of the other scenarios, and by extension easier for modern people to connect with, but it is also something that is genuinely interesting in its own right. It is also very important to understand well. That is because it is by no means a “good scenario,” even if it is perhaps the most “natural” one, since it will eventually entail unimaginable amounts of suffering for untold billions a few centuries down the line, when the time comes to balance the Malthusian equation. We will also have to spend an extended amount of time under an elevated level of philosophical existential risk. This would be the price we will have to pay for state regulations that block the path to a biosingularity today.


Bostrom, N. (2002). Existential risks. Journal of Evolution and Technology / WTA, 9(1), 1–31.

Bostrom, N. (2003). Are We Living in a Computer Simulation? The Philosophical Quarterly, 53(211), 243–255.

Bostrom, N. (2006). What is a Singleton. Linguistic and Philosophical Investigations, 5(2), 48–54.

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

Dutton, E., Van Der Linden, D., & Lynn, R. (2016). The negative Flynn Effect: A systematic literature review. Intelligence, 59, 163–169.

Good, I. J. (1965). Speculations Concerning the First Ultraintelligent Machine. In F. Alt & M. Ruminoff (Eds.), Advances in Computers, volume 6. Academic Press.

Hanson, R. (2016). The Age of Em: Work, Love, and Life when Robots Rule the Earth. Oxford University Press.

Hsu, S. D. H. (2014, August 14). On the genetic architecture of intelligence and other quantitative traits. arXiv [q-bio.GN]. Retrieved from

Johnson, M. (2016). Principia Qualia: the executive summary. Open Theory. Retrieved from

Jonas, E., & Kording, K. (2016). Could a neuroscientist understand a microprocessor? bioRxiv. Retrieved from

Kahn, H. (1960). On thermonuclear war (Vol. 141). Cambridge Univ Press.

Karlin, A. (2015). Introduction to Apollo’s Ascent. The Unz Review. Retrieved from

Kearny, C. H. (1979). Nuclear war survival skills. NWS Research Bureau.

Korotaev, A. V., & Khaltourina, D. (2006). Introduction to Social Macrodynamics: Secular Cycles and Millennial Trends in Africa. Editorial URSS.

Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Penguin.

Lin, H. W., & Tegmark, M. (2016, August 29). Why does deep and cheap learning work so well?arXiv [cond-mat.dis-nn]. Retrieved from

Markov, A. V., & Korotayev, A. V. (2007). Phanerozoic marine biodiversity follows a hyperbolic trend. Palaeoworld, 16(4), 311–318.

Müller, V. C., & Bostrom, N. (2016). Future Progress in Artificial Intelligence: A Survey of Expert Opinion. In V. C. Müller (Ed.), Fundamental Issues of Artificial Intelligence (pp. 555–572). Springer International Publishing.

Sandberg, A. (2014). Monte Carlo model of brain emulation development. Retrieved from

Shulman, C., & Bostrom, N. (2014). Embryo Selection for Cognitive Enhancement: Curiosity or Game-changer? Global Policy, 5(1), 85–92.

Stager, C. (2011). Deep Future: The Next 100,000 Years of Life on Earth. Macmillan.

Vinge, V. (1993). The coming technological singularity: How to survive in the post-human era. In Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace. Retrieved from



Today I was at a talk with Robin Hanson to promote his book THE AGE OF EM hosted by the Bay Area Futurists.

As an academic polymath with interests in physics, computer science, and economics, Hanson draws upon his extensive reading across these fields to try to piece together what such a society will look like.

His argument is that in 30 years to a century, there will be a phase transition as mind uploading takes off and the world economy rapidly becomes dominated by “ems” (emulations); human brains running on a silicon substrate, and potentially millions of times faster. Since transport congestion costs aren’t a factor, this em civilization will live in a few very densely populated cities largely composed of cooling pipes and computer hardware. The economy will double once every month, and in a year or two, it will transition to yet another, cardinally different, growth phase and social structure.

I might or might not eventually do a book review, but for now, here is a link to Scott Alexander’s.

Alternatively, this lecture slide summarizes the main points.


A few observations, arguments, and counterarguments from the meeting:

(1) This struck many people as the most counterintuitive assetion, but I agree that wages in the em world should quickly plummet to subsistence levels (which are much lower than for biological organisms). This is probably what will happen eventually with our civilization if there is no “singularity”/transition to a higher growth phase, since fertility preferences are an aspect of personality, and as such, highly heritable. (Come to think of it this is basically what happens to the Imperium of Man in Warhammer 40k, down to the hive cities in which most citizens eke out “lives of quiet desperation,” though ones which “can still be worth living.”)

Since Ctrl-C Ctrl-V is much easier and quicker than biological reproduction, a regression to the historical (and zoological) norm that that is the Malthusian trap seems – barring some kind of singleton enforcing global restrictions on reproduction – seems inevitable.

(2) A more questionable claim is Hanson’s prediction that ems will tend to be more religious than humans, on the basis that hardworking people – that is, the sorts of people whose minds are most likely to be uploaded and then copied far and wide – tend to be more religious. This is true enough, but there is also a strong and well known negative correlation between religiosity and intelligence. Which wins out?

(3) The marginal return on intelligence is extremely high, in both economics and scientific dynamism (Apollo’s Ascent theory). As such, raising the intelligence of individual ems will be of the utmost priority. However, Hanson makes a great deal of the idea that em minds will be a black box, at least in the beginning, and as such largely impenetrable to significant improvement.

My intuition is that this is unlikely. If we develop technology to a level where we can not only copy and upload human minds but provide them with internally consistent virtual reality environments that they can perceive and interact within, it would probably be relatively trivial to build brains with, say 250 billion neurons, instead of the ~86 billion we are currently endowed with and largely limited to by biology (the circulatory system, the birth canal, etc). There is a moderate correlation between just brain volume and intelligence, so its quite likely that drastic gains on the order of multiple S.D.’s can be attained just by the (relatively cheap) method of doubling or tripling the size of the connectome. The creative and scientific potential of billions of 300 IQ minds computing millions of times faster than biological brains might be greater than the gap between our current world and that of a chimpanzee troupe in the Central African rainforest.

Two consequences to this. First, progress will if anything be even faster than what Hanson projects; direct intelligence amplification in tandem with electronic reproduction might mean going straight to the technological singularity. Second, it might even help ems avoid the Malthusian trap, which is probably a good thing from an ethical perspective. If waiting for technological developments that augment your own intelligence turns out to be more adaptive than making copies of yourself like Agent Smith in The Matrix until us ems are all on a subsistence wage, then the Malthusian trap could be avoided.

(4) I find this entire scenario to be extremely unlikely. In both his book and his lecture, Hanson discusses and then quickly dismisses the likelihood of superintelligence first being attained through research in AI and neural nets.

There are two problems with this assertion:

(a) The median forecast in Bostrom’s Superintelligence is for High Level Machine Intelligence to be attained at around 2050. (I am skeptical about this for reasons intrinsic to Apollo’s Ascent theory, but absolutely the same constraints would apply to developing brain emulation technology).

(b) The current state of AI research is much more impressive than brain emulation. The apex of modern AI research can beat the world’s best Go players, several years ahead of schedule. In contrast, we only finished modeling the 302 neuron brain of the c. elegans worm a few years ago. Even today, we cannot obtain functional models even of 40 year old microchips from scanning them, to say nothing of biological organisms. That the gap will not only be closed but for the brain emulation route to take the lead is a rather formidable leap of faith.

Now to be fair to Hanson, he did explicitly state that he does not regard the Age of Em as a certain or even a highly probable future. His criterion for analyzing a future scenario is for it to have at least a 1% chance of happening, and he believes that the Age of Em easily fulfills that condition. Personally I suspect it’s a lot less than 1%. Then again, Hanson knows a lot more computer science than I do, and in any case even if the predictions fail to pan out he has still managed to provide ample fodder for science fiction writers.

(5) My question to Hanson during the Q&A section of the talk: Which regions/entities do you expect to form the first em communities? And what are the geopolitical ramifications in these last years of “human” civilization?

(a) The big factors he lists are the following:

  • Access to cold water, or a cold climate in general, for cooling purposes.
  • Proximity to big human cities for servicing human customers (at least in the initial stages before the em economy becomes largely autonomous).
  • Low regulations.

So plausible candidates (according to Hanson) would be Scandinavia, or the “northern regions of China.”

As he also noted at another point, in the early stages of em creation technology, mind uploading is likely to be “destructive,” i.e. resulting in the biological death of the person who is to be emulated. So there might be an extra selection filter for state or corporate ruthlessness.

(b) In domestic and social terms, during the transition period, humans can be expected to “retire” as the em economy explodes and soon far exceeds the scope of the old human economy. Those humans who control a slice of the em economy will become very rich, while those who don’t… fare less well.

However, Hanson doesn’t have anything to say on the geopolitical aspects of the transition period because it is much less predictable than the “equilibrium state” of the em economy that he set out to describe. As such, he does not think it is worthwhile for someone who is not a sci-fi writer to delve into that particular issue. That makes sense.

(6) As a couple of people pointed out, atomic weapons can wipe out an entire em “city,” which contain billions of ems.

What would em warfare be like? The obvious answer is cyber-cyber-cyber we gotta hack the mainframe style stuff. But surely, sometimes, the easiest move is to just knock over the table and beat your opponent to death with the chessboard.

If Pinker gets pwned during the em era and global nuclear wars between em hive cities ruled by Gandhi emulations break out, could this make em hive cities unviable and result in a radical decentralization?

(7) How did Hanson become Hanson?

He repeated the Talebian argument (which I sympathize with) that following the news is a pointless waste of time.

It is much more productive to read books, especially textbooks, and to take introductory classes in a wide range of subjects. To try to get a good grasp on our civilization’s system of knowledge, so that you might be able to make productive observations once you reach your 50s.

Confirmation bias? Regardless, it’s one more small piece of evidence in favor of my decision to log off.

• Category: Science • Tags: Futurism, Superintelligence, The AK 

In recent years there has been a surge in interest in gut flora in the wake of research on its substantial effects on personality, so much so that researchers have even taken to describing it as a neutral network.

And much like humans, and even their brains, they are not going to be an exception to recent evolution.

As Chris Kresser writes:

In other words, evolution does not act solely on your 23,000 human genes. Rather, it acts on the 9.02 million genes (both host and microbial) that are present in and on your body, as a single entity.

Moreover, the microbiome can introduce genetic variation and evolve through methods specific to it, such as sharing genes with each other and acquisition of new strains from the environment. And even the borders between bacterial genes and “human” genes are surprisingly porous.

The really interesting observation is yet to come:

Social behavior in primates is also thought to be a critical factor in the evolution of human intelligence (32). Access to microbes may have been a driving force in the evolution of animal sociality, since microbes confer many benefits to the host (33). Social behaviors like grooming, kissing, and sex increased the transfer of microbes from one organism to another. Studies in social mammals have found that development of the forebrain and neocortex in social mammals depends on signals from the microbiota (34), and germ-free mice that lack a microbiota also lack social behavior and show deficits in social cognitive abilities (35).

Depending on the size of these effects there could be some pretty important implications and confounds for psychometrics and genetics of IQ research.

Bacterial composition, for instance, though strongly hereditary, is also going to be affected by the food one eats (a cultural factor), the people with whom one has close contacts with (kissing, certain intimate contacts, and one supposes, effluence in non-hygienic countries), and the local geography, elevation, and climate. Could intelligence be a matter of not just blood and chance, but of soil?

Best not to get too carried away with yet. This paper finds that spousal partners did not have significantly more microbiome similarity than unrelated invididuals (though the sample sizes were small). Still, it might be worth bearing in mind.

• Category: Science • Tags: Ancestral Health, Intelligence 

Last month there was an interview with Eliezer Yudkowsky, the rationalist philosopher and successful Harry Potter fanfic writer who heads the world’s foremost research outfit dedicated to figuring out ways in which a future runaway computer superintelligence could be made to refrain from murdering us all.

It’s really pretty interestingl. It contains a nice explication of Bayes, what Eliezer would do if he were to be World Dictator, his thoughts on the Singularity, justification of immortality, and thoughts on how to balance mosquito nets against the risk of genocidal Skynet from an Effective Altruism perspective.

That said, the reason I am making a separate post for this is that here at last Yudkowsky gives a more more or less concrete definition of what conditions a superintelligence “explosion” would have to satisfy in order to be considered as such:

Suppose we get to the point where there’s an AI smart enough to do the same kind of work that humans do in making the AI smarter; it can tweak itself, it can do computer science, it can invent new algorithms. It can self-improve. What happens after that — does it become even smarter, see even more improvements, and rapidly gain capability up to some very high limit? Or does nothing much exciting happen?

It could be that, (A), self-improvements of size δ tend to make the AI sufficiently smarter that it can go back and find new potential self-improvements of size k ⋅ δ and that k is greater than one, and this continues for a sufficiently extended regime that there’s a rapid cascade of self-improvements leading up to superintelligence; what I. J. Good called the intelligence explosion. Or it could be that, (B), k is less than one or that all regimes like this are small and don’t lead up to superintelligence, or that superintelligence is impossible, and you get a fizzle instead of an explosion. Which is true, A or B? If you actually built an AI at some particular level of intelligence and it actually tried to do that, something would actually happen out there in the empirical real world, and that event would be determined by background facts about the landscape of algorithms and attainable improvements.

You can’t get solid information about that event by psychoanalyzing people. It’s exactly the sort of thing that Bayes’s Theorem tells us is the equivalent of trying to run a car without fuel. Some people will be escapist regardless of the true values on the hidden variables of computer science, so observing some people being escapist isn’t strong evidence, even if it might make you feel like you want to disaffiliate with a belief or something.

Psychoanalyzing people might not be so useful, but trying to understand the relationship between cognitive capacity and technological progress is another matter.

I am fairly sure that k<1 for the banal reason that more advanced technologies need exponentially more and more cognitive capacity – intelligence, IQ – to develop. Critically, there is no reason this wouldn’t apply to cognitive-enhancing technologies themselves. In fact, it would be extremely strange – and extremely dangerous, admittedly – if this consistent pattern in the history of science ceased to hold. (In other words, this is merely an extension of Apollo’s Ascent theory. Technological progress invariably gets harder as you climb up the tech tree, which works against sustained runaway dynamics).

Any putative superintelligence, to continue making breakthoughs at an increasing rate, would have to not only solve ever harder problems as part of the process of constantly upgrading itself but to also create and/or “enslave” an exponentially increasing amount of computing power and task it to the near exclusive goal of improving itself and prevent rival superintelligences from copying its advances in what will surely be a far more integrated noosphere by 2050 or 2100 or if/whenever this scenario happens. I just don’t find it very plausible our malevolent superintelligence will be able to fulfill all of those conditions. Though admittedly, if this theory is wrong, then there will be nobody left to point it out anyway.

• Category: Science • Tags: Apollo's Ascent, Rationality, Superintelligence 

Latest data from NASA:


At +1.35C, this is the biggest monthly temperature anomaly (measured from the base period of 1951-1980) ever measured, and it is a near certainty now that 2016 will be warmer overall than 2015, making for a third-time consecutive record breaking year.

There are several reasons for this:

(1) The El Nino effect. This year’s is a pretty strong one as far as they go, but not quite as strong as the one in 1997-1998, which produced the last major local peak and formed the lynchpin of GW denier arguments throughout the 2000s. Nonetheless, average global temperatures in February 2016 were almost half a degree higher than the +0.88C anomaly seen in February 1998. The most comparably strong El Nino before that was the 1982-1983 one, but the February 1983 anomaly was fairly unremarkle at +0.40C. That’s a difference of almost a degree between then and now.

solar-irradiance(2) Solar irradiance is actually pretty weak relative to its average in the 1950-2000 period so that can’t be part of the explanation.

(3) I wonder to what extent if any the major recent uptick in methane emissions from melting permafrost, which has expressed itself in the form of some spectacular new craters in Northern Siberia last year, has contributed to this.

All in all, this is very bad news for the international community’s target of limiting global warming to the IPCC’s two degrees injunction.

There have been some encouraging counter developments – for instance, global carbon emissions actually fel l in 2015 – but celebrations are premature since there have been plenty of prior periods when global CO2 emissions fell not just for one year but several years in a row: 1973-1975 (first oil shock), 1980-83 (second oil shock), 1989-1994 (collapse of the highly energy-inefficient Communist economies), and 2008-2009 (the Great Recession).

In any case, if the aforementioned methane release scenario is at or close to the runaway threshold, that wouldn’t really matter all that much anyway.

For myself I have always been skeptical that this particular drifting oil tanker could be stopped in time to avert serious levels of warming. I still stand by my 2010 prediction that “geoengineering” is going to start appearing on normies’ vocabularies sooner rather than later, and perhaps implementation of some geoengineering schemes will begin as early as the 2030s. It’s unlikely to be a happy project that brings everyone together. I suspect it’s more likely to either take the form of a ruinous geopolitical free-for-all, or to catalyze the consolidation of today’s already incipient globalist elite into a stiffling singleton.

• Category: Science • Tags: Geoengineering, Global Warming 


The heroes of Hikaru’s Go were off by 86 years.

As some of you might have heard, the word of go – or weiqi as it is known in its homeland of China – is currently undergoing its Deep Blue moment as one of the world’s strongest players Lee Sedol faces off against Google’s DeepMind AlphaGo project. Deep Blue was the IBM/Carnegie Mellon supercomputer that in 1997 beat the world’s top grandmaster Gary Kasparov in a series of 6 chess games. But the computer’s margin of victory at 3.5 to 2.5 was modest, and the event was dogged by Kasparov’s allegations that the IBM team had underhandedly helped the computer. It would be an entire decade before the top computer chess programs decisively overtook the top human players. As of today, there is a 563 point difference between the Elo rating of Magnus Carlsen, the current highest rated human player on the FIDE’s database, and the world’s most powerful chess program, the open source Stockfish 7. In practical terms, this means that Carlsen can expect to win fewer than one in a hundred games against the Stockfish running on a contemporary 64-bit quadcore CPU.

In terms of game complexity, more orders of magnitude separate go from chess than chess from draughts, a game that has been fully solved. The aim is to capture territory and enemy stones by encircling them while defending your own turf, both of which are tallied up at the end of the game with the winner being the one with the most points. It is played on a 19×19 board, a lot larger than the 8×8 arrangement of chess, and you can position your pieces – or stones – on any empty space not occupied by or completely encircled by the enemy, whereas the range of possible moves in chess is strongly constricted. Chess is tactics, go is logistics; chess is combined arms, go is encirclements; chess draws strongly upon algorithmic and combinatorial thinking, whereas go is more about pattern matching and “intuition.” Therefore it is not surprising that until recently it was common wisdom that it would be many decades before computers would start beating the world’s top human players. The unimpressive performance of existing go computer programs, and the slowdown or end of Moore’s Law in the past few years, would have only given weight to that pessimistic assessment. (Or perhaps optimistic one, if you’re with MIRI). Lee Sedol himself thought the main question would be whether he would beat AlphaGo by 5-0 or 4-1.

Which makes it all the more remarkable that Lee Sedol is not just behind but having lost all of his three games so far is getting positively rekt.

But apparently Lee’s confidence was more rational than hubris. He had watched AlphaGo playing against weaker players, in which it made some apparent mistakes. But as a DeepMind research scientist noted, this was actually feature, not bug:

As Graepel explained, AlphaGo does not attempt to maximize its points or its margin of victory. It tries to maximize its probability of winning. So, Graepel said, if AlphaGo must choose between a scenario where it will win by 20 points with 80 percent probability and another where it will win by 1 and a half points with 99 percent probability, it will choose the latter. Thus, late in Game One, the system made some moves that Redmond considered mistakes—“slow” in his terminology. These moves seemed to give up points, but from where Graepel was sitting, AlphaGo was merely trying to maximize its chances.

In other words, while the projected points on the board – territory held plus stones captured – might for a long time appear to be roughly equal, at the same time the probability of ultimate victory would inexorably shift against Lee Sedol. And capped as our human IQs are, not only Lee but all the rest of us might be simply incapable of discerning the deeper strategies in play: “And so we boldly go – into the whirling knives” (to borrow from Nick Bostrom’s book on the risks of computer superintelligence).

Those are in fact the exact terms in which AI scientist/existential risks researcher Eliezer Yudkowsky analyzed this game in a lengthy Facebook post:

At this point it seems likely that Sedol is actually far outclassed by a superhuman player. The suspicion is that since AlphaGo plays purely for *probability of long-term victory* rather than playing for points, the fight against Sedol generates boards that can falsely appear to a human to be balanced even as Sedol’s probability of victory diminishes. The 8p and 9p pros who analyzed games 1 and 2 and thought the flow of a seemingly Sedol-favoring game ‘eventually’ shifted to AlphaGo later, may simply have failed to read the board’s true state. The reality may be a slow, steady diminishment of Sedol’s win probability as the game goes on and Sedol makes subtly imperfect moves that *humans* think result in even-looking boards.

For all we know from what we’ve seen, AlphaGo could win even if Sedol were allowed a one-stone handicap. But AlphaGo’s strength isn’t visible to us – because human pros don’t understand the meaning of AlphaGo’s moves; and because AlphaGo doesn’t care how many points it wins by, it just wants to be utterly certain of winning by at least 0.5 points.

In the third game, which finished just a few hours ago – by the way, you can watch the remaining two games live at the DeepMind YouTube channel, though make sure to learn the rules beforehand or it will be very boring – Lee Sedol, by then far behind on points, made a desperate ploy to salvage the game (or more likely just use the opportunity to test AlphaGo’s capabilities) by initiating a ko fight. A ko is a special case in go in which a local altercation sharply becomes the fulcrum around which the outcome of the entire game might be decided. Making the winning moves requires perfect, precise play as opposed to AlphaGo’s key method of playing out billions of random games and choosing the one which results in the most captured territory after n moves.

But AlphaGo handled the ko situation with aplomb, and Lee had to resign.

The Korean Lee Sedol is the fourth highest rated go player on the planet. But even as of March 9, were it a person, AlphaGo would have already displaced him. The top player in the world is the Chinese Ke Jie, who is currently 100 Elo points higher than Lee. According to my calculations, this implies that Lee should win slightly more than a third of his matches against Ke Jie. His actual record is 2/8, or 25%. Not only is his current tally against AlphaGo is 0/3, but he was beaten by a considerable number of points by an entity that is perfectly content to minimize its lead in order to to maximize its winning probability.

will-lee-sedol-defeat-alphago Finally, a live predictions market on whether Lee Sedol would defeat AlphaGo in any of the three games remaining (that is, before the third match) varied between 20%-25%, implying that the probability of him winning any one game against the the DeepMind monster was less than 10%. (If anything, those probabilities would be even lower now that AlphaGo has demonstrated ko isn’t its Achilles heel, but let us set that aside).

According to my calculations, IF this predictions market is accurate, it would imply that AlphaGo has a ~400-450 Elo point superiority over Lee Sedol based on its performance up to and including the first two games against him.

It would also mean it would be far ahead of Ke Jie, who is the highest ranked human player ever and is currently virtually at his peak. Whereas Lee can only be expected to win 7%-9% of his games against AlphaGo, for Ke Jie this figure would be only modestly higher at 12%-15%. But in principle I see no reason why AlphaGo’s capabilities couldn’t be even higher than that. It’s a long tail – and we can’t see all that far ahead!

But really the most astounding element of this is that what took chess computing a decade to accomplish increasingly appears to have occured in the space of a few days with AlphaGo – despite the slowdown in Moore’s Law in recent years, and the problems of go being far more challenging than those of chess in terms of traditional AI approaches.

For all intents and purposes AI has entered the superhuman realm in a problem space where merely human intelligence had hitherto ruled supreme, and even though we are as far away as ever from discovering the “Hand of God” – the metaphorical perfect game, which will take longer than the lifetime of the universe to compute if all of the universe were to become computronium – we might well be starting the construction of a Sliver of Him.

Update -

Lee won the fourth game!

A win rate of 25% means that AlphaGo’s Elo likely superiority over Lee’s current 3519 points has just plummeted from 400-450 (based on predictions market) to 191, i.e. 3710. Still higher than top player Ke Jie at 3621.

If Lee loses the next game, that Elo difference goes up to 241; if he wins, it gets reduced further to 120. Regardless, we can now say with considerable confidence that AlphaGo is peak human level but decidedly not superhuman level.

Update 2 -

Final remarks:

Was writing article instead of watching final Lee-AlphaGo game but final score is 4:1. Reverse of what Lee had originally predicted! ;)

Anyhow 4:1 score (w/out looking into details) implies Alpha has *probabilistic* ~240 point higher Elo rating than Lee Sedol i.e. ~3760.

That means its likely ~140 points higher than first ranked human Ke Jie and should beat him about 70% of the time.

I had a look at go bots historic performance other day. Looks like they move up by 1 S.D. every two years or so. Treating AlphaGo as the new base, humans should be *completely* outclassed by computer in go by around 2020.

• Category: Science • Tags: Game, Supercomputers 

Prolific IQ researcher Richard Lynn together with two Russian collaborators have recently published arguing that multiple aspects of socio-economic development – infant mortality, fertility, stature, and literacy-as-a-proxy for intelligence were significantly intercorrelated in late Tsarist Russia.


Literate rate of the European part of the Russian Empire in 1897.

Here is the link to the paper – Regional differences in intelligence, infant mortality, stature and fertility in European Russia in the late nineteenth century

And here is a summary by James Thompson – 50 Russian oblasts.

To the right: Here’s your map, JayMan. You’re welcome.

The main potential sticking point:

There are no data for regional intelligence in the nineteenth century and we have therefore adopted rates of literacy as a proxy for intelligence. This is justified on the grounds that a high correlation between literacy rates and intelligence have been reported in a number of studies. For example, a correlation of .861 between literacy rates for Italian regions in 1880 and early twenty-first century IQs has been reported by Lynn (2010); a correlation of .83 between literacy rates for Spanish regions in the early twenty-first century has been reported by Lynn (2010); (Lynn, 2012); and a correlation of 0.56 between literacy rates and IQs for the states and union territories of India in 2011 has been reported by Lynn and Yadav (2015). There is additional support for using literacy in the nineteenth century as a proxy for intelligence in the results of a study by Grigoriev, Lapteva and Ushakov (Григорьев, Лаптева, Ушаков, 2015) showing a correlation of .58 between literacy rates of the peasant populations of the districts (uezds) of the Moscow province in 1883 and the results of the Unified State Exam and State Certification on Russian Language in the districts of the contemporary Moscow oblast.

The methodology at first struck me as being rather problematic.

I’ve read a bit about Russian state literacy programs in the 19th century (National Literacy Campaigns and Movements) and one of their main features is that they tended to spread out from the central European Russian provinces due to cost effectiveness reasons, hence the low literacy rates of e.g. Siberia in Lynn’s data set. However, there is no particular evidence that Siberian Russians are any duller than average Russians. To the contrary, some 3% of Siberian schoolchildren become “Olympians” – high performers who qualify for highly subsidized higher education. This proportion is lower than the 15% of the central region (which hosts Moscow, Russia’s main cognitive cluster with a 107 average IQ), and the 14% of the north-west region (which hosts Russia’s second cognitive cluster with a 103 average IQ Saint-Petersburg, plus the Russians there are probably slightly brighter in general on account of Finno-Ugric admixture), but is considerably higher than in any other Russian Federal District: The Urals and Volga (both about 2%), and the Far Eastern, Southern, and Caucasus (all considerably below 1%).

In other words, would such a historical literacy – modern intelligence correlation apply to Russia as it does to Italy, Spain, and to a lesser extent, India?


Average 2009 PISA results by Russian region.

Fortunately, we don’t have to postulate, since we do actually have PISA data for many Russian provinces that I revealed back in 2012.

This allows us to test if Lynn’s assumptions apply.

There are difficulties, to be sure. Not all Russian provinces were tested in PISA, and there is, needless to say, no data for any of the Ukrainian and Polish oblasts, or for Belarus. As such, only 20 Russian provinces could be tested in this manner (26 if you also include now independent countries excluding Russia itself).

In some cases, names have changed, typically to honor some faceless Soviet bureaucrat; in more problematic cases, borders have changed significantly (e.g. the five provinces of Estonia, Livonia, Courland, Kovno, and Vilna have become the three countries of Estonia, Latvia, and Lithuania – I have tried to average the literacy figures between them in a common sense but back of the envelope way). The Moscow Governorate has been split into the City of Moscow (with its 107 average IQ) and Moscow oblast (with a modest 96 average IQ). Which of those should be attached to Moscow’s 1897 literacy rate of 40%? (As it happens, I went with just the City of Moscow instead of figuring out how to weigh the populations and adjust and so forth. I’m not trying to writea formal paper, after all).


There is an exponential correlation of R=0.75 between average PISA derived IQs of Russian regions and of now independent countries, and their literacy rate according to the 1897 Census. Therefore, this bears out Lynn’s assumptions.

The two downwards outliers – more relatively intelligent than literate – are Moscow, Tatarstan, Tula, Samara, and Tambov. Moscow is easily explainable – the city itself in Tsarist times would have been more literate than the Moscow Governorate, while its average IQ was artificially boosted in Soviet times since it became not just the empire’s political but also its cognitive (artistic, scientific) capital. Getting a Moscow propiska required considerable intelligence.

The three very major upwards outliers – more relatively literate than intelligent – are the Finno-Ugric Baltic states: Finland, Estonia, and Latvia. This can’t have been a non-Orthodox/Muslim thing: Both Poland (On-The-Vistula Governorate) and Lithuania (Kovno and Vilna) lie neatly on the correlation curve. Nor was it something Finno-Ugric; Karelia (then Olonets) is not an exception either. It must have been something specific just to them and the most obvious explanation is Protestantism. There is a lot of literature on the independent literacy-raising effects of Protestantism and I see no reasons why Estonia, Latvia, and Finland should have been exceptions to that.

Another outlier, though this one is at the bottom of the IQ scale, is Moldova. To be fair I think Moldova’s PISA-derived IQ is artificially lowered by a third to half of an S.D. due to the massive brain drain it has experienced after the collapse of the Soviet Union (something like half the working age population are Gastarbeiters in the EU and Russia). We see similar drops in other countries so afflicted, such as (possibly) Puerto Rico, and (almost certainly) Ireland during most of the 20th century, when it repeatedly reported IQs in the ~90 range (and ironically one of the reasons Richard Lynn himself abandoned it to move to Northern Ireland, thus getting stuck in the most depressed region of the UK and missing out on the rise of the Celtic Tiger a few years later).


The correlation improves further to R=0.80 when we consider only those Tsarist-era provinces which are still part of the Russian Federation. This is accomplished (more than) entirely just by removing the Protestant Baltic nations (Finland, Estonia, and Latvia) and Moldova (whose current day average IQ is depressed due to massive brain drain as per above).

As usual Lynn does his north/south IQ gradient analysis, finding it to be a real thing but diminishing to nothing once the Baltic states of Estonia, Livonia, and Courland are accounted for.

Quoting from Thompson’s summary:

The Russian provinces differed significantly by geographical location. The positive correlations with latitude (r= .33, p<.05) and the negative correlation with longitude (r=−.43, p<.01) show that the rates of literacy were higher in the northand west than in the south and east. These trends were partly determined by the rates of literacy being highest in the north-western provinces of St. Petersburg and the three Baltic states of Estland, Livland and Kourland (correspondingapproximately but not precisely to contemporary Estonia and Latvia; Livland consisted of southern part of contemporary Estonia and eastern part of contemporary Latvia). Removing these four regions makes both correlations non-significant (.21 and −.23).


The Pale of Settlement in 1897.

One additional issue worth bearing in mind: The influence of the Jews. Namely, their concentration in the Pale of Settlement, which correlates to modern day Poland, Belarus, and right-bank Ukraine (west of the Dnieper). There were more than 5.2 million Jews, and their literacy rates were very high (according to the 1926 Soviet Census, Jews over the age of 50 – i.e., who had been educated under the Empire – had a literacy rate of 63% versus 28% for ethnic Russians).

This must have “artificially” raised the literacy rates in this area – as pertains to those regions’ 21st century average IQs, anyway, since the vast majority of those Jews are no longer there due to the trifecta of the Holocaust, Jackson-Vanik, and Aliyah. The effect would probably be to reduce the “indigenous” literacy rates in Lithuania and Poland closer to those of European Russia, while pushing the already low literacy rates of strongly ethnic Malorossiyan and Belorussian provinces considerably lower still. Not a single province of modern Ukraine outside historical Novorossiya (with its strong Great Russian admixture) had a literacy rate above 20% in 1897, despite highly literate Jews helping them out with the statistics.

Unfortunately, there is a severe paucity of usable psychometric data from Ukraine – for instance, it is one of the very few European countries that doesn’t participate in PISA. So its average IQ has to be estimated through generally more indirect means. It does the converted equivalent of 9 IQ points worse than Russia on the TIMSS standardized test. Ukrainians spend less than half as much time as Russians reading, and those from the western parts at least spend a lot more time participating in torchlit processions and chanting “Putin Khuylo.” Some of those activities are considerably more g loaded than others. The low literacy rates in late Tsarist Malorossiya, coupled with the finding of a close correlation between those literacy rates and modern day average IQ across both Russian provinces and today’s independent post-Soviet states, constitutes further evidence of a modest average IQ in Ukraine. Higher than in Moldova to be sure, but probably closer to the level of the Balkans than to Poland.


Sources: Grigoriev, Lapteva, and Lynn 2015; Karlin 2012 (derived from PISA 2009).

IQ Literacy in 1897
Astrakhan 94.8 15.5%
Bashkortostan 93.4 16.7%
ESTONIA 102.1 77.9%
FINLAND 106.6 75.6%
Kaluga 91.7 19.4%
Karelia 98.1 25.3%
Kursk 94.6 16.3%
LATVIA 98.0 74.3%
LITHUANIA 99.0 35.4%
MOLDOVA 84.9 15.6%
Moscow 106.6 40.2%
N. Novgorod 93.1 22.0%
Orenburg 92.7 20.4%
Perm 93.3 19.2%
POLAND 100.2 30.5%
RUSSIA 96.0 21.1%
Ryazan 94.7 20.3%
Saint-Petersburg 102.6 51.5%
Samara 99.2 22.1%
Saratov 96.0 23.8%
Tambov 95.9 16.6%
Tatarstan 98.1 17.9%
Tula 98.6 20.7%
Ulyanovsk 91.5 15.6%
Vladimir 98.9 27.0%
Vologda 95.3 19.1%
Voronezh 92.7 16.3%

Literacy and Social Development in 1890s Russia (from Grigoriev et al. 2015)

Incidentally, I am not surprised to see Yaroslavl being the top non-Baltic/non-capital Russian region by literacy rate in 1897. It struck me as by far the cleanest and most civilized provincial Russian town on the Golden Ring when I visited it in 2002 (a time when Russia was still shaking off the hangover of the Soviet collapse). Curiously enough, it also hosted one of the most vigorous insurrections against the Bolshevik regime in central Russia. Although it was not one of the regions covered by PISA, I would not be surprised if Yaroslavl oblast was to get a 100-102 score on it should it be carried out there (and as would be implied by the correlation curve).



book-human-accomplishment Charles Murray has made the entire database compiled for his book Human Accomplishment freely available at the Open Science Framework.

Here is the link:

Incidentally, my concept of Apollo’s Ascent was to a significant extent the result of my reaction to Human Accomplishment. (A brief reminder of the AA thesis: The rate and global distribution of technological progress is dependent on the absolutely numbers of literate “smart fraction” people available to different societies at different points in history). Although Human Accomplishment was a thoroughly brilliant work, I had some quibbles with its core argument – namely, that Christianity was at the root of Europe’s post-1450 intellectual preeminence.

The Greeks laid the foundation, but it was the transmutation of that foundation by Christianity that gave modern Europe its impetus and differentiated European accomplishment from that of all other cultures around the world.

This was a judgement that Murray appears to have made relatively late in the writing process, and I suspect that as a social scientist he might not have been 100% satisfied – intellectually, at any rate – with ascribing possibly the biggest puzzle in world history to unquantifiable and unfalsifiable “transcendental values.”

After all, purely cultural explanations don’t tend to have a greaat track record in explaining economic success/failure (which are substantially related to intellectual achievement: You need smart fractions both to invent stuff and to have more productive economies). See how Confucianism was first used to explain the stagnation of East Asian societies before 1950, before the historians and sociologists did a 180 and started citing that same Confucianism to explain the success of the East Asian tiger economies when they burst into prominence by the 1980s. I don’t think it’s a particularly wild or radical idea that concrete, quantifiable concepts such as literacy rates and smart fractions would be a more credible explanation. But let the eventual critics of Apollo’s Ascent be the judge of that.

Speaking of Apollo’s Ascent, writing the book will be much easier with access to Charles Murray’s database. It would also be on much firmer theoretical ground, since instead of just highlighting general patterns – it’s not as if I have the time or resources to construct a comprehensive database of human accomplishment by myself – I will also be able to run numerical experiments, e.g. on on the correlation between calculated historical “aggregate mindpower” levels in different countries (aka literate smart fraction people) and their production of eminent figures.

Charles Murray was actually kind enough to email me the HA database a couple of months ago, so this public release is mostly redundant for my own project. But it is a very good thing nonetheless that many more people will now be able to run their own historical and social “experiments” using his data, including those who might earlier have shied at openly requesting it.

It is also part of a general process now underway in which there is growing demand for scientists to make their data publically available as opposed to just on request. To a significant extent I think the reason more scientists don’t yet do this is that the technical means for doing so – especially for older scientists who tend to be less computer savvy – are still few and far between. The Open Science Framework, for instane, only began operations in 2011. So persons such as Emil Kirkegaard who are heavily involved with the opening up of the scientific process – incidentally, it was partly thanks to his timely prodding that the Human Accomplishment data was released – should also be strongly commended.

To go a bit meta, this process – both in its technological and social aspects – is itself an information technology that acts as a multiplier on aggregate mindpower, in the style of Renaissance reading glasses and the Internet. The Flynn Effect has stopped in the developed world, literacy rates are pretty much maxed out, and Apollo’s load almost always gets heavier, not lighter. Just like in the Civilization video games, you need more and more “science points” to generate discoveries as you go up the technology tree. As such, we have to start eking everything we can out of existing technology to keep up the production of our Great Scientists. Shifting to open science paradigms is by far not the worst way of going about this.


The latest data from Top 500, a website that tracks the world’s most powerful supercomputers, has pretty much confirmed this with the release of their November 2015 list.

The world’s most powerful supercomputer, the Tianhe-2 – a Chinese supercomputer, though made on American technology – has now maintained its place for 2.5 years in a row. The US supercomputer Cray XK7 built three years ago maintains its second place today. Relative to June 2013, there has not even been a doubling in aggregate performance, whereas according to the historical trendlines, doublings have typically taken just a bit over a single year to occur. This is unprecedented, since Moore’s Law applies (applied?) to supercomputers just as much as it did to standard electronics.


Apart from serving as a conventient bellweather for general trends, futurists are well advised to follow supercomputers for two reasons.

Technological Projections

Their obvious application to the development of radical technological breakthroughs, from the extraordinarily complex protein folding simulations vital to uncovering medical breakthroughs to the granddaddy of them all, computer superintelligence. The general “techno-optimistist” consensus has long been that Moore’s Law will continue to hold, or even strengthen further, because the Kurzweilian view was that the exponent itself was also (slowly) exponentially increasing. This would bring us an exaflop machine by 2018 and the capability to do full human brain neural simulations soon afterwards by the early 2020s.


But on post-2012 trends, exponentially extrapolated, we will actually be lucky just to hit one exaflop in terms of the aggregate of the world’s top 500 supercomputers by 2018. Now the predictions of the first exaflop supercomputer have moved out to 2023. Though perhaps not much in conventional life, a “delay” of 5 years is a huge deal so far as projections built on big exponents are concerned. For instance, assuming the trend isn’t reversed, the first supercomputer theoretically capable of full neural simulations moves out closer to 2030.

In terms of developing superintelligence, raw computing power has always been viewed as the weakest limit, and that remains a very reasonable view. However, the fact that even in this sphere there appear to be substantial unforeseen obstacles means a lot of trouble for the traditional placement of superintelligence and even the technological singularity at around 2045 or 2050 (not to even mention the 2020s as per Vernor Vinge).

National Power

Supercomputers can also be viewed as an instrument of national power. Indeed, some of the most powerful supercomputers have been used for nuclear testing (in lieu of real life). Other supercomputers are dedicated to modeling the global climate. Doing it better than your competitors can enable you to make better investments, even predict uprisings and civil wars, etc. All very useful from a geopolitical perspective. And of course they are very useful for a range of purely scientific and technological applications.


As in so many spheres in the international arena, the overwhelming story here is of the Rise of China.

From having o-1 supercomputers in the Top 500 during the 1990s and a couple dozen in the 2000s, it surged past a waning Japan in the early 2010s and now accounts for 109 of the world’s top supercomputers, second only after the USA with its 199 supercomputers. This just confirms (if any such confirmations is still needed) that the story of China as nothing more than a low wage workshop is laughably wrong. An economy like that would not need 20%+ of the world’s top supercomputers.

United States 199 39.8 172,582,178 246,058,722 10,733,270
China 109 21.8 88,711,111 189,895,013 9,046,772
Japan 37 7.4 38,438,914 49,400,668 3,487,404
Germany 32 6.4 29,663,941 37,844,201 1,476,524
United Kingdom 18 3.6 11,601,324 14,230,096 724,184
France 18 3.6 12,252,180 14,699,173 766,540
India 11 2.2 4,933,698 6,662,387 236,692
Korea, South 10 2 7,186,952 9,689,205 283,568
Russia 7 1.4 4,736,512 6,951,848 208,844
Brazil 6 1.2 2,012,268 2,722,150 119,280

Otherwise the rankings are approximately as one might expect, with the Big 4 middle sized developed Powers (Japan, Germany, UK, France) performing modestly well relative to the size of their population and the rest – including tthe non-China BRICS – being almost minnows in comparison.


Work shouldn’t start until 10am and school even later, says sleep expert

Paul Kelley of Oxford University’s Sleep and Circadian Neuroscience Institute says society is in the midst of a sleep-deprivation crisis, as the working hours we force ourselves to adapt to are often unnatural and unsuitable for our internal body clocks. …

He advocates 8:30am starts for children aged eight to 10, 10am starts for 16-year-olds and 11am lessons for 18-year-olds.

“At the age of 10 you get up and go to school and it fits in with our 9-to–5 lifestyle,” Kelley said. “When you are about 55 you also settle into the same pattern. But in between it changes a huge amount and, depending on your age, you really need to be starting around 3 hours later, which is entirely natural.”

If Kelley’s right, what this effectively means is that our whole lives from the onset of our teen years through to the end of middle age are like being woken up too early. Every. Single. Day.

““Staff should start at 10am… Staff are usually sleep-deprived,” Kelly told the British Science Festival. “Everybody is suffering and they don’t have to. We cannot change out 24-hour rhythms.”

Can’t agree more. The 9-5 workday is a structural microaggression against people who identify as night owls like myself.

*warning: crappy evopsych theorizing follows*

In ancestral times, you didn’t want everyone dozing off at the exact same time. It would have made sense for someone to always keep an eye out for predators, enemy bands, etc. It would have been much easier to do so if society had a mix of night owls and early risers, just as they needed both altruists and psychopaths in certain proportions for optimal group survivability.

I just don’t feel all that great waking up very early in the morning, even if had a perfectly adequate night’s sleep beforehand. I wonder if sometime in the next decade science will show that the near universal advice to go to bed early and wake up early regardless of personal psychology will go the way the medical community old imprecations against salt, eggs, and butter.

There appear to be some people, typically very energetic ones, who don’t seem to need very much sleep at all. Elon Musk seems to be one of them, I suspect Razib Khan is as well.

As Peter Frost reported a few months back, African-Americans need on average one hour less sleep than European Americans. Assuming sleep is essentially just a way of keeping energy expenditures down when they’re not needed (humans can’t hunt or gather at night) it stands to reason that northern peoples would sleep more on average. Of course this is has not been germane since the industrial revolution and I for one would be happy to see the need for sleep (bio)engineered away altogether.

• Category: Science • Tags: Sleep 

The cultural and scientific achievements of Ancient Greece are so manifold that it is barely worth recounting them. Socrates, Plato and Aristotle laid the foundations of Western philosophy. Pythogoras, Euclid, and Archimedes launched mathematics as a disciple grounded on logic and proof, a break from the approximative techniques that had held sway in other civilizations (and would largely continue to do so). To this day many medical schools have their students swear an oath under the name of Hippocrates. Homer, Aeschylus, Euripides – the originators of, and still giants in, the Western literary canon. Herodotus and Thucydides, the founders of a historiography that was something more than just a court chronicle.

Ancient Greek IQ = 125 (Galton)

Bearing in mind the very small population from which these intellectual giants were drawn – at its height, Ancient Athens had no more than 50,000 male citizens – it is little wonder that many thinkers and historians have posited a very high average IQ to the ancient Greeks, including most recently evolutionary psychologist Gregory Cochran. But the argument was perhaps best stated by the Victorian polymath and inventor of psychometrics Francis Galton, in the (not very politically correctly titled) “Comparative Worth of Different Races” chapter of his book Hereditary Genius:

The ablest race of whom history bears record is unquestionably the ancient Greek, partly because their master-pieces in the principal departments of intellectual activity are still unsurpassed, and in many respects unequalled, and partly because the population that gave birth to the creators of those master-pieces was very small. Of the various Greek sub-races, that of Attica was the ablest, and she was no doubt largely indebted to the following cause, for her superiority. Athens opened her arms to immigrants, but not indiscriminately, for her social life was such that none but very able men could take any pleasure in it; on the other hand, she offered attractions such as men of the highest ability and culture could find in no other city. Thus, by a system of partly unconscious selection, she built up a magnificent breed of human animals, which, in the space of one century—viz. between 530 and 430 B.C.—produced the following illustrious persons, fourteen in number:—

Statesmen and Commanders.—Themistocles (mother an alien), Miltiades, Aristeides, Cimon (son of Miltiades), Pericles (son of Xanthippus, the victor at Mycalc).
Literary and Scientific Men.—Thucydides, Socrates, Xenophon, Plato.
Poets.— Aeschylus, Sophocles, Euripides, Aristophanes.

We are able to make a closely-approximate estimate of the population that produced these men, because the number of the inhabitants of Attica has been a matter of frequent inquiry, and critics appear at length to be quite agreed in the general results. It seems that the little district of Attica contained, during its most flourishing period (Smith’s Class. Geog. Dict.), less than 90,000 native free-born persons, 40,000 resident aliens, and a labouring and artisan population of 400,000 slaves. The first item is the only one that concerns us here, namely, the 90,000 free-born persons. Again, the common estimate that population renews itself three times in a century is very close to the truth, and may be accepted in the present case. Consequently, we have to deal with a total population of 270,000 free-born persons, or 135,000 males, born in the century I have named. Of these, about one-half, or 67.500, would survive the age of 26, and one-third, or 45,000, would survive that of 50. As 14 Athenians became illustrious, the selection is only as I to 4,822 in respect to the former limitation, and as I to 3, 214 in respect to the latter. Referring to the table in page 34, it will be seen that this degree of selection corresponds very fairly to the classes F (1 in 4, 300) and above, of the Athenian race. Again, as G is one-sixteenth or one-seventeenth as numerous as F, it would be reasonable to expect to find one of class G among the fourteen; we might, however, by accident, meet with two, three, or even four of that class— say Pericles, Socrates, Plato, and Phidias.

Now let us attempt to compare the Athenian standard of ability with that of our own race and time. We have no men to put by the side of Socrates and Phidias, because the millions of all Europe, breeding as they have done for the subsequent 2,000 years, have never produced their equals. They are, therefore, two or three grades above our G—they might rank as I or J. But, supposing we do not count them at all, saying that some freak of nature acting at that time, may have produced them, what must we say about the rest? Pericles and Plato would rank, I suppose, the one among the greatest of philosophical statesmen, and the other as at least the equal of Lord Bacon. They would, therefore, stand somewhere among our unclassed X, one or two grades above G—let us call them between H and I. All the remainder—the F of the Athenian race— would rank above our G, and equal to or close upon our H. It follows from all this, that the average ability of the Athenian race is, on the lowest possible estimate, very nearly two grades higher than our own—that is, about as much as our race is above that of the African negro. This estimate, which may seem prodigious to some, is confirmed by the quick intelligence and high culture of the Athenian commonalty, before whom literary works were recited, and works of art exhibited, of a far more severe character than could possibly be appreciated by the average of our race, the calibre of whose intellect is easily gauged by a glance at the contents of a railway book-stall.

Francis Galton was writing before the invention of the standard deviation, but in his methodology a “grade” was equivalent to 10.44 IQ points (under an S.D. of 15), so in practice the Athenians had an IQ of perhaps 120 relative to a Victorian British mean of 100. (And presumably, therefore, about 110 relative to the modern Greenwich mean, which is considerably higher than a century ago due to the Flynn Effect).

There are however a few problems with this.

Ancient Greek IQ = 90 (Apollo’s Ascent)

First off, there is no particularly obvious explanation for why this part of the Mediterranean world evolved such a high average IQ – a standard deviation higher than everyone else – in the first place. One would then likewise have to explain why they then lost it so thoroughly that modern Greeks now consistently place lower in European IQ assessments than all but a few Balkan backwaters.

However, it turns out that using the Apollo’s Ascent method of computing aggregate mindpower – with adjustment for the intellectual discovery threshold – as a function of population size, literacy rate, and average IQ can explain the record of Greek achievement just as succinctly without requiring positing superhumanly high average IQ levels which are so dubious from an evolutionary perspective.

Let us treat each of these factors in turn:

Ancient Greek Demography

It is often forgotten that when we are speaking of ancient Greek accomplishment it is more than just a story of Athens, a city that drew the cognitive elites of the entire oikoumene to itself (much as major metropolises like New York, London, Paris, etc. do so today).

To be sure, Athens might have had 50,000 male citizens, and a total population of 250,000-300,000 [CORRECTION: Actually refers to the entire Athenian city-state. Population of just the city was probably about twice less]. But the population of Greece proper was probably at least five times larger, because the total urbanization rate never went much above 20% in any preindustrial country that we know of. Moreover, Greeks were scattered all across the Mediterranean world, in Ionia and Sicily and the shorelines of Egypt, the Italian “boot,” France, Spain, and the Pontic steppe.


Greece: More than just Greece. Source.

According to recent calculations, the total population of Greeks in the 4th century BC was at least 7.5 million, and probably more like 8-10 million (Mogens Herman Hansen in An Update on the Shotgun Method). For perspective, at the time, this represented just under 5% of the world’s population (i.e. remarkably similar to the US today). These figures might still be modest, but they are essentially comparable in magnitude to those of even the biggest preindustrial civilizations (source: Several, but mainly Angus Maddison):

  • Egypt: A consistent 5 million in both Roman and Islamic times
  • Persia: Likewise, around 5 million
  • Roman Empire: 50-60 million (of which 20 million were in the Greek East)
  • Qin China: 22 million in ~210BC (only 2x more than Greek world!)
  • Han China around 1AD: 60 million
  • Byzantine Empire: 10-12 million when it was at its geographical peak
  • Abbasid Caliphate: 30 million
  • Medieval China: 100 million
  • Medieval France: 20 million (most populated W. European country; peak)
  • Renaissance Italy: 10.5 million in 1500

To be sure, many ancient Greeks were slaves and women who were more or less excluded from participating in intellectual endevours. But in that respect they were no different from any other preindustrial civilization that we know of.

Ancient Greek Literacy

In William V. Harris’ Ancient Literacy, he estimates that the literacy rate of late Classical Greece was 5-10%, rising to 10% in the Hellenistic period, and 10-15% in Roman Italy (but considerably lower in the peripheries like Gaul). This might seem very low and it is. But in that period, it was low everywhere; in reality, the literacy rates attained in the classical Mediterranean world were far higher than had been previously seen anywhere else. Because Classical Greece was pretty much the first society in the world (only much smaller Phoenicia could have been even a remote contender) to attain what he calls “craftsman literacy” i.e. around 10%. All previous societies had been limited to the 1-2% rates that he calls “priestly literacy.”

Although he doesn’t spell it out explicitly, the key factor that must have enabled this in my view was the development of the alphabet, which occured first amongst the Phoenicians (who were also respectably creative for their numbers).

It is speculated that the alphabet might have arisen as a result of the intense trading culture of the Phoenicians, which made simplification of the writing system highly adaptive. Due to Greek and Roman influence, Mesopotamian cuneiform and Egyptian hieroglyphs were displaced. In contrast, perhaps by the time trade had reached similarly intensive levels in China – perhaps after the construction of the Grand Canal in the 7th century AD – the characters system was already too embedded in the bureaucracy and was kept on due to a QWERTY effect. However, there might also be an HBD angle. Peter Frost has suggested the spread of the ASPM gene from Middle Eastern origins – large lacking in East Asians, and associated with continuous text processing – could have tipped the scales in favor of the adoption of alphabetic systems in the Near East and the Mediterranean in a way that could not have happened in East Asia. (Note that Korea’s Sejong the Great introduced an alphabetic system in the 15th century, for the express reason of increasing literacy amongst the commonfolk, but it took until the 20th century for it to truly catch on).

Whatever the case, it is a simple fact that learning literacy is incredibly easier with alphabet based systems than character based systems. Learn the 50 or fewer symbols of your typical alphabet and their vocalizations and you are pretty much set; everything else is style and detail. In contrast, you need to know 1,000-1,500 characters just to be considered literate in Chinese (and you would still struggle a great deal even with newspaper texts). An average Chinese college graduate is expected to recognize around 5,000 characters and even they frequently have trouble with some remarkably “straightforward” characters. Here is an anecdote that represents this really well from David Moser’s classic essay Why Chinese is So Damn Hard:

I happened to have a cold that day, and was trying to write a brief note to a friend canceling an appointment that day. I found that I couldn’t remember how to write the character 嚔, as in da penti 打喷嚔 “to sneeze”. I asked my three friends how to write the character, and to my surprise, all three of them simply shrugged in sheepish embarrassment. Not one of them could correctly produce the character. Now, Peking University is usually considered the “Harvard of China”. Can you imagine three Ph.D. students in English at Harvard forgetting how to write the English word “sneeze”?? Yet this state of affairs is by no means uncommon in China.

By medieval times, China had by far the world’s most sophisticated infrastructure for increasing human capital, such as movable type (invented 400 years in advance of Gutenberg), cheap mass produced paper (in contrast, the Mediterranean world had to rely on expensive Egyptian papyru, which put a further limit on mass literacy), the system of meritocratic exams for entry into the Confucian bureaucracy, and a vast network of writing tutors, including free ones (the founder of the Ming dynasty Zhu Yuanzhang was an impoverished orphan who was taught literacy in a Buddhist monastery). Even so, held back by its writing systems, medieval China’s literacy rate was no higher than 10% at best (that was the rate at the close of the Qing dynasty and that came after the beginning of education reforms).

There are some scholars like Evelyn Rawski who argue China’s historical literacy rates were far higher. I addressed them in my Introduction to Apollo’s Ascent article (Ctrl-F for “fish literacy”).

Of course at the time of the Ancient Greeks none of this existed yet in China, so the literacy rate then was probably around 1-2% as was typical of societies with “priestly literacy.” Ergo for the great civilizations of the Middle East before the classical era.

This is common sense, but the point needs to be made regardless: Without literacy, no matter how intelligent you are, you can almost never meaningfully contribute to scientific or cultural progress.

With a literacy rate 5 or even 10 times as high as that of other contemporary civilizations (barring the Romans), their modest demographic preponderance over Greece is put into necessary perspective. To be sure, a literacy rate of 10% might not functionally translate into 5 times as much aggregate mindpower (all else equal) as a 2% literacy rate, because presumably, it is the brightest people who tend to become literate in the first place. On the other hand, however, this was a world of hereditary caste and class, of Plato’s Golds, Silvers, and Bronzes. The advanced cognitive sorting that developed in the US in the second part of the 20th century, as described in detail in Charles Murray’s Bell Curve, was totally unimaginable then. Furthermore, there might be a network effect from having a relatively dense concentration of literate people. I would imagine these two factors substantially or wholly cancel out the effect of diminishing returns to higher literacy in terms of human accomplishment. (If you have any ideas as to how this could be quantified, please feel free to mention it in the comments).

Ancient Greek IQ

As I wrote in Introduction to Apollo’s Ascent, there are a number of factors which have been shown to strongly influence IQ, making it just about feasible to guesstimate them historically.

Some of the most important ones as they pertain to Ancient Greece vs. everyone else are:

  • Nutrition
  • Inbreeding/consanguineous marriage
  • Parasitic Load

It just so happens that so far as all of these are concerned the Greeks hit the jackpot.

Nutrition: The Ancient Greeks were remarkable effective at escaping the Malthusian trap for a preindustrial society. (I am not sure why that was the case. Slavery? Feel free to leave suggestions in the comments).

According to a 2005 paper by Geoffrey Kron, citing Lawrence Angel, the average heights for Classical Greece males are 170.5cm, rising to 171.5cm for Hellenistic Greek males, which is similar to the levels attained by Britain and Germany in the early 20th century, and furthermore, compares very well with the average heights of Greek conscripts in the mid-20th century. The n=927 Roman average from 500BC to 500AD was 168.3cm, and the figures for the Byzantine Empire (at least in Crete) appear to have been similar. Here are some figures for other times and places for comparison from Gregory Clark’s A Farewell to Alms:


In other words, the Ancient Greeks were about as tall as the Georgian British, some of the tallest Europeans at that time, who were on the cusp of permanently escaping the Malthusian trap and were likewise undergoing a remarkable cultural and scientific explosion.

This must have been enabled by a remarkable level of personal prosperity, as expressed in how much grain the average laborer could buy with a day’s wage. Again via Gregory Clark:


The Odyssey is full of people sacrificing ridiculous numbers of bulls. While presumably not to be taken literally, it does probably illustrate that there were no major shortages of animal proteins. (The same certainly could not be said for China, India, or Japan, where diets have always been almost fully dominated by carbohydrates). To be sure the Odyssey takes place in the 8th century BC, but cattle shares in the Mediterranean remained high through the period of Classical Greece and only plunged as Greece transitioned into the Hellenistic period, according to an exhaustive paper by Nikola Koepke:


Additionally, as a seafaring culture, fish and sea products must have played a substantial part in the Greek diet. This would have helped them avoid the iodine deficiency that tends to depress IQ and lead to cretinism in more inland and mountainous areas. Even the very poor who could not afford fish would have used garum, the fish sauce popularized by the Romans but invented by Greeks, to flavor their staples.

Inbreeding: Inbreeding/cousin marriage, especially of the FBD type, directly lowers IQ and to a very large extent. But as prominent blogger hdbchick noticed, the Greeks had begun to outbreed extensively in the Archaic Age:

well, from mitterauer again we have [pg. 69]:

“Greek was the first European language to eliminate the terminological distinction between the father’s and mother’s side, a transition that began as early as between the fifth and third century BC.35

so that’s just at the transition point between archaic greece and classical greece. but starting at least in the early part of the archaic period and lasting throughout to the classical period the archaic greeks were outbreeding! at least the upper class ones were — difficult/impossible to know about the lower classes. from Women in Ancient Greece [pg. 67]:

“Marriages were arranged by the prospective groom and the prospective bride’s guardian, and the wife usually (although not always) went to live with her husband’s family. In the early Archaic Age [800 BC – 480 BC], to judge from the evidence of Homer’s poems (e.g. ‘Odyssey’ 4.5), male members of the upper classes generally married women who were not related to them, and who came from different areas. This upper-class habit of exogamy — marrying outside the community — was related to the political importance which marriage possessed in these circles. Marriage exchanges were one of the means by which noble families created political alliances with groups living in other areas, and in this way they made a considerable contribution to the aristocracy’s stranglehold on power. This practice survived to the end of the Archaic Age. However, with the emergence of the *polis*, exogamy began to give way in some places to endogamy — to marriage within the community. For the upper classes, this meant marriage within a tight circle of aristocratic families living in the same *polis*.”

so there was outbreeding in archaic greece for a few hundred years (at least amongst the upper classes), and, then, eventually — after about 400 years or so — there was a linguistic shift to more general kinship terms which reflected that outbreeding.

Moreover, of Emmanuel Todd’s four main European family systems – nuclear, egalitarian, authoritarian/stem, and communitarian (see Craig Willy’s post for a detailed explanation) – the Ancient Greeks practiced the authoritarian type, in which the eldest son stays with the parents while his siblings leave and inherits most or all of his family’s property.

The authoritarian family system, also seen in regions such as Germany, Sweden, Scotland, the Jews, Korea, and Japan (after ~1500), and substantially in 18th century Britain, seems to be highly eugenic in terms of selection for IQ and longterm planning. This stands to reason. Families with a lot of land/property can breed a lot of children and disperse them into the general population, and when they die, the eldest son who inherits everything can himself repeat the process. Those families who mismanage their affairs and lose land no longer have the resources to produce so many children (surviving ones, at any rate) and thus their contribution to the overall genepool peters out.

This is the opposite of the dynamics involved in communitarian family systems, in which property is divided equally amongst the sons. But all of the major Middle Eastern civilizations, as well as the Etruscan Roman heartlands, were characterized by communitarian family systems (albeit with varying rates of cousin marriage: Low in the Roman world, much higher in the Middle East and especially Egypt, where even brother/sister marriages appear to have been been quite widespread under both the Pharaohs and the Greco-Romans).

In communitarian family systems the eugenic factor is much weaker. Family ties play a big role with associated nepotism and (especially in the most endogamous societies) clannishness. Reproductive success is tied not so much on one’s own capability to use intelligence and planning to create surpluses as on support from the extended family and clan. hbdchick calls this “clannish dysgenics,” though considering that communitarian family systems are the “default” for most of human histor, I would argue it might be more apt to talk of “nuclear/stem family eugenics.” Be as it may, aggregate selection for increased IQ is much weaker.

The ancient Greeks also practiced direct eugenics, exposing physically deformed babies. The Spartans in particular are (in)famous for it. However, this seems to have been more or less universally prevalent in preindustrial history, so I doubt this could have been much of a factor.

Parasitic Load: The Mediterranean climatic and agricultural system made for a (relatively) very salubrious environment, in stark contrast to subtropical environments with their humidity and endemic diseases (e.g. India, South China) and to inland agricultural systems heavily dependent on irrigation, in which large bodies of still water are breeding grounds for all sorts of nasty parasites (most major civilizations outside Europe).

In particular, as noted in Mark Elvin’s The Pattern of the Chinese Past, aggregate parasitic load steadily INCREASED in China over the past two millennia, as its demographic center of gravity shifted inexorably south, which was characterized by irrigated rice growing and high humidity.

As if that wasn’t enough, the Ancient Greeks and other Mediterraneans also had one of the most potent counters to parastitic load in the form of their advanced viniculture. Due to their relative wealth (see above), they could afford a lot of wine, and back then it was usually stronger too.

Aggregate Mindpower in Ancient Greece

And now we can put together the final tally for Ancient Greece:

  • Could draw on a population of ~10 million Greeks (Romans: 50 million; Han Chinese: 60 million; Renaissance Italy: 10 million)
  • Had a literacy rate of 10%. Romans – Also 10%; Chinese – ~2%; Renaissance Italy – about 20% (see Van Zanden et al., 2009).

Some back of the envelope calculations for IQ:

  • Greeks are Caucasoids so let’s take the modern Greenwich mean of 100 as first default approximation, and slightly higher for Mongoloids (Romans: 100; Chinese: 105; Italy: 100)
  • Nutrition (subtract from optimal): Greeks – minus 5; Romans – minus 8; Chinese – minus 10 (would increase later); Italy – minus 5 (was very well fed in the depopulated years after the Black Death).
  • Inbreeding/Family Systems: Greeks – minus o (positive advantage of stem family type cancels out relatively modest incidence of cousin marriage); Romans – minus 2 (exagamous communitarian); Chinese – minus 5 (exagamous communitarian but more cousin marriage than amongst Romans); Italy – minus 0 (egalitarian family system with little cousin marriage thanks to Catholic Church regulations)
  • Parasitic Load: Greeks – minus 5 (let’s say that’s best possible in preindustrial age); Romans – minus 7 (did have more irrigation); Chinese – minus 10; Italy – minus 7
  • Guesstimated IQ: Greeks – 90; Romans – 83; Han Chinese – 80; Renaissance Italy – 88. Incidentally, this would give the Greeks enough of an edge to give substance to ancient stereotypes about their intelligence and craftiness but without having to evoke superhuman IQ levels.

Let us recall some definitions:

Assume that the intellectual output of an average IQ (=100, S.D.=15) young adult Briton in the year 2000 – as good an encapsulation of the “Greenwich mean” of intelligence as any – is equivalent to one nous (1 ν).

This can be used to calculate the aggregate mindpower (M) in a country.


Technological growth c * M(>threshold IQ for new discovery) * literacy rate

Here are some rough calculations:



  • c is information tech multipliers, i.e. things that make scientific/cultural progress easier. A modern example would be the Internet. I gave Renaissance Italy a bonus because of its invention of eyeglasses, which essentially doubled the creative lifespans of skilled artisans (and at the peak of their powers), and the spread of the printing press from the mid-15th century.
  • M is total aggregate mindpower. It does not have much meaning for Malthusian societies, but in the modern world it would generally correlate with total GDP.
  • The other Ms refer to the aggregate mindpower that is above the Greenwich mean to one, two, and three standard deviations respectively. Recall that not even a trillion homo erectus will come up with the calculus; you need to be above a certain threshold to make any progress. Recall also that the discovery threshold is generally 2 S.D. above the mastery threshold.
  • Recall also the assumption that (beyond the threshold) more intelligent people are exponentially more effective at solving problems that duller people; but of course the absolute numbers of those highly intelligent people taper off dramatically due to bell curve dynamics.

To understand the Pythagoras Theorem you need an IQ of around 100, implying that to discover it, the threshold is around 130. The Odyssey might be a great classic, but it has a simple, linear storyline with no particularly deep moral themes or conundrums (reminder: The putative heroes end up hanging all the female household servants who had allegedly slept with the suitors and no time is lost on further introspection). I suspect the threshold for writing it is also around 130.



This implies that around that period – the 8th-6th centuries BC in the Mediterranean – you needed a 130 IQ to move the intellectual boundaries outwards. As we can see, Ancient Greece was overshadowed by both the Roman Empire and Renaissance Italy at ΔT(+2.0), except that… conveniently, neither of the latter two existed. Its competitors at the time, civilizations like the Assyrians, Babylonians, and Egyptians, lagged substantially in IQ and literacy, and did not compensate demographically; Phoenicia might have matched Greek literacy, but was probably behind in IQ, and had far fewer people. Remarkably, it was vastly ahead of China even 500 years later.

Literacy increased during this period, and the population rose steadily to its plateau of ~10 million as Greeks colonized the Mediterranean rim, and so during this time, intellectually they were the only game in town.

During the two centuries of Classical Greece’s flowering from the 5th-4th centuries BC, the Ancient Greeks almost singlehandedly pushed the discovery threshold up by almost a standard deviation. In the process, tons of discoveries and advancements were made. To really appreciate Euclid, you probably need an IQ closer to 115. Archimedes was perhaps the most quantitatively brilliant Greek of them all, coming tantalizingly close to uncovering the calculus. Understanding classical Greek philosophy (and for that matter, the later works of the Neoplatonists and Gnostics) likewise becomes far more demanding but is not beyond the capabilities of a committed 110 or 115 IQ person. Even so, they have nothing on the likes of 20th century philosophers like Ludwig Wittgenstein or Martin Heidegger. Even very intelligent people have to commit years of dedicated effort in order to master their ideas. The complexity of the Antikythera mechanism (Hellenistic times) has been compared to late medieval European mechanical clocks. To really master them, I suspect the minimal IQ is likewise around 110-115, hence innovating it might require a threshold IQ of around 140-145.

By Hellenistic times, progress became much harder, not because Greeks had become (much) dumber or had become culturally Orientalized, but because the low hanging fruit had already been picked. Naturally, the same went for the Romans.

ΔT(+2.0) i.e. at the 130 discovery threshold for Ancient Greece as of ~500 BC was 43,000 (plus/minus a very large percentage error). ΔT(+3.0) i.e. at the 145 discovery threshold for the Romans as of ~0AD was 2,500 – and there were far more discoveries to be made. Naturally, progress slowed down drastically.

ΔT(+3.0) i.e. at the 145 discovery threshold of Renaissance Italy just by itself more than twice as dynamic as the entire Roman Empire. And the figures for Europe as a whole would have been vastly bigger still. Hence the (real) perception that by the Renaissance, the boundaries were once again being pushed outwards at a face rate, which would become a positive explosion from the 17th century on, when the first incipient mass literacy programs were launched and demographic mass also started soaring.

• Category: History, Science • Tags: Ancient Near East, Apollo's Ascent, BigPost 


RATING: 8/10. (Please note my ratings system is harsh and virtually no films get a 10).

In 2011, American sci-fi giant Neal Stephenson bewailed the pessimism prevalent in the genre and called for writers to start thinking more positively about the possibilities of technology in order to inspire new generations to “get big stuff done.”

Of course, he himself hardly set a great example in the next four years with his latest tome.

But the Martian most definitely did. In this hard sci-fi scenario, an astronaut stranded on Mars has to figure out how to survive until a rescue mission could be organized. To do this, he has to, in his own words, “science the shit” of the scarce oxygen and food resources at his disposal, while a NASA that is much better funded than in real life has to solve its own set of problems, which at first glance appear intractable.

Making the story of one solitary man’s struggle to survive is not a enviable task, but the creators pull it off with ample wit and verve. The protagonist Mark Watney is constantly cracking Nerd Lite jokes with himself and mission control in his struggle with the remorseless but indifferent main villain, the Red Planet itself.

nasa-survival-on-the-moon Scientific and technical problems are explained in a way that is neither patronizing nor unintelligible to the average viewer. These problems, though varied, all tend to be in the general spirit of the classic “Survival on the Moon” exercise compiled by NASA, in which different options have to be weighed against each other in a way that in a way that could tip the otherwise dismal odds of survival in your favor.

There are frequent references and homages to NASA themes. The “Rich Purnell manoeuvre” that ultimately enabled Watney’s survival is a direct nod to NASA mathematician Michael Minovitch’s idea of a gravity assist to propel Voyager past all four of the gas giants and into deep space (though the theoretical basis for it had been as early as the 1930s in the Soviet Union).

The film appears to be faithful to NASA culture, down to the contrast between the formal and besuited setting of NASA HQ and the more casual setting of its Jet Propulsion Laboratories. As in real world space exploration, duct tape is the solution to a lot of problems. The “no duct tape on Mars” trope is most decidedly averted.

Most of the challenges faced appear to be technically accurate. This is not surprising, since the book by Andy Weir that the film is based on was rigorously researched and initially published chapter by chapter on his website, where space nerds with encyclopedic knowledge on everything space related continuously corrected him.

There are certainly errors now and then. (I have not read the book and probably will not anytime soon, so these apply exclusively to the film). Gravity on Mars appears a bit too Earth like, with astronauts having to really physically apply themselves to scramble up ladders. Although Mars has the occasional storm, the much thinner atmosphere means that even the most furious tempests will be perceived as a light breeze; certainly nowhere near strong enough to uproot a pole and spear it into Watney. For a novel ostensibly set in 2035, comms systems act as if they are half a century out of date, just to serve a couple of plot points (if otherwise very elegant and clever ones). An astronaut propels himself around the outside of a spacecraft without a tether, while making an appearance in the one case in which a teether would have actually been redundant.

mars-radiation Another criticism of the film is that the astronauts should be all dying of cancer by the end of the film because of all the cosmic radiation (there are no obvious attempts to shield them from it). I am rather skeptical of this. The radiation dose Mars explorers receive will only be 3x as great as that received by astronauts who spend half a year on the International Space Station. But those guys aren’t keeling over dead. Theoretical research shows that the lifetime risk of cancer will only increase by three percentage points over baseline for astronauts who go to Mars, and in real life perhaps outcomes will if anything be even less dire because of the hormetic effects of radiation exposure.

Has anyone actually performed any concrete demographic studies of the death rate from cancer for astronauts (as opposed to theoretical projections)? Let me know in the comments.

But all these are ultimately minor triffles. At its root, it is a highly optimistic, positive, and inspirational story about the victory of technology and human ingenuity over the challenges posed by the last frontier. There should be more of these kinds of cultural products for civilization to continue to flourish.

The Martian is an excellent film, by far the best sci-fi flick this year along with Ex Machina, and incomparably better than the banal Hollywood fare that was Jurassic World, Mad Max: Fury Road, and by all indications, the final Hunger Games movie.

• Category: Science • Tags: Film, Review, Sci-Fi, Space Exploration 
HBD, Hive Minds, and H+

Today is the publication date of Hive Mind, a book by economist Garett Jones on the intimate relationship between average national IQs and national success, first and foremost in the field of economics.

I do intend to read and review it ASAP, but first some preliminary comments.

This is a topic I have been writing about since I started blogging in 2008 (and indeed well before I came across Steve Sailer or even HBD) and as it so happens, I have long been intending to write a similar sort of book myself – tentatively titled Apollo’s Ascent – but one that focuses more on the historical aspect of the relationship between psychometrics and development:

My basic thesis is that the rate of technological progress, as well as its geographical pattern, is highly dependent on the absolute numbers of literate high IQ people.

To make use of the intense interest that will inevitably flare up around these topics in the next few days – not to mention that rather more self-interested reason of confirming originality on the off chance that any of Garett Jones’ ideas happen to substantively overlap with mine – I have decided to informally lay out the theoretical basis for Apollo’s Ascent right now.

1. Nous

Assume that the intellectual output of an average IQ (=100, S.D.=15) young adult Briton in the year 2000 – as good an encapsulation of the “Greenwich mean” of intelligence as any – is equivalent to one nous (1 ν).

This can be used to calculate the aggregate mindpower (M) in a country.

Since sufficiently differing degrees of intelligence can translate into qualitative differences – for instance, no amount of 55 IQ people will be able to solve a calculus problem – we also need to be able to denote mindpower that is above some threshold of intelligence. So in this post, the aggregate mindpower of a country that is above 130 will be written as M(+2.0), i.e. that aggregate mindpower that is two standard deviations above the Greenwich mean.

2. Intelligence and Industrial Economies

There is a wealth of evidence implying an exponential relationship between average IQ and income and wealth in the United States.


Click to enlarge.

There is likewise a wealth of evidence – from Lynn, Rindermann, La Griffe du Lion, your humble servant, etc. – that shows an exponential relationship between levels of average national IQ and GDP per capita (PPP adjusted). When you throw out countries with a legacy of Communism and the ruinous central planning they practiced (China, the Ex-USSR and Eastern Europe, etc), and countries benefitting disproportionately from a resource windfall (Saudi Arabia, the UAE, etc), there is an amazing R2=0.84 correlation between performance in the PISA international standardized student tests and GDP (PPP) per capita. (In sociology, anything about R2=0.3 is a good result).

The reasons for this might be the case are quite intuitive. At the most basic level, intelligent people can get things done better and more quickly. In sufficiently dull societies, certain things can’t get done at all. To loosely borrow an example from Gregory Clark’s A Farewell to Alms, assume a relatively simple widget that requires ten manufacturing steps that have to be done just right to make it commercially viable. Say an 85 IQ laborer has a failure rate of 5% for any one step, while a 100 IQ laborer has a failure rate of 1%. This does not sound like that big or cardinal of a difference. But repeated ten times, some 40% of the duller worker’s production ends up being a dud, compared to only 10% of the brighter worker’s. Consequently, one is competitive on the global markets, whereas the other is not (if labor costs are equal; hence, of course, they are not).

Now imagine said widget is an automobile, with hundreds of thousands of components. Or an aircraft carrier, or a spaceship. Or a complex surgery operation.

More technical way of looking at this: Consider the GDP equation, Y = A * K^α * L^(1-α), in which K is capital, L is labour, α is a constant that usually equals about 0.3, and A is total factor productivity. It follows that the only way to grow per capita output in the longterm is to raise productivity. Productivity in turn is a function of technology and how effectively it is utilized and that in turn depends critically on things like human capital. Without an adequate IQ base, you cannot accumulate much in the way of human capital.

There are at least two further ways in which brighter societies improve their relative fortunes over and above what might merely be implied by their mere productivity advantage at any technological level.


Source: Swiss Miss.

First, capital gets drawn to more productive countries, until the point at which its marginal productivity equalizes with that of less productive countries, with their MUCH LOWER levels of capital intensity. First World economies like Germany, Japan, and the US are extremely capital intensive. It is probably not an accident that Japan, Korea, and Taiwan – some of the very brightest countries on international IQ comparisons – also have by far the world’s highest concentrations of industrial robots per worker (and China is fast catching up). Since economic output is a function not only of pure productivity but also of capital (though subject to diminishing returns) this provides a big further boost to rich countries above the levels implied by their raw productivity. And as the age of automation approaches, these trends will only intensify.

Second, countries with higher IQs also tend to be better governed, and to effectively provide social amenities such as adequate nutrition and education to their populations. Not only does it further raise their national IQs, but it also means that it is easier to make longterm investments there and to use their existing human capital to its full potential.

All this implies that different levels of intelligence have varying economic values on the global market. At this stage I am not so much interested in establishing it with exactitude as illustrating the general pattern, which goes something like this:

  • Average IQ = 70 – Per capita GDP of ~$4,000 in the more optimally governed countries of this class, such as Ghana (note however that many countries in this class are not yet fully done with their Malthusian transitions, which will depress their per capita output somewhat – see below).
  • Average IQ = 85 – Per capita GDP of ~$16,000 in the more optimally governed countries of this class, such as Brazil.
  • Average IQ = 100 Per capita GDP of ~45,000 in the more optimally governed countries of this class, or approximately the level of core EU/US/Japan.
  • Average IQ = 107 – Per capita GDP of potentially $80,000, as in Singapore (and it doesn’t seem to have even finished growing rapidly yet). Similar figures for elite/financial EU cities (e.g. Frankfurt, Milan) and US cities (e.g. San Francisco, Seattle, Boston).
  • Average IQ = 115 – Largely a theoretical construct, but that might be the sort of average IQ you’d get in, say, Inner London – the center of the global investment banking industry. The GDP per capita there is a cool $152,000.

Countries with bigger than normal “smart fractions” (the US, India, Israel) tend to have a bigger GDP per capita than what could be assumed from just from their average national IQ. This stands to reason because a group of people equally split between 85 IQers and 115 IQers will have higher cognitive potential than a room composed of an equivalent number of 100 IQers. Countries with high average IQs but smaller than normal S.D.’s, such as Finland, have a slightly smaller GDP per capita that what you might expect just from average national IQs.

These numbers add up, so a reasonable relationship equilibrium GDP (assuming no big shocks, good policies, etc) and the structure and size of national IQ would be:

Equilibrium GDP of a country exponent (IQ) * the IQ distribution (usually a bell curve shaped Gaussian) * population size * the technological level

Which can be simplified to:

Y ≈ c*M*T

… where M is aggregate mindpower (see above), T is the technology level, and c is a constant denoting the general regulatory/business climate (close to 1 in many well run capitalist states, <0.5 under central planning, etc).

To what extent if any would this model apply to pre-industrial economies?

3. Intelligence and Malthusian Economies


Source: A Farewell to Alms

Very little. The problem with Malthusian economies is that, as per the old man himself, population increases geometrically while crop yields increase linearly; before long, the increasing population eats up all the surpluses and reaches a sordid equilibrium in which births equal deaths (since there were a lot of births, that means a lot of deaths).

Under such conditions, even though technology might grow slowly from century to century, it is generally expressed not in increasing per capita consumption, but in rising population densities. And over centennial timescales, the effects of this (meager) technological growth can be easily swamped by changes in social structure, biome productivity, and climatic fluctuations (e.g. 17th C France = pre Black Death France in terms of population, because it was Little Ice Age vs. Medieval Warm Period), or unexpected improvements in agricultural productivity e.g. from the importation of new crops (e.g. the coming of sweet potatoes to China which enabled it to double its population over the previous record even though it was in outright social regress for a substantial fraction of this time).

All this makes tallying the rate of technological advance based on population density highly problematic. Therefore it has to be measured primarily in terms of eminent figures, inventions, and great works.


Distribution of significant figures across time and place. Source: Human Accomplishment.

The social scientist Charles Murray in Human Accomplishment has suggested a plausible and objective way of doing it, based on tallying the eminence of historical figures in culture and the sciences as measured by their prevalence in big reference works. Societies that are at any one time intensively pushing the technological frontiers outwards are likely to be generating plenty of “Great People,” to borrow a term from the Civilization strategy games.

To what extent does the model used for economic success apply to technology?

4. Intelligence and Technology Before 1800

A narrow intellectual elite is responsible for 99%+ of new scientific discoveries. This implies that unlike the case with an economy at large, where peasants and truck drivers make real contributions, you need to have a certain (high) threshold level of IQ to materially contribute to technological and scientific progress today.

The Anne Roe study of very eminent scientists in 1952 – almost Nobel worthy, but not quite – found that they averaged a verbal IQ of 166, a spatial IQ of 137, and a math IQ of 154. Adjusted modestly down – because the Flynn Effect has only had a very modest impact on non-rule dependent domains like verbal IQ – and you get an average verbal IQ of maybe 160 (in Greenwich terms). These were the sorts of elite people pushing progress in science 50 years ago.

To really understand 1950s era math and physics, I guesstimate that you would need an IQ of ~130+, i.e. your typical STEM grad student or Ivy League undergrad. This suggests that there is a 2 S.D. difference between the typical intellectual level needed to master something as opposed to making fundamental new discoveries in it.

Moreover, progress becomes steadily harder over time; disciplines splinter (see the disappearance of polymath “Renaissance men”), and eventually, discoveries become increasingly unattainable to sole individuals (see the steady growth in numbers of paper coauthors and shared Nobel Prizes in the 20th century). In other words, these IQ discovery thresholds are themselves a function of the technological level. To make progress up the tech tree, you need to first climb up there.

An extreme example today would be the work 0f Japanese mathematician Shinichi Mochizuki. At least Grigory Perelman’s proof of the Poincare Conjecture was eventually confirmed by other mathematicians after a lag of several years. But Mochizuki is so far ahead of everyone else in his particular field of Inter-universal Teichmüller theory that nobody any longer quite knows whether he is a universal genius or a lunatic.

In math, I would guesstimate roughly the following set of thresholds:

Mastery Discovery
Intuit Pythagoras Theorem (Ancient Egypt) 90 120
Prove Pythagoras Theorem (Early Ancient Greece) 100 130
Renaissance Math (~1550) 110 140
Differential Calculus (~1650+) 120 150
Mid-20th Century Math (1950s) 130 160
Prove Poincare Conjecture (2003) 140 170
Inter-universal Teichmüller theory (?) 150 180

This all suggests that countries which attain new records in aggregate elite mindpower relative to their predecessors can very quickly generate vast reams of new scientific discoveries and technological achievements.

Moreover, this elite mindpower has to be literate. Because a human brain can only store so much information, societies without literacy are unable to move forwards much beyond Neolithic levels, their IQ levels regardless.

As such, a tentative equation for estimating a historical society’s capacity to generate scientific and technological growth would look something like this:

Technological growth c * M(>threshold IQ for new discovery) * literacy rate


ΔT c * M(>discovery-threshold) * l

in which only that part of the aggregate mindpower that is above the threshold is considered; c is a constant that illustrates a society’s propensity for generating technological growth in the first place and can encompass social and cultural factors, such as no big wars, no totalitarian regimes, creativity, etc. as well as technological increases that can have a (generally marginal) effect on scientific productivity, like reading glasses in Renaissance Italy (well covered by David Landes), and the Internet in recent decades; and the literacy rate l is an estimate of the percentage of the cognitive elites that are literate (it can be expected to generally be a function of the overall literacy rate and to always be much higher).

Is it possible to estimate historical M and literacy with any degree of rigor?


Source: Gregory Clark.

I think so. In regards to literacy, this is an extensive area of research, with some good estimates for Ancient Greece and the Roman Empire (see Ancient Literacy by William Harris) and much better estimates for Europe after 1500 based on techniques like age heaping and book production records.

One critical consideration is that not all writing systems are equally suited for the spread of functional literacy. For instance, China was historically one of the most schooled societies, but its literacy tended to be domain specific, the classic example being “fish literacy” – a fishmonger’s son who knew the characters for different fish, but had no hope of adeptly employing his very limited literacy for making scientific advances, or even reading “self-help” pamphlets on how to be more effective in his profession (such as were becoming prevalent in England as early as the 17th century). The Chinese writing system, whether it arose from QWERTY reasons or even genetic reasons – and which became prevalent throughout East Asia – surely hampered the creative potential of East Asians.

Estimating average national IQs historically – from which M can be derived in conjunction with historical population sizes, of which we now generally have fairly good ideas about – is far more tricky and speculative, but not totally hopeless, because nowadays we know the main factors behind national differences in IQ.

Some of the most important ones include:

  • Cold Winters Theory – Northern peoples developed higher IQs (see Lynn, Rushton).
  • Agriculture – Societies that developed agriculture got a huge boost to their IQs (as well as higher S.D.s).
  • Inbreeding – Can be estimated from rates of consanguineous marriage, runs of homozygosity, and predominant family types (nuclear? communitarian?), which in turn can be established from cultural and literary evidence.
  • Eugenics – In advanced agricultural societies, where social relations come to be dominated by markets. See Greg Clark on England, and Ron Unz on China.
  • Nutrition – Obviously plays a HUGE role in the Flynn Effect. Can be proxied by body measurements, and fortunately there is a whole field of study devoted to precisely this: Auxology. Burials, conscription records, etc. all provide a wealth of evidence.
  • Parasite Load – Most severe in low-lying, swampy areas like West Africa and the Ganges Delta.

This old comment of mine to a post by Sailer is a demonstration of the sort of reasoning I tend to employ in Apollo’s Ascent.

All this means that educated guesses at the historic IQs of various societies are now perfectly feasible, if subject to a high degree of uncertainty. In fact, I have already done many such estimates while planning out Apollo’s Ascent. I will not release these figures at this time because they are highly preliminary, and lacking space to further elucidate my methods, I do not want discussions in the comments to latch on to some one figure or another and make a big deal out of it. Let us save this for later.

But in broad terms – and very happily for my thesis – these relations DO tend to hold historically.

Classical Greece was almost certainly the first society to attain something resembling craftsman level literacy rates (~10%). Ancient Greeks were also unusually tall (indicating good nutrition, for a preindustrial society), lived in stem/authoritarian family systems, and actively bred out during their period of greatness. They produced the greatest scientific and cultural explosion up to that date anywhere in the world, but evidently didn’t have quite the demographic weight – there were no more than 10 million Greeks scattered across the Mediterranean at peak – to sustain it.

In 15th century Europe, literacy once again begun soaring in Italy, to beyond Roman levels, and – surely helped by the good nutrition levels following the Black Death – helped usher in the Renaissance. In the 17th century, the center of gravity shifted towards Anglo-Germanic Europe in the wake of the Reformation with its obsession with literacy, and would stay there ever after.

As regards other civilizations…

The Islamic Golden Age was eventually cut short more by the increasing inbreeding than by the severe but ultimately temporary shock from the Mongol invasions. India was too depressed by the caste system and by parasitic load to ever be a first rate intellectual power, although the caste system also ensured a stream of occasional geniuses, especially in the more abstract areas like math and philosophy. China and Japan might have had an innate IQ advantage over Europeans – albeit one that was quite modest in the most critical area, verbal IQ – but they were too severely hampered by labour-heavy agricultural systems and a very ineffective writing system.

In contrast, The Europeans, fed on meat and mead, had some of the best nutrition and lowest parasitic load indicators amongst any advanced civilization, and even as rising population pressure began to impinge on those advantages by the 17th-18th centuries, they had already burst far ahead in literacy, and intellectual predominance was now theirs to lose.

5. Intelligence and Technology under Industrialism

After 1800, the world globalized intellectually. This was totally unprecedented. There had certainly been preludes to it, e.g. in the Jesuit missions to Qing China. But these were very much exceptional cases. Even in the 18th century, for instance, European and Japanese mathematicians worked on (and solved) many of the same problems independently.


Source: Human Accomplishment.

But in the following two centuries, this picture of independent intellectual traditions – shining most brightly in Europe by at least an order of magnitude, to be sure, but still diverse on the global level – was to be homogenized. European science became the only science that mattered, as laggard civilizations throughout the rest of the world were to soon discover to their sorrow in the form of percussion rifles and ironclad warships. And by “Europe,” that mostly meant the “Hajnal” core of the continent: France, Germany, the UK, Scandinavia, and Northern Italy.

And what had previously been but a big gap became an awning chasm.

(1) In the 19th century, the populations of European countries grew, and the advanced ones attained universal literacy or as good as made no difference. Aggregate mindpower (M) exploded, and kept well ahead of the advancing threshold IQ needed to make new discoveries.

(2) From 1890-1970, there was a second revolution, in nutrition and epidemiology – average heights increased by 10cm+, and the prevalence of debilitating infectitious diseases was reduced to almost zero – that raised IQ by as much as a standard deviation across the industrialized world. The chasm widened further.

(3) During this period, the straggling civilizations – far from making any novel contributions of their own – devoted most of their meager intellectual resources to merely coming to grips with Western developments.

This was as true – and consequential – in culture and social sciences as it was in science and technology; the Russian philosopher Nikolay Trubetzkoy described this traumatic process very eloquently in The Struggle Between Europe and Mankind. What was true even for “semi-peripheral” Russia was doubly true for China.

In science and technology, once the rest of the world had come to terms with Western dominance and the new era of the nation-state, the focus was on catchup, not innovation.This is because for developing countries, it is much more useful in terms of marginal returns to invest their cognitive energies into copying, stealing, and/or adapting existing technology to catch up to the West than to develop unique technology of their own. Arguments about, say, China’s supposed lack of ability to innovate are completely besides the point. At this stage of its development, even now, copying is much easier than creating!

This means that at this stage of global history, a country’s contribution to technological growth isn’t only a matter of the size of its smart fractions above the technological discovery IQ threshold. (This remains unchanged: E.g., note that a country like Germany remains MUCH more innovative per capita than, say, Greece, even though their aveage national IQs differ by a mere 5 points or so. Why? Because since we’re looking only at the far right tails of the bell curve, even minor differences in averages translate to big differences in innovation-generating smart fractions).

It also relates closely to its level of development. Countries that are far away from the technological frontier today are better served by using their research dollars and cognitive elites to catch up as opposed to inventing new stuff. This is confirmed by real life evidence: A very big percentage of world spending on fundamental research since WW2 has been carried out in the US. It was low in the USSR, and negligible in countries like Japan until recently. Or in China today.

Bearing this in mind, the technological growth equation today (and since 1800, more or less) – now due to its global character better described as innovation potential – would be better approximated by something like this:

Innovation potential ≈ c * M(>threshold IQ for new discovery) * literacy rate * (GDP/GDP[potential])^x


I c * M(>discovery-threshold) * l * (Y/Y[P])^x

in which the first three terms are as before (though literacy = 100% virtually everywhere now), and potential GDP is the GDP this country would obtain were its technological endowment to be increased to the maximum level possible as dictated by its cognitive profile. The “x” is a further constant that is bigger than 1 to reflect the idea that catchup only ceases to be the most useful strategy once a country has come very close to convergence or has completely converged.

Japan has won a third of all its Nobel Prizes before 2000; another third in the 2000s; and the last third in the 2010s. Its scientific achievements, in other words, are finally beginning to catch up with its famously high IQ levels. Why did it take so long?

Somebody like JayMan would say its because the Japanese are clannish or something like that. Other psychometrists like Kenya Kura would notice that perhaps they are far less creative than Westerners (this I think has a measure of truth to it). But the main “purely IQ” reasons are pretty much good enough by themselves:

  • The Nobel Prize is typically recognized with a ~25-30 year lag nowadays.
  • It is taking ever longer amounts of time to work up to a Nobel Prize because ever greater amounts of information and methods have to be mastered before original creative work can begin. (This is one consequence of the rising threshold discovery IQ frontier).
  • Critically, Japan in the 1950s was still something of a Third World country, with the attended insults upon average IQ. It is entirely possible that elderly Japanese are duller than their American counterparts, and perhaps even many Europeans of that age, meaning smaller smart fractions from the Nobel Prize winning age groups.

Japan only became an unambiguously developed country in the 1970s.

And it just so happens that precisely 40 years after this did it begin to see a big and still accelerating increase in the numbers of Nobel Prizes accruing to it!

Extending this to South Korea and Taiwan, both of which lagged around 20 years behind Japan, we can only expect to see an explosion in Nobel Prizes for them from the 2020s, regardless of how wildly their teenagers currently top out the PISA rankings.

Extending this to China, which lags around 20 years behind South Korea, and we can expect to see it start gobbling up Nobel Prizes by 2040, or maybe 2050, considering the ongoing widening of the time gap between discovery and recognition. However, due to its massive population – ten times as large as Japan’s – once China does emerge as a major scientific leader, it will do so in a very big way that will rival or even displace the US from its current position of absolute primacy.

As of 2014, China already publishes almost as many scientific papers per year as does the US, and has an outright lead in major STEM fields such as Math, Physics, Chemistry, and Computer Science. (Though to be sure, their quality is much lower, and a significant fraction of them are outright “catching up” or “adaption” style papers with no new findings).

If we assume that x=1, and that c is equal for both China and the US, then it implies that both countries currently have broadly equal innovation potential. But of course c is not quite equal between them – it is lower for China, because its system is obviously less conductive to scientific research than the American – and x is higher than 1, so in practice China’s innovation potential is still considerably lower than that of the US (maybe a quarter or a third). Nonetheless, as China continues to convege, c is going to trend towards the US level, and the GDP gap is going to narrow; plus it may also be able to eke out some further increases in its national average IQ from the current ~103 (as proxied by PISA in 2009) to South Korea’s level of ~107 as it becomes a truly First World country.

And by mid-century it will likely translate into a strong challenge to American scientific preeminence.

6. Future Consequences

The entry of China onto the world intellectual stage (if the model above is more or less correct) will be portentuous, but ultimately it will in its effects on aggregate mindpower be nowhere near the magnitude in global terms of the expansion in the numbers of literate, mostly European high IQ people from 1450 to 1900, nor the vast rise in First World IQ levels from 1890-1970 due to the Flynn Effect.

Moreover, even this may be counteracted by the dysgenic effects already making themselves felt in the US and Western Europe due to Idiocracy-resembling breeding patterns and 80 IQ Third World immigration.

And no need for pesky implants!

Radically raise IQ. And no need for pesky neural implants!

A lot of the techno-optimistic rhetoric you encounter around transhumanist circles is founded on the idea that observed exponential trends in technology – most concisely encapsulated by Moore’s Law – are somehow self-sustaining, though the precise reasons why never seem to be clearly explained. But non-IT technological growth peaked in the 1950s-70s, and has declined since; and as a matter of fact, Moore’s Law has also ground to a halt in the past 2 years. Will we be rescued by a new paradigm? Maybe. But new paradigms take mindpower to generate, and the rate of increase in global mindpower has almost certainly peaked. This is not a good omen.

Speaking of the technological singularity, it is entirely possible that the mindpower discovery threshold for constructing a superintelligence is in fact far higher than we currently have or are likely to ever have short of a global eugenics program (and so Nick Bostrom can sleep in peace).

On the other hand, there are two technologies that combined may decisively tip the balance: CRISPR-Cas9, and the discovery of the genes for general intelligence. Their maturation and potential mating may become feasible as early as 2025.

While there are very good reasons – e.g., on the basis of animal breeding experiments – for doubting Steve Hsu’s claims that genetically corrected designer babies will have IQs beyond that of any living human today, increases on the order of 4-5 S.D.’s are entirely possible. If even a small fraction of a major country like China adopts it – say, 10% of the population – then that will in two decades start to produce an explosion in aggregate global elite mindpower that will soon come to rival or even eclipse the Renaissance or the Enlightenment in the size and scope of their effects on the world.

The global balance of power would be shifted beyond recognition, and truly transformational – indeed, transhuman – possibilities will genuinely open up.



Image by Kerihobo.

While everybody is discussing the tantalizing possibility that this far off star with its strange dimming patterns hosts an alien megastructure, perhaps a Dyson Sphere under construction, there are even more exotic scenarios out there.

For instance, why not the ruins of one? One of the obvious (if pessimistic) solutions to the Fermi Paradox is that space is a war of all against all, with every surviving alien civilization soon realizing that they can’t afford to show their head above the cosmic parapets. Due to the vast distances involved across space and time, stealth is surely the decisive factor in space warfare, so the offensive reigns supreme over the defensive. Chuck a big, cool clump of dense matter at a very high velocity into a location where it is likely to intersect with the path of a rival space civilization and the guys at the receiving end would hardly have any time to know what hit them let alone where it came from.

It is thus possible that xenocidal aggressiveness is an evolved behavior across all surviving alien civilizations. Just as any good or trusting creature dreamt up by mortals and given flesh in the northern Chaos Wastes of the world of Warhammer gets instantly killed by stronger and more evil entities, so too, perhaps, the less paranoid and aggressive space civilizations get snuffed out as soon as they make their existence known to the cruel gods of the heavens.

Or maybe, Nick Bostrom is correct and we are living in a simulation – with the catch that computing resources are limited and cannot support more than a certain number of superintelligent civilizations and their subsimulations, to say nothing of some kind of Kurzweilian “the universe wakes up” intelligence saturation scenario. Maybe that explains the “supervoid.” A singularitarian civilization attempted to “wake up” the universe in an expanding radius from its home planet, and got their section of space Ctrl-Alt-Deleted by The Architect for their trouble. Since then, other advanced civilizations logically deducated what must have happened, and universally agreed – without any consultation, naturally – to adopt the Lannisterian code that everyone who isn’t us is an enemy.

Or maybe the very observation of KIC 8462852 at this moment in history is an elaborate trap. For instance, here is a particularly paranoid but not implausible scenario from a comment to a Less Wrong article by the Russian futurist Alexey Turchin on the risks of passive SETI:

A comment by JF: For example the lack of SETI-attack so far may itself be a cunning ploy: At first receipt of the developing Solar civilization’s radio signals, all interstellar ‘spam’ would have ceased, (and interference stations of some unknown (but amazing) capability and type set up around the Solar System to block all coming signals recognizable to its’ computers as of intelligent origin,) in order to get us ‘lonely’ and give us time to discover and appreciate the Fermi Paradox and even get those so philosophically inclined to despair desperate that this means the Universe is apparently hostile by some standards. Then, when desperate, we suddenly discover, slowly at first, partially at first, and then with more and more wonderful signals, the fact that space is filled with bright enticing signals (like spam). The blockade, cunning as it was (analogous to Earthly jamming stations) was yet a prelude to a slow ‘turning up’ of preplanned intriguing signal traffic. If as Earth had developed we had intercepted cunning spam followed by the agonized ‘don’t repeat our mistakes’ final messages of tricked and dying civilizations, only a fool would heed the enticing voices of SETI spam. But now, a SETI attack may benefit from the slow unmasking of a cunning masquerade as first a faint and distant light of infinite wonder, only at the end revealed as the headlight of an onrushing cosmic train…

Or maybe it really is something very banal, like a cloud of disintegrating comets…

• Category: Science • Tags: Existential Risks, Space Exploration 

The newly released paper by Anne Case and Angus Deaton showing that mortality rates amongst middle-aged White American males (MAWAM) increased from 1999-2013 has been generating a lot of discussion of late. This mortality increase was concentrated amongst MAWAMs with a high school degree or less (“Fishtown,” to borrow from Charles Murray’s archetype of a White working class town), who now have a mortality rate even greater than that of US Blacks with their much-discussed health and violent crime problems. But mortality continued falling amongst the better educated Whites (“Belmont,” Murray’s archetypical White American upper middle class suburb).

This mortality increase was apparently driven by a surge in deaths from external causes, especially poisonings, suicide, and chronic liver cirrhosis. Even MAWAMs with a BS degree or higher saw a tiny increase in deaths from external causes, to the extent that MAWAMs are now more likely to die from external causes in their middle age than Latinos, or even Blacks.


It is worth pointing out that it seems to be a very unusual pattern relative not just to other ethnic groups in the US but to other developed European and Anglo countries. The graph below shows all-cause mortality for 45-54 year old MAWAMs relative to their peers in France, Germany, the UK, Canada, Australia, and Sweden. Americans went from being middle of the pack to an outlier.


As someone familiar with Russian demographic history, this was a depressingly familiar pattern to me.

Russian demographic history 101: By the mid-1960s, Russian life expectancy – both male and female – had basically converged with that of the First World.

Then it essentially stagnated… for half a century.


Remarkably, a Russian 50 year old man in 1964 had a smaller chance of dying (1,129/100,000 annually) than his grandson in 2010 (1,655/100,000) – regardless of all the medical advances in the intervening half century.

This mortality tsunami was driven by a huge rise in alcoholism from the 1960s, coupled with the Soviet Union’s lack of interest in creating a modern hi-tech medical system (as the West started to do in earnest from the 1970s). Although there was a modest interruption to these negative trends in the mid-to-late 1980s, when there was a modest improvement thanks to Gorbachev’s anti-alcohol campaign, the decline resumed with a vengeance in the 1990s as the Soviet state lost its monopoly on vodka production and vodka prices plummeted. In the 2000s things started looking up again, as the Putin government raised excise taxes on vodka, invested in modern medical care, and changing social mores and the labor discipline promoted by capitalist economics started making binge drinking less cool. Even so, as of 2015, the health profiles of Russian men – though far improved relative to the days of the late Brezhnev, to say nothing of Yeltsin – have yet to exceed their mid-1960s peaks.

Although there were some “bad trends” in terms of healthy lifestyle in the West as well from the 197os – it was from this period onwards that the US got started on its obesity epidemic – these were much less detrimental to overall health than the hardcore vodka binge drinking that became prevalent in Soviet life by the 1970s, and their negative effects were in any case more than fully counteracted by vast improvements in emergency response and cardiac medical care.

The great stagnation in MAWAM mortality in the 1990s and 2000s revealed by Case and Deaton – down to the social differentiation, with the situation improving slightly in well-educated, upper class Belmont, but positively plummeting in poorly-educated, lower class Fishtown and more than cancelling out improvements in Belmont so far as MAWAMs as a whole are considered – seems to have a striking parallel with what happened after the collapse of the Soviet Union.

Although the post-Soviet mortality crisis was felt across all social groups, it impacted middle-aged Russian men (MARMs) especially hard. Mortality for the best educated segments of the population, while rising initially in the early 1990s, quickly reversed and soon fell below Soviet-era levels. In contrast, the lower class Russians – the “gopnik” class of popular culture – have far poorer health today (despite the Putin era recovery) than even in the most vodka-drenched days of the Soviet Union in the mid-1980s. Here is a 2005 article from the American Journal of Public Health:

The mortality advantage of better-educated men and women in 1980 increased substantially by 2001. In 1980, life expectancy at age 20 for university-educated men was 3 years greater than for men with elementary education only, but was 11 years greater by 2001, reflecting not only declining life expectancy in less-educated men but also an improvement among better-educated men. Similar patterns were seen in women.

Looking at causes of mortality, mortality increases were driven above all by the rise in deaths from poisonings – almost exclusively alcohol deaths, in Russia’s case – and associated factors, such as deaths from external causes (these are linked: When you imbibe vodka in regular binges, you will be more likely to commit suicide, have car accidents, murder your drinking partners in a fit of drunken homicidal rage, etc). Moreover, increases in alcohol death were also reflected in and magnified to a large extent by deaths from cardiovascular diseases – chronic bingeing, needless to say, has bad effects on your overall health – which was further compounded by Russia’s traditional lack of modern medical facilities to treat expensive modern ailments. That is because neither the USSR nor Yeltsinite Russia cared much for the health and welfare of ordinary Russians: So far as the Soviets were concerned, the main thing was to get people through military and reproductive age in more or less adequate shape, and not bother themselves overmuch with what happened to them from their 50s; while the oligarchs who ruled Russia from behind the scenes in the 1990s didn’t care even about that.

To be sure, there are also major differences between the American and Russian experiences. For instance, in Russia, the 1990s and 2000s saw a big dip, and then a big recovery back to late Gorbachev levels of MARM mortality, whereas the mortality rates of MAWAMs simply stagnated at a more or less steady level throughout from 1990 to 2013.

And critically, 1980s Soviet mortality levels themselves began from a much higher base relative to the US. While according to Case and Deaton’s graph MAWAM annual mortality levels for the 45-54 age group were 415/100,000 in 2013, rising to 736/100,000 for poorly educated MAWAMs, that is still far less than the 1,655/100,000 mortality rate for 50 year old MARMs in 2010 (or even in 2014 when it is now perhaps 1,300/100,000).

Nonetheless, regardless of the fact that the US mortality crisis is far less severe in absolute terms, and didn’t undergo the catastrophic “spike” that post-Soviet Russia experienced, the similarities – a major demographic group experiencing a sustained deterioration in its mortality prospects over a period of decades in an industrialized country – are otherwise quite remarkable.

Steve Sailer suggests that the cause of this might be in the stress inflicted on poorer MAWAM workers by mass immigration and other woeful trends:

Perhaps painkiller overdoses, mental health declines, reported pain, disability, dropping out of the labor force, lower wages, and The Big Unmentionable (immigration) all tie together. As Hispanics flooded in, lowering wages, blue collar whites felt less motivated to stay in the labor force as they aged and their bodies got creakier. Getting on disability requires, I imagine, an ability to get doctors and other authority figures to believe your account of musculoskeletal and/or mental health disabilities. The most effective way to get other people to believe you are disabled by physical and mental pain is to believe it yourself. And if you tell the doctor your back is killing you so much you can’t work and you persuade him, he’ll likely write you a prescription for some pills.

Or perhaps it was the 1960s Big Party generation finally burning itself out:

I think there is definitely a pattern in that coming of age in the Late 1960s / 1970s seem to have taken a toll on people, leaving them more vulnerable to dying of overdoses, suicide, and alcoholism later in life.

It’s kind of like how homeless people and AIDS sufferers started showing up in the 1980s. There are all sorts of explanations for these separate effects, some valid, some tendentious, but a common theme that’s almost totally overlooked today is that the 1970s were a Big Party and that took its toll on some people.

Then there is my 2013 post on health inequalities in the US, in which I noticed that unlike Blacks, Asians, and Latinos – whose counterparts abroad have universally lower life expectancies – US Whites are near the bottom of the life expectancy league tables of other majority White countries.

In contrast, US White life expectancy is equivalent to that in not fully developed Chile, and Denmark, the shortest-lived West European country.


This is pretty strange for a country supposedly dominated by “structural racism” and discrimination against its minorities (as many European and American Leftists allege).

Some speculations as to the cause of this pattern were advanced by myself and other people in the comments. One by the commentator Thorfinsson was particularly intriguing:

Extreme Hispanic apathy probably results in good mental health and thus longer lifespans. In America our abundance allows them to achieve the rusty pickup trucks, crappy houses with cars parked on the lawn, Tecate beer (‘scuse me that’s CERVEZA), and 24/7 access to their desired entertainment of telenovelas and pro-wrestling.

As for Asian-Americans being longer lived than their coethnics across the Pacific, I suspect America’s more laid back culture makes for better mental health than the cram and shame obsessed cultures back home.

White Americans on the other hand not only have less healthy lifestyles than their cousins across the pond, but are constantly bombarded with propaganda about how evil they and their ancestors are. Unlike less introspective and curious peoples, they are also given to introspection and moral neurosis. Not a good recipe for good mental or physical health.

But I doubt the explanation is as simple as any of those.

Note that the mortality prospects of middle-aged men in the developed European countries, not to mention Canada and Australia, have continued to improve throughout the 1990s and 2000s, even though many of them too have had a lot of Third World immigrants. That train left the station in the 1960s, not the 2010s, today’s angry rhetoric regardless.

And Yuropeans have been partying at least as hard as Americans since the 1970s. In fact, as someone who has lived in both the UK and the US, I can attest that the prevalence of binge drinking is FAR higher in Britain. Even so, it does not impose a heavy mortality burden even there. That is because British binge drinking mostly occurs amongst robust 16-25 year olds youngsters and only lasts for an evening. The sort of reckless binge drinking that afflicted Russia – and in earlier times, Finland - carried on throughout life and not infrequently degenerated into alcohol layovers lasting several days. Moreover, the “party hard” and recreational drugs culture in both Britain and the US is more of a Belmont thing, while the denizens of Fishtown have to work hard to put food on their family, and in jobs where they are much more likely to be tested for drugs besides.

As for Thorfinsson’s hypothesis, it is entertaining but not very serious. It is intellectual White liberals who read Howard Zinna and agonize over white guilt and have a growing cuckoldry fetish. They are also precisely those MAWAMs whose mortality rates have continued falling.

Otherwise, explanations from the “Left,” like increasing inequality .are not particularly persuasive either. Why didn’t it affect Blacks and Hispanics, who mortality rates continued falling? And besides, virtually the entire world got a great deal more unequal after 1990. Nonetheless, that didn’t stop Western Europe and other Anglo offshots from continuing to improve middle-aged male mortality rates.

Some suggest a connection between neoliberal reform and rising mortality. Contrary to that, after a brief mortality shock in the early 1990s, even decommunizing countries with their own “shock therapies” like Poland started to rapidly increase their life expectancy. This suggests that the primary cause of Russia’s mortality crisis in the 1990s and early to mid 2000s was not so much the much-hated “shock therapy,” as suggested in a famous 2009 Lancet article, but the specific fact of the collapse of the state’s authority, which expressed itself in the loss of control over the hard liquor monopoly, as well as the inability to check the proliferation of underground moonshine operations to serve the alcohol needs of the most far gone Russian alcoholics. At the end of the day, the simple fact was that hard booze got a lot cheaper, and there were many Russians who were willing to take advantage of it. Since vodka is so dominant as a driver of Russian mortality, to the extent that neoliberal reform was responsible for the 1990s Russian mortality crisis, it was because it made cheap hard alcohol more accessible to many Russians.

To wrap this up – while I don’t have any particularly good explanations for the great stagnation in MAWAM mortality prospects, I will suggest the following scenario:

As Case and Deaton state, from the mid-1990s, the US pharma industry has pushed all sorts of painkiller prescriptions including opioids onto the American population. Americans enthusiastically gobbled them up to deal with the bodily pains and discomforts caused by the contemporaneous advance of the obesity epidemic.

The increase in midlife morbidity and mortality among US white non-Hispanics is only partly understood. The increased availability of opioid prescriptions for pain that began in the late 1990s has been widely noted, as has the associated mortality (14, 20‒22). The CDC estimates that for each prescription painkiller death in 2008, there were 10 treatment admissions for abuse, 32 emergency department visits for misuse or abuse, 130 people who were abusers or dependent, and 825 nonmedical users (23). Tighter controls on opioid prescription brought some substitution into heroin and, in this period, the US saw falling prices and rising quality of heroin, as well as availability in areas where heroin had been previously largely unknown (14, 24, 25).

While rising obesity and the growing reach of the pharma industry has been prevalent throughout the First World in the past two decades, nowhere have both of these trends gone as far as in the United States. Possibly it is their combination that has magnified the effects of each to create a much bigger overall effect on the segment of the population most vulnerable to them?

So why, then, did this trend not affect Blacks and Hispanics? After all, their obesity crises are even bigger than those of White Americans. They are also far poorer than Whites. However, possibly their innately much more positive outlooks – Latinos clearly have a higher joie de vivre, while even the poorest Blacks have higher levels of self esteem than the richest Whites – might have translated into a tendency to use fewer pain meds, and perhaps greater defenses against getting seriously hooked on them or gatewaying into stuff like heroin and deciding to end their lives, as far more neurotic Whites are wont to do. In other words, Africanist rhetoric about the psychological dispositions of Sun People vs. Ice People does have some validity to it.

East Asians are relatively neurotic too. But they are also the one racial group in the US that is not having a major obesity crisis, plus their high average IQ ensures few of them live in depressed Fishtown anyway. Their mortality profile has therefore also continued to improve unimpeded.

In effect, maybe MAWAMs have won a sort of genetic anti-lottery: Intelligent enough to be deeply neurotic and prone to suicide, but not intelligent enough to almost entirely avoid Fishtown like Asian-Americans; and wealthy and privileged enough to have bottle of Vicodin as a retirement plan, but not wealthy or genetically endowed enough to avoid obesity on a large scale, which in turn further feeds into the pain meds and neuroticism spiral.

Last but not least, they live in a country where untramelled market forces and technological preeminence have resulted in the complete commercialization of agriculture and healthcare, paradoxically resulting in suboptimal outcomes like the spread of cheap empty carb diets that have led to mass obesity, and the usage of addictive and harmful pharma products to treat those very symptoms.

I am not sure this is anywhere near the correct explanation but I have yet to hear of anything more convincing.

Finally, it’s worth pointing out at least in passing that it is precisely these Fishtown MAWAMs who constitute the core of Donald Trump’s support base. The ordinary, lower class Russians hit hardest by the 1990s mortality shock – for instance, the Uralvagonzavod workers, so despised by Western liberal journalists – are likewise the class showing the biggest support for Putin. As such, this is just the latest if rather small commonality on which there is a kind of Trump-Putin convergence.


So 2015 will almost certainly set a new global temperature record. In so doing, it will also discredit the last lingering skeptic arguments that the 2010s “pause” in global warming somehow negates thermodynamics and a century of observations.


Source: NCDC. Red line is 5 year moving average. 2015 figures extrapolated based on Jan-May 2014.

Which does bring a new sense of relevance and perhaps urgency to Emil Kirkegaard’s recent post on tail effects in climate science.

Most of us here have heard of IQ bell curves. We also know that the effects are most pronounced at the edges of the graphs. For instance, assuming a 15 point S.D, a 100 IQ population will have 50% of its members above the 100 threshold, relative to 16% of an 85 IQ population. A large difference, but ultimately not that cardinal. But move the threshold to 160 – the approximate level of elite scientists – and the difference becomes onehundredfold. Certain intellectual achievements possible in a 100 average IQ society become impossible in an 85 average IQ society.


Being all about bell curves and thresholds it is not surprising that you would see similar dynamics in climate science.

Small changes in general conditions = potentially big changes in the frequency of extreme events (major new scientific discoveries, intense hurricanes and droughts).

Small changes in general conditions = rising probability of entirely unprecedented events (the Scientific Revolution, clathrate gun scenario – both of which, incidentally, were and would be greatly self-sustaining).

Many ecological systems are also highly susceptible to threshold effects. Liebig’s law states that crop growth is limited by the scarcest resource available, not the total sum of resources. Change net climatic conditions, and the most extreme events can create stresses that impinge on some minimum or other (e.g. max temperature, water availability), leading to sweeping dieoffs of organisms that had become adapted to previously stable steady states and are unable to change in time.

Humans are a sapient, highly K-selected species. They can adapt. A lot. This is a good argument against climate change denialism’s opposite, climate alarmism.

Still, there are limits to this too.

One example: There are models that indicate “zones of uninhabilibility” – levels of thermal stress that mammals just can’t withstand in principle – will start to appear past a 7C rise, and encompass half of the world given another 5C rise, and most of the world with another 5C.

Of course the probability of this is really low, according to conventional climate models, and virtually non-existent within the 21st century.

But then again the probability distributions of future temperature increase are themselves subject to the same rules of bell curves and thresholds. And most feasible climate shocks/changes in assumptions would shift those bell curves right, not left, making the formerly impossible, possible, or even likely.

Both effective altruists and more dispassionate strategic planner types would do well to bear this in mind.

• Category: Science • Tags: Global Warming, RealWorld, The Bell Curve 

Chanda Chisala’s article on black/white IQ differences has been making quite the stir in the HBDsphere. It is well worth reading in its entirety, as some of the points he makes – e.g., the evidence for high IQ amongst certain African ethnic groups such as the Igbo – are quite compelling and novel even to those well versed in this debate. But the central plank of his argument is ultimately a strike against the “hereditarian position” in IQ on the basis that the children of African immigrants are failing to regress to the mean.

The predictable response of the hereditarians is to adopt the environmentalist argument of super high immigrant selection to explain this unexpected trend: where some environmentalists propose that these immigrants are the most driven achievers in their countries, the hereditarians say they are the most intellectually elite, the ones from the topmost segment of the IQ bell curve in their countries; the outliers who got some lucky genes in an otherwise poor-gene environment. But like the hyper-driven-personality hypothesis, this argument cannot explain the equally, if not more impressive, achievements of their children: lottery winners never have children who also win the lottery. The stubborn refusal of their children to conspicuously regress to the much lower African genetic mean IQ (and not even to the African American mean IQ) predicted by hereditarians is simply inexplicable under their racial genetic hierarchy.

The problem is that African IQs from all social groups are highly repressed because of Third World factors like malnutrition and parasitic load. Very significantly so – around 15 points, or one standard deviation. When Third Worlders migrate to the First World, they experience a sort of “accelerated Flynn Effect” as their children with one plane ride get to enjoy advantages such as superior nutrition, medicine, etc. that had taken their host countries a century to build up. It’s not so much that regression to the mean isn’t happening but that it is being cancelled out by Flynn. This is a point that with apparently just one exception on the part of the IQ blogger Pumpkin Person has been overlooked in both Chisala’s article and the comments to it.

Let’s do a few back of the envelope calculations based on several plausible scenarios to demonstrate this.

The (commonly accepted) phenotypic IQ of Sub-Saharan Africans is typically estimated at 65-80, with 70 being a particularly common estimate. Their genotypic IQ is around 85 extrapolating from African Americans (there are issues such as ~20% Caucasian admixture, selection effects during slavery, diversity in Africa itself, etc. but let’s keep things simple). As is also well known, and cited by Chisala himself, African immigrants to both the UK and the US tend to be highly credentialed (more credentialed in fact than any other ethnic immigrant group). A reasonable estimate of their average phenotypic IQ would be 100, i.e. two S.D.’s above the Nigerian/Ghanaian/etc. average (three S.D.’s would be too implausible since there are so very few of them), and a genotypic IQ of 115.

Some at this point would object that the genotypic/phenotypic difference diminishes for higher IQ Africans since they’d be wealthier and more “elite” on average than the commoners, and hence have access to better food, medicine, etc. This is a good argument, but actual height data indicates that in the Third World entire populations are shifted down – both commoner and elites – relative to their counterparts in the First World. You can see the same phenomenon not only geographically but historically, e.g. average US Presidential heights, which increased by more than three inches between 1776 and today (and that is despite the US being very well fed by global standards even two centuries ago).

Assume the standard method of calculating offspring IQ: The average of the father’s and mother’s IQs, plus some degree of convergence to the mean of the parents’ racial genotypic IQs, i.e. what is otherwise known as regression to the mean, which is usually estimated at 40%.

Now let’s assume our African immigrant is an economic migrant, i.e. an educated and credentialed Nigerian, as opposed to a semi-literate refugee from wartorn Somalia or DRC. (Average IQ of Black African immigrant offspring in the UK is about 93 according to the CAT tests, as Chisala points out and as I mentioned three years back. Since this group will include a lot of these very low IQ Somali/Eritrean/etc. refugees, the average IQ of children of African economic migrants should logically be a lot higher, i.e. maybe around the White average. This hypothesis will be further supported below).

Let’s assume our African immigrant is male for simplicity’s sake – plus the fact there are somewhat more men than women amongst African immigrants anyway – and that he made some of the following marriage choices:

  • Marries another cognitively elite Black immigrant woman just like himself, i.e. phenotypic IQ of 100, and genotypic IQ of 115, resulting in average offspring IQ of 107, i.e. standard “model minority”-level performance. It would not be particularly surprising or strange if Britain’s best performing secondary student in one particular year – Chidera Ota, prominently featured in Chisala’s article – was to come from the high end of this particular group’s bell curve.
  • Marries a Black immigrant woman whom he married back at home, thus she did not undergo the selection for higher IQ that is the selection process for economic migrants, thus has a phenotypic IQ of 85 and a genotypic IQ of 100. Resultant average IQ of offspring: 101.
  • Marries an African American woman with a phenotypic and genotypic IQ of 100 (i.e. associational mating). Expected offspring IQ: 101.
  • Marries a Caucasian woman with a phenotypic and genotypic IQ of 100 (i.e. associational mating). Expected offspring IQ: 103.

Here’s a summary:

Genotypic IQs F (ego) F (race) M (ego) M (race) S & D
Black immigrant (elite) + Black immigrant (elite) 115 85 115 85 107
Black immigrant (elite) + Black immigrant (nonelite) 115 85 100 85 100.5
Black immigrant (elite) + US Black (assoc) 115 85 100 85 100.5
Black immigrant (elite) + US White (assoc) 115 85 100 100 102.5

So you see the pattern? Cognitively, the children of African immigrants are basically Caucasians, i.e. a standard deviation above African Americans, but nowhere close to an elite cognitive group like Ashkenazi Jews or US Indians who are almost a full S.D. above Caucasians. They will come to form a population group with a fixed cognitive profile set around 100 or slightly higher (since regression to the mean stops after one generation), and as such they will do fairly well socially and economically. Most likely, better then Caucasians, since they will benefit from affirmative action policies in education and employment designed to benefit 1 S.D. duller African Americans while in fact being cognitively similar to Caucasians (think Ashkenazi Jews counting as Whites in university admissions). All of this, in fact, seems to be happening in real life.

Chisala might not have “disproven” the hereditarian or HBD position (at least its nuanced, non-White Nationalist part that pays due respect to auxology and Flynn dynamics). But he did demonstrate that African immigrants are doing fairly well for themselves. Indeed, as a cognitively elite Zambian immigrant, Chisala would presumably be quite the expert on it.

And don’t get me wrong, this is a genuinely attractive message, at least so long as you are an egalitarian blank slatist (US liberals), a cultural supremacist (US conservatives), or even a cognitive elitist who doesn’t attach any value to racial particularism. Liberals can point to them as living proof that Blacks are just as mentally gifted as Whites, and it is structural racism which is keeping African Americans down. As such, there needs to be more affirmative action, more racial quotas, more laws against hate speech, etc. to end it. Conservatives too would welcome it. They will praise the work ethic and family values of these African immigrants, citing the lack thereof amongst African Americans as the real cause of why they lag so much behind other ethnic groups in the US. That in turn will enable them to continue to wage their culture war against genuine African American culture. The economists and economic rationalists will be happy. Surely this is a good reason to expand immigration from Sub-Saharan Africa? More jobs, more skills, more entrepreneurialism. If anything, the only unhappy people would be the White Nationalists, and who cares about those primitive troglodytes anyway?

Even so, it should be pointed out that this argument can be critiqued even from morally universalistic, if still cognitively elitist, principles. An argument could be made that accepting African cognitive elites might improve the host societies, at least in the views of non-nativists: By increasing the size of the middle class, solving skills shortages, and providing fuel for the egalitarian narrative which – whatever its problems with logic, reason, and data – is nonetheless morally superior to “kneejerk” ideologies based on real racism and exclusion.

But proponents of these views should also seriously consider what effect their policies are going to have on the African societies that the high IQ immigrants are abandoning. It is becoming increasingly accepted in development economics that countries with high numbers of “smart fractions” – either via a high average IQ, like China, or at least a substantial “Brahmin” class, like India or South Africa – tend to do much better than low IQ and cognitively homogenous countries, like… most of Sub-Saharan Africa. The region has very few cognitive elites to start off with, and a large percentage of them are getting sucked up into Western societies that frankly have orders of magnitude less need of them than their own cognitively-strapped countries. These losses are not just financial, though those are no small matter even just by themselves: It takes a lot of money to train a doctor or an engineer, money which Sub-Saharan Africa generally doesn’t have. Even worse are the cognitive losses, as the stock of competent administrators and businessmen dwindles, reducing the size of Africa’s smart fractions even further and resulting in even more poverty and dysfunction.

It is adaptive to adopt the language of the Left on this issue. Enabling educated African immigration at a large scale is Western cognitive colonialism against the African continent, and is nothing more than a subtler version of the resource rapine that it subjected Africa to back in the days when imperialism was overt and didn’t bother concealing its iron fist beneath a velvet glove. Colonialism is bad and morally unjustifiable, and all foes of the global plutocratic elites must unite against it.


Why is the HBDsphere so damn interested in IQ, anyway?

While I can’t speak for the “movement” at large, in my own case the interest stems from the fact that it explains so much about our world. (In fact, I was interested in this topic long before I discovered HBD, Charles Murray, Jensen, Lynn, Rushton, etc). In particular, it convincingly answers the central question of political economy since the days of Adam Smith – why are some nations poor and some nations rich? After all the long debates about the merits of free markets over industrial policy, over the influence of institutions versus geography; after all the human miseries suffered from zealous adherence to some ideology or other, from the Great Leap Forwards in China to the capitalist disaster zone that neoliberalism made of the ex-Soviet Union in the 1990s, after all these blunders, mishaps, and occasional horrors committed in search of the Answer, we find that it mostly boils down to just one ultimately rather banal thing: Some peoples are more intelligent than others, work more efficiently, and hence enjoy greater wealth; and as a result of said greater efficiency, capital naturally flows towards them, further multiplying their output relative to the backwards countries.

In extreme cases, institutional factors do make a huge difference. Countries with a socialist (central planning) legacy – that is, East Central Europe, the ex-USSR, China, Vietnam – are still systemically much poorer than countries where markets have long functioned with at least some minimal degree of freedom, even though their IQs do not differ much from those of the US, Western Europe, and Japan. Stress on the “minimal” – beyond some fairly modest point of economic freedom and basic political stability, it appears that institutions and economic openness offer rapidly diminishing returns; for instance, the Belorussian economy, which is still 90% state owned and a dictatorship, was actually the most successful of all the ex-Soviet economies after 1991, including even economic reform stars like Estonia (actually Azerbaijan performed even better, but it was helped by a massive oil windfall). Speaking of which, on the other side of the correlation curve you have countries with a very big resource windfall per capita – Saudi Arabia, South Africa, Norway, etc. – which are much richer than the level “warranted” by the quality of their human capital. But once we take these two groups out of the equation, and also get rid of tiny finance-orientated city-states, the correlation between national IQ and economic wealth becomes extremely close – a fact all the more remarkable when we consider that estimates of both national IQ and GDP per capita (PPP) can vary fairly widely.

Here is a graph I made from 2013, which shows a correlation of R2=0.84. This is entirely in line with other similar calculations by professional psychometricians like Heiner Rindermann.


That said, as I noted even back then, there are some curious outliers in the “capitalist normal” countries. Moreover, these outliers tend to be concentrated at the wealthy frontier: The US is a positive outlier, whereas Japan, the East Asian countries, Finland, and to a lesser extent, the “Anglo offshoots” (Canada, Australia, New Zealand) are negative outliers.

As economic historian pseudoerasmus pointed out on many occasions, while national IQ is central to the growth story for low-income and middle-income countries in catch-up growth, for already developed nations with their standard 100±5 IQs the benefits accrue overwhelmingly to those with more “marginal” advantages, such as those having somewhat better institutions, or conditions for doing business. This is a hypothesis that makes good theoretical sense, but a closer examination reveals that things might not be that simple. The Anglo nations have what are widely regarded as very good institutions, courts, and conditions for business, but they are relative underperformers, even (especially) when productivity is taken into account. Japan has a 5-7 IQ advantage over, say, Italy, but its GDP per capita (PPP) is similar, while its productivity is significantly lower – even though Japan rates higher on ease of business and perception of corruption indices. There must be other factors that are at play, and I will admit that I am unsure as to what they are. But before we get ahead of ourselves, let’s examine the data in greater detail.

This is the data table I used in the charts in this article:


I limited myself to countries that satisfied the following list of conditions:

  • Those that had a substantial population, at least 5 million or more (smaller countries tend to be financial/tourism hubs with too much artificially inflated wealth).
  • Did not have a central planning legacy that depressed their wealth (so, no country from the socialist camp during the Cold War) or a big resource endowment per capita (so, out go countries like Saudi Arabia and Norway). We are talking primarily of the old OECD members minus Mexico and Turkey.
  • Are wealthy, i.e. have a GDP per capita of at least $20,000. We already established that the correlation between national IQ and wealth in poorer countries is very good; the question we now want to answer is why it begins to break down at the edge of the graphs.

GDP per capita is measured in purchasing power parity terms because it better reflects the real level of production and living standards in any country and accounts for short-term currency fluctuations. Productivity is the GDP per capita (PPP) adjusted for the labor participation rate and average hours worked per country, i.e. GDP per hour worked. Most of the data I got from the World Bank or the OECD, though I frequently had to look for other sources in the cases of Taiwan, Hong Kong, and Singapore. The regional averages were calculated as a weighted population average of each regional label. National IQs were derived from the average of the Math, Science, and Reading component in PISA 2009.

The first series of graphs show regional and country national IQ versus GDP per capita (PPP) data, with the bubbles scaled for population size.


Here, at an amalgamated level, we already see a distinct pattern: Americans are much richer than they “should” be, whereas East Asians are much poorer. But curiously, the Anglo offshoots are closer to East Asia here than they are to European-stock populations, so it is not at all obvious that it is an HBD issue.

And now for the country specific data.


While all the countries of Western Europe hew close to the line of best fit, again there are three major exceptions: The US to the upside, and Japan and South Korea to the downside.

The obvious and immediate explanation is that some countries have greater labor participation rates, and/or work more hours. So a natural adjustment would be to calculate the GDP per capita generated per manhour of work and see if that explains American and East Asian exceptionalism relative to Western Europe.

I would note at the outset a few caveats to bear in mind. First, in many cases – certainly regarding the US vs. Western Europe – a large share of the differences in overall labor participation is explained by the greater percentage of American youth and the elderly in the workforce by dint of its less generous welfare state (left-wing view) and less restrictive labor laws (right-wing view). Increasing the labor participation of both of these groups will yield only marginal improvements in total output because they are far less productive than people in their prime. Likewise, working longer hours is of questionable value, because workers will presumably either get more tired and less productive, and/or end up wasting time due to Parkinson’s Law (“work expands so as to fill the time available for its completion”). On paper, Greeks work far longer than Germans… if by “working” you mean drinking coffee. The Japanese have it even worse; extra hours “worked” there means pretending to work until the boss leaves. Germans, on the other hand, actually get all the important stuff done quickly and efficiently, and get to enjoy a big chunk of the rest of the day. Americans tend to work long hours and productively.

Even so, on average, productivity is probably more impacted by national IQ than the level of GDP per capita. At the very least, by far the biggest discrepancy – that between the US and Western Europe – largely vanishes after this adjustment.


Although the gap between the Westerners (barring the Anglo offshots) and East Asia then becomes even wider.


Now that I’ve laid out all the data, time to consider some hypotheses for American exceptionalism and Asian mediocrity. At the outset, I should thank pseudoerasmus and James Thompson for participating in the Twitter discussion where many of these ideas were initially raised, analyzed, and critiqued.

1) Historical Leadership. The US has been at the technological edge since its inception; Britain industrialized a bit earlier, but there was never a significant gap in per capita output. Moreover, it burst clear of everyone else in the wake of World War Two, which devastated most of Europe. But 70 years is more than enough time to recover and catch up. In fact, that is precisely what happened: The first part of the period was of the Wirtschaftswunder, the Trente Glorieuses, Il Sorpasso, the Japanese Miracle, and the East Asian Tigers. But ever since 1990 or thereabouts, longterm per capita growth rates in developed Europe, the US, and Japan – for all the rhetoric about “European stagnation” and “Japan’s lost decade” – have basically converged. Here is Paul Krugman’s famous chart on this:


The only two major countries for which uncompleted convergence could still be a significant factor are South Korea and perhaps Taiwan. But any further relative gains on their parts, if the past five years are anything to go by, are going to be slow and marginal. For all its dazzling PISA performance and blisteringly rapid economic catchup, Korea’s productivity levels are still equivalent to those of Portugal, which has traditionally been the poorest country in Europe with the exception of a few Balkan backwaters, and Greece, which is at the tail end of a multi-year depression. Both Portugal and Greece have national IQs almost 10 points below Korea’s.

2) Immigration, Population Composition, and IQ Structure. But if anything, this makes the puzzle even more acute. We know that in recent decades Europe received a lot of immigrants, whose IQs are far lower than those of the natives and show no signs of convergence. The US, meanwhile, is host to two major population groups – Blacks and Non-White Hispanics – with consistently subpar IQs that together make up more than 20% of the population. If anything, that should depress productivity, which probably partially explains New Zealand, where ~90 IQ Maoris and Pacific Islanders also make up slightly more than 20% of the population. In contrast, high IQ and ethnically homogenous Japan, Korea, and Finland all underperform, as do Canada and Australia, which are not ethnically homogenous but do make sure to have cognitively elitist immigration policies.

That said, there are two reasons why this effect might not be all that powerful for both Europe and the US. First of all, in both Europe and the US, these NAMs (Non-Asian Minorities) have a relatively greater demographic preponderance amongst the youngest cohorts, whose members are either not in the workforce at all (infants, schoolchildren, students) or aren’t able to contribute much anyway (they are younger workers with less experience; while they might be quicker on the uptake, older workers often beat them with experience, especially in the more cognitively intense professions). This will likely do Europe and the US no good in the longterm, as they develop ever larger, ethnically distinct cognitive/economic underclasses that will pull down overall GDP per capita and productivity, but this probably just doesn’t play that big of a role… for now.

Moreover, at least in the US, the situation is further improved by the presence of sizable “smart fractions,” which have a disproportionately large positive effect on overall GDP per capita according to many psychometricians like Heiner Rindermann. These smart fractions are both ethnic – most notably, the 2% of the population that is Jewish – as well as the result of a global cognitive clustering effect (many of the world’s brightest and most ambitious people are inordinately drawn to US universities and Silicon Valley). It would also explain Israel’s overperformance – while the national IQ is depressed by Arabs and Sephardic Jews, and the economy is burdened by Haredi welfare bums, the Ashkenazi Jewish cognitive elite still manages to compensate for all that and elevate GDP per capita above the global correlation curve.

Some thinkers have speculated that the reason for East Asian underperformance is that although they have higher IQs than Whites, they have fewer very high IQ people (“smart fractions”) because of narrower distributions. The only problem with this very plausible and reasonable theory is that it is almost certainly completely wrong. The PISA tests show that East Asian S.D.’s are no different from those of European countries (though Finland’s, curiously enough, is lower at a statistically significant level). This theory could furthermore be disproved by a cursory glance at a list of names of members of the US Mathematical Olympiad teams – since 2010, fully 75% have either Chinese or Vietnamese last names.

Another, more plausible theory, advanced by Griffe de Lion as well as Rindermann, is that some forms of IQ, most notably verbal, in which the European-East Asian gap is very modest or even non-existent, are relatively more important for economic success than mathematical aptitude, where the gap is substantial, or visuospatial ability, where it is as big as 10 points. (Lynn actually claims that Europeans are verbally smarter, but PISA shows otherwise, though it does confirm that the Asian/European gap in verbal IQ is much less than the mathematical one). This would largely though not fully resolve the puzzle of East Asian underperformance, though you would still have to convincingly explain why verbal IQ in particular is more important for economic prosperity than, say, just g.

Finally, we must also bear in mind that gaps in cognitive ability can increase or decrease with age. Most tests of intelligence are performed on children or teenagers because it is easy to get big, representative samples from them. But what is true for under-18s may no longer be true for the mid-25s, when fluid intelligence is maximized (the ability to learn), or the 50s, when crystallized intelligence (total stock of applicable knowledge and experience) is maximized. For instance, while male and female IQ tends to be similar, though the latter have famously narrower distributions, it appears that at least on progressive matrices tests, a 5 point gap opens up during the 20s in favor of men and persists thereafter. Just as a significant part of the Flynn Effect can be explained through faster maturation due to better nutrition and parasitic disease control during the past century, so the biological reality that men fully physically mature about five years later than women could explain the appearance of a gender IQ gap in adulthood. Could there be similar processes at work in regards to different ethnic groups? Certainly it seems to pertain to the famous Black-White IQ gap, which increases with age, and very substantially so. Note that productivity in most smart fraction professions peaks in the 50s, when crystallized intelligence is maximized.

Could it be that the Asian IQ lead over Europeans in childhood and adolescence closes or even reverses with age? I have no idea. I was unable to find any hard statistical data on this. (Do tell me in the comments if you have). So for now it must remain but a stab in the dark hypothesis. However, if this is indeed the case – that the Caucasian/Asian IQ gap diminishes or even reverses with age, or put another way, that the much maligned “old white man” really is the smartest dude around – would be able to fully explain Asian underperformance, especially if paired with the observations on the relatively greater importance of verbal IQ as it pertains to economic prosperity.

3) Institutions and Economic Freedom. We know that in the most extreme cases – for instance, central planning under Communist regimes – lack of economic freedom leads to substantially inferior economic outcomes relative to what they might have been under market conditions. Beyond some minimal level, however, the role that increasing economic freedom plays seems to be subject to rapidly diminishing returns. Chile is one of the freest economies on the planet thanks to Señor Pinochet, Argentina is the exact opposite – but their GDP per capita is virtually the same, as – who’d have guessed it? – are their national IQs. But Chile and Argentina are middle-income countries, so institutional differences might not be making themselves felt as much as in fully developed countries.

So let’s look at the biggest outliers and the quality of their instutitions and business environment, as proxied by the World Bank’s Ease of Doing Business indicator and Transparency International’s Corruption Perceptions Index.


Now this is hardly a rigorous statistical test, but it’s clear that there’s little or no evident connection. All negative outliers are well within the world’s top quintile by ease of doing business – unlike, say, Italy (56th) and Greece (61st), which although poor by OECD standards are not however major outliers on the IQ charts. Finland, Australia, New Zealand, and Canada are some of the freest economies and best places for business on the planet.

The only two negative outliers which might have a significant problem with corruption are Taiwan and Korea. Now Taiwan is… a strange case. According to one poll, also carried out by Transparency International, 36% (!) of them said they paid a bribe in the past year. This is almost certainly a statistical fluke. On the other hand, only 2% of Koreans said they paid a bribe in the past year; only Denmark, the UK, and Norway, all countries that everyone agrees have minimal levels of everyday corruption, claimed to have paid fewer bribes. Assuming they weren’t lying, perhaps Korea’s rating on the CPI is overly pessimistic. Regardless – that’s still a lot better than most of the rest of the world, including rich non-outlier countries like Italy and Greece, both of whom are joint 69th on the CPI rankings.

4) Economies of Scale. The US is a single integrated market of more than 300 million people with a common language and set of laws and institutions, which enables massive economies of scale. To a lesser extent, this is also the case in the EU, which now has common markets but is still divided by political-fiscal barriers that are make life very difficult for at least some of their members, such as Greece and the Mediterranean countries generally. While Japan might not be of continental proportions, it does have a very substantial population – at 127 million, it is more than one and a half times as big as Germany’s – so it should enjoy most of the benefits from this as well. This factor would have a negative effect on Australia and especially New Zealand, which have low populations themselves and are geographically distant from other big markets.

5) Geography. The US has some of the best geography for industrial civilization on the entire planet: Multiple excellent ports on both seaboards,and the massive Mississippi River and Great Lakes water network that interconnects the entirety of its central core at next to no cost. Europe has middling geography, while Japan’s is poor and prone to natural disasters. Australia and New Zealand are very isolated, making economies of scale unrealistic. That said, the role of geographic factors in our days of dirt cheap oceanic bulk transport and dense railway networks is presumably quite modest.

6) Resource Windfalls. I purposefully excluded those countries where the economy is very clearly radically inflated by large resource windfalls per capita, such as Norway, but even so this factor is still significant for Canada, where natural resource rents as a share of GDP is at 4.4%, and Australia, where it is 8.0%. Combined with their relatively high national IQs and careful immigration policies, their “underperformance” becomes more puzzling, if anything. Even though the US also has a very substantial resource endowment, its effect is swamped by the overall size of its economy; natural resource rents as a share of GDP are a mere 1.3%.

7) Financial Windfalls. Might be a factor in Singapore’s good (relative to the rest of East Asia) performance. Why not Hong Kong? Because after it rejoined its motherland, China had no particular reason to favor it over, say, Shanghai or Guangdong, and quite a lot of disincentives to, considering the pro-Western tilt of many of Hong Kong’s elites. Singapore, however, was free to continue its project of becoming the world’s third major financial hub after London and New York, and its skyhigh GDP per capita (though unremarkable productivity) is a result of that. However, as mentioned at the start, I purposefully excluded places that were so small that a financial or tourism sector could play a dominant role, such as Luxembourg, Monaco, and Liechtenstein, all of which have ridiculously inflated GDP per capitas. Once you get to a British scale, let alone an American one, the impact of global financial centers like London or New York on GDP per capita becomes swamped by the overall economy.

8) American Alpha. Artificially lower risk premiums in the US means foreigners are willing to “irrationally” invest in American bonds at rates well beyond equilibrium. Here is Willem Buiter’s explanation of this phenomenon:

Some of the excess returns on US investment abroad relative to foreign investment in the US may have reflected true alpha, that is, true US alpha – excess risk-adjusted returns on investment in the US, permitting the US to offer lower financial pecuniary risk-adjusted rates of return, because, somehow, the US offered foreign investors unique liquidity, security and safety. Because of its unique position as the world’s largest economy, the world’s one remaining military and political superpower (since the demise of the Soviet Union in 1991) and the world’s joint-leading financial centre (with the City of London), the US could offer foreign investors lousy US returns on their investments in the US, without causing them to take their money and run. This is the “dark matter” explanation proposed by Hausmann and Sturzenegger for the “alpha” earned by the US on its (negative) net foreign investment position. If such was the case (a doubtful proposition at best, in my view), that time is definitely gone. …

There is no chance that a nation as reputationally scarred and maimed as the US is today could extract any true “alpha” from foreign investors for the next 25 years or so. So the US will have to start to pay a normal market price for the net resources it borrows from abroad. It will therefore have to start to generate primary surpluses, on average, for the indefinite future. A nation with credibility as regards its commitment to meeting its obligations could afford to delay the onset of the period of pain. It could borrow more from abroad today, because foreign creditors and investors are confident that, in due course, the country would be willing and able to generate the (correspondingly larger) future primary external surpluses required to service its external obligations. I don’t believe the US has either the external credibility or the goodwill capital any longer to ask, Oliver Twist-like, for a little more leeway, a little more latitude. I believe that markets – both the private players and the large public players managing the foreign exchange reserves of the PRC, Hong Kong, Taiwan, Singapore, the Gulf states, Japan and other nations – will make this clear.

Such a painful adjustment is indeed what has been occuring in Mediterranean Europe. But note that his pessimistic and falsifiable predictions specifically in regards to the US – that there would be “a global dumping of US dollar assets, including US government assets” – have yet to happen.

9) Cheaper Land and Energy Inputs. Land in the US tends to be pretty cheap, outside the North-East, the SF Bay Area, and a few other prestige locations. Much cheaper than in developed Europe or in Japan. Energy inputs are also lower, specifically in relation to fuel, which is taxed at much lower rates than in Europe or Japan. This should lower the cost of business across the board and increase overall thoroughput.


The only problem? The countries right next to the US here are Canada, Australia, and Japan – some of the biggest negative outliers.

10) Hedonics and GDP Fiddling. There are various claims that the US is really… generous at calculating its GDP. Perhaps “American exceptionalism” is just a statistical artifact? I haven’t studied national accounting practices on any detailed level, though pseudoerasmus has and he is skeptical, and I’m also a bit put off that a lot of the sites that make these claims tend to be libertarian goldbugs and LaRouche types. That said, I will admit to an intuitive sense that there might be something behind this. As the commentator Lazy Glossophiliac has pointed out a few times, many things that are either free or cheap in Europe and most of the rest of the world can be pretty damn expensive in the US. The healthcare industry is just the most blatant (and perhaps grotesque) example, accounting for a prodigal share of American GDP while delivering population health outcomes that are, in general, nothing to write home about. Americans dine out much more frequently than Europeans – the labor of chefs and waiters appears in GDP, while creating a home cooked meal does not. You can probably extend this to quite a lot of different things.

American Exceptionalism, East Asian Mediocrity

To sum up: At the technological edge of high IQ/high wealth per capita, there appears an interesting and puzzling disjoint between the US, which is a big positive outlier, and Japan and the rest of East Asia, which are big negative outliers. Adjusting for labor participation and hours worked, to get in effect a measure of productivity, largely resolves “American exceptionalism” relative to developed Western Europe, but if anything widens the chasm between the West and East Asia even further. Moreover, Australia, Canada, and New Zealand – all Anglo-derived settler societies that are culturally close to the US and enjoy low corruption and good institutions – are moderate negative outliers.

In general, possible explanations are either critically flawed in some way, or only partially explain some difference while deepening the puzzle around some other difference. For instance, cheaper energy inputs might appear to partially explain why the US is a positive outlier, but then it would make the question of why Canada and Australia are negative outliers – even though their fuel taxes are also low – all the more inscrutable. Beyond some fairly minimal conditions like having free markets, the quality of institutions do not appear to play any significant role.

Still, it is possible to identify a few factors that likely play some important role:

1) Economies of Scale – Clearly give the US and to a lesser extent, continental Europe, a boost. Many of the negative non-East Asian outliers are relatively isolated island nations with small populations, especially Australia and New Zealand.

2) Smart Fractions and the US – The two biggest rich positive outliers, the US and Israel, have many duller ethnic minorities but also enjoy an Ashkenazi Jewish cognitive elite. Moreover, a significant percentage of the world’s smartest and most ambitious people immigrates to the US.

3) Personality, Culture, IQ Structure – Apart from the partial exception of Singapore – a fact that is mitigated by its status as a financial city-state – all East Asian states economically underperform relative to where they “should” be at. This is The (Other) East Asian Exception. This leads me to believe that the cause of this must be something that is culturally or even biologically common to the region. Maybe it has something to do with a relative lack of creativity in terms of personality (in Nobel Prizes per capita, as in GDP per capita, Japan far more closely resembles Italy than Germany; while Korea has yet to win a single real, i.e. non-Peace, Prize); maybe it is a consequence of East Asia’s shame culture, which is more socially stultifying than Europe’s guilt culture, and can lead to inefficiencies like paying undue respect to an incompetent boss who just happens to be older; maybe it is simply that East Asian IQ is simply “worth” about 5 points less than European IQ due to its particular quirks or structure (specifically, the fact of the Asian advantage in verbal IQ being much more relatively modest relative to Whites); and/or maybe – and this is by far the most tentative hypothesis here – it might be that the East Asian IQ advantage over Europeans disappears in adulthood, meaning that Europeans still retain a relative preponderance in the fraction of smart 40-50-60 year olds who are responsible for most of the greatest scientific and cultural accomplishments.

4) Other Factors – This leaves only Finland and Canada to explain. Finland’s underperformance might be due to the lower S.D. of its national IQ, if the PISA tests are accurate. Moreover, Richard Lynn pegs Finnish IQ at a standard British 100. Perhaps, for whatever reason, Finns simply perform unduly well on PISA. If Lynn is correct, it would not even be an outlier. Or it could be their particular psychological profile, which might be unfavorable for the expression of ingenuity. Canada could be a modest negative outlier because it borders the US and loses too big a percentage of its smartest fractions to its giant southern neighbor.

• Category: Economics, Science • Tags: Economic Theory, IQ, Psychometrics, Race/IQ 
Anatoly Karlin
About Anatoly Karlin

I am a blogger, thinker, and businessman in the SF Bay Area. I’m originally from Russia, spent many years in Britain, and studied at U.C. Berkeley.

One of my tenets is that ideologies tend to suck. As such, I hesitate about attaching labels to myself. That said, if it’s really necessary, I suppose “liberal-conservative neoreactionary” would be close enough.

Though I consider myself part of the Orthodox Church, my philosophy and spiritual views are more influenced by digital physics, Gnosticism, and Russian cosmism than anything specifically Judeo-Christian.