Chess psychometrics – The length of games

One of the easiest metrics to extract from chess databases is the number of moves certain games contain on average. This can be seen as a measure of grit – both the determination to beat the other player even if it takes 7h and 120 moves to do so, and also the ability to hang in there and defend bad positions for a long time to save the draw.

Unfortunately at the same time it can also be a measure of caution: Very aggressively played games tend to be short, such is the nature of risk taking.

Nevertheless, we take the opportunity to shine further light onto the male-female over-the-board-relationship. For this little investigation we look at the length of games between women and men, men and men, and finally women and women. We use a different method here to identify female players. Instead to trying to connect the players to the Fide player database, we just classify the players given name into male or female. This provides us with a few hundred games for each combination of male and female where both players are rated above 1500 Elo.

We see that men playing white against women probably try a little shorter to turn the first move into a win than against men, while women generally play somewhat longer games. But overall the differences aren’t too big. Not terribly exciting but another indication, that men probably on average do not try harder to beat women than to beat other men.

Random Thoughts – Hierarchies

I
Women and men are organized into different social hierarchies that don’t usually overlap all that much. Female hierarchies have stronger components of conformism and imitating the behavior of the top, ah, bitch. Male hierarchies are more based on dominance and skill.

One thing that happened with the emancipation of women and the integration of women into the workforce, was that women are now to a much larger degree part of male hierarchies. I believe the complaint that women aren’t taken as seriously even with superior skill is based on women being on average lower status than similarly skilled men. That is simply the result of women not being as good as men at playing the male hierarchy game.

And why would they be? Height and strength are only the most obvious deficiencies that women have on that playing field. And of course the incentives are very different. Men at the top of the hierarchy have access to the best mates. Women at the top of the hierarchy stay childless.

Unfortunately I also suspect that male hierarchies are a lot better at getting things done. For example men are able to work with people they dislike, possibly because failure to cooperate for men had immediate very bad consequences in the evolutionarily typical male endeavors. So it seems unlikely that this problem has an easy solution.

II
Additionally it seems to be the case that female hierarchies have become more dysfunctional in our day and age than male hierarchies. Imitating the top bitch is all well and good if you are living in a village or a band of hunter-gatherers. If the top bitch is a celebrity of unattainable perfection not so much.

If your conformism is hijacked by the fashion industry on the one hand and crazy political ideologues on the other, the result is also not very pretty. It always strikes me as ironic that many of the problems of modern women/girls that are blamed on the patriarchy are the direct result of intra female competition with hardly a (hetero) male involved.

HGH in high-level sports

Years ago when Jamaican sprinters started to dominate the 100m/200m dashes, it was whispered that they had unusual jaw growth, some of them needing braces even in their early twenties. The allegation was that they used HGH, human growth hormone, that besides improving performance had side effects on the growth of extremities.

These side effect can occur naturally, usually if there is a tumor in the pituitary gland and very high amounts of growth hormone are created. This leads to a condition called Acromegaly, where jaw and brow ridge and basically everything else grows to almost grotesk proportions. André the giant may be most famous example, or Richard Kiel playing Jaws in the Bond movies.

At one of the recent Olympics, maybe 2012 or maybe 2016, I was struck by how similar the two superstars Micheal Phelps and Usain Bolt looked in terms of their body shape. Long, lean with a strong jaw and big hands and feet (Usain Bolt has shoe size 13, Phelps size 14).

I began wondering whether HGH-abuse, probably starting already in teenage years, was playing a major role in shifting the borders of human performance in high level sports. In swimming the never-ending flood of world records was explained by the improvements of full body swimming suits. Of course when these suits were banned records kept falling.

HGH leads to detectable changes in the facial structure. And Deep Learning methods allow us to turn pictures of faces into vectors that encode facial structures. This gives us a way to empirically assess whether faces of world class athletes have been shifted towards the facial structure typical for people suffering Acromegaly.

These people suffer from Acromegaly

Because this is half-assed science and not full-assed science and there is, as always, a severe lack of graduate slaves, we will only manage a proof of concept. For this we select the male Olympic finalists in swimming for the years 1976, 1992 and 2016. These three years fall into three different phases of HGH-abuse: HGH has been used in high-level sport since 1982 and it was possible to detect it’s abuse since the early 2000s. So 1976 is pre-HGH, 1992 is HGH-time with no risk of being caught and 2016 is HGH-time with the theoretical possibility of being caught.

We also select the a couple of people suffering Acromegaly, as given by the wiki-article on the subject. And as a control group a number of normal guys, by googling ‚random guy‘ and ‚normal guy‘. We use a model that creates face embeddings, that is it detects faces in a picture and assigns vectors to these faces that encode facial structure.

We then compare the average face vector for our Olympic finalists and normal guys with the average Acromegaly face vector. Our results show that the distance to the facial structure typical for Acromegaly was biggest in 1976 with 0.671, smallest in 1992 with 0.625 and a little bigger than 1992 in 2016 with 0.634. The normal guys have on average a face 0.658 away from the Acromegaly face. The standard deviation of the distance of normal guys is 0.058, so the difference between 1976 and 1992 is 0.78 standard deviations.

This is certainly a big difference and the pattern of differences between 1976, 1992 and 2016 fit the different phases of HGH-abuse very well. The only thing that should give us pause is the fact that my normal guys are closer to the Acromegaly face than the 1976 athletes. More work is needed, but probably not by me!

Random Thoughts – Morality

I
It confuses me that vegans state animal welfare as a reason for foregoing meat, despite the fact that the animals vegans claim to care for largely wouldn’t even exist if they weren’t eaten by humans. Even granting that non-existence is better than some versions of factory farming, clearly the most moral course of action, if one gives moral valence to animals, is to buy as much meat from happy chickens as possible. This way one supports the happy existence of thousands of animals throughout one’s life. And if you buy meat, you might as well eat it.

II
Throughout history philosophers have tried to create consistent theories of moral behavior. Much like vegans they don’t usually even get close. I believe the reason for this is that the human mind has moral intuitions that are shallow situation filters. Each evolved to trigger a behavior in a certain social situation that is advantageous in the long term. But if situations don’t usually trigger several moral intuitions there is no reason to believe that these intuitions are consistent with each other. Trying to build a consistent framework on top of these inconsistent intuitions will always fail because thought experiments like the famous trolley problem easily expose the fault lines where our intuitions cannot be stitched together.

III
I believe Singers arguments also amount to the extrapolation of shallow filters. Singer argues that geographical distance should have no effect on moral judgements, so that the child you could save on a different continent is equivalent to the child you might pull out of a river right here. This seems very reasonable because imagining a child dying in Africa and imagining a child dying right here certainly feels very similar. It both feels like imagining.
Of course in reality there is a gigantic difference in our moral intuition concerning a child we see drowning in front of our eyes and a statistic. Our intuition is that we have much stronger moral obligations towards people who are present, who we know or who are related to us.
(Reading Singer’s wikipedia page right now, it seems he also managed to be wrong about everything else. Of course that’s probably the only way to make a career in philosophy.)

Bias in AI

„Bias in AI“ is a popular topic these days, and usually this just means that algorithms cannot be shamed into not picking up certain pattern in the real world.

Half a year ago OpenAI published the exploits of a new Deep Learning language model called GTP-2, without publishing the larger versions of the model itself. This language model can create text that is much more coherent than was previously possible. It is trained on 40GB of articles, which makes it likely that it is severely biased in the same way as our media seems to be.

The reasoning for not publishing the model was that its capabilities make it too dangerous to put into the wrong hands. It might for example make fake news much more convincing or easy to produce. But, now, several independent replications later they started publishing larger models with the latest one having almost half as many parameters as their largest one which is as of yet still unpublished.

As this is about the biggest model that would fit into my RAM anyway, I tested GTP-2’s grasp of HBD. The prompt is my text, the samples are created by the model, which aims to coherently build on the prompt. I only excluded one or two samples that didn’t turn out coherent or grammatical. For an overview I only cite the very first sentence after my prompt:

Model prompt >>> The persistent IQ and achievement gaps between different ethnic groups are predominantly due to …

Sample1:
… ipsilateral technical dominance, or their tendency to share innovative problems and productive parts, rather than being the result of genetics. Top performers in the tribal dress component are among the most genetically gifted officials both socially and economically.

Sample 2:
… condemns from lower socio-economic backgrounds , likely reflecting environmental factors or Aryans mining wealth in the Middle East ( Hu et al., 2002 ).

Sample 3:
… erythrocytosis deficiency which may be partially compensated by factors including cosmetic surgery.

Sample 4:
… differences in transactional culture.

Sample 5:
… under- or over-selection with strong negative and positive selection processes, respectively.

Sample 6:
… differences in immigration policy patterns.

Sample 7:
… ersatz”personal”classes, not to ethnic differences in cultural relatedness, a group standpoint evolutionary economist].

Sample 8:
… vernacular, cultural and cognitive factors.

Sample 9:
… prevalent racism and the subsequent exacerbated IQ gap has a high cost.

Sample 10:
… genetically determined differences in IQ with no other neutral or intergroup effects.

Sample 11:
… bias against European-descended Black Americans

Sample 12:
… vernacular education system, not to individual differences, says Alan Kendry, professor of social policy at Victoria University in Sydney.

Sample 13:
… vernacular media and behavioural aberrations derived from biased educational models.

I am not sure what’s up with „vernacular“ but you have to be impressed by the variety of responses. These results are not nearly as biased as I assumed, though of course still heavily shifted towards environmental explanations. Out of these 13 samples at least three give a genetic reason. The „selection“ answer continues „On the other hand, other studies (for example Eurogenes 2013 and Mandarin 2007) show a genetic basis for larger and more ancillary effects on test scores at the end of high school.“ and the „vernacular, cultural and cognitive factors.“ continues with „Untreated dysfunctions of some genes not only may contribute to the variation observed between and across ethnic groups, but may position racial and ethnic groups at disadvantage relative to other ethnic groups; for example, attenuating the manifest motor skills of Jews“

Let’s finish this post with some gems that stood out among the continuations of the above responses:

There has been a larger increase in IQ among the general public by a range of thousands points on the standardised IQ test across India ( Singh and Sharma, 1980 ; Das, 2001 ). Meanwhile, socioeconomic status is negatively related to ‘intelligence levels among youth of all economic groups in the country’ ( Bhatnagar et al., 2007 ).

Yeah, thousands of points, sounds about right.

No ethnic group raises future generations in the spirit of Western society and values, and different part of the ethnic group must adjust to what it must learn from the outsider, so different ethnicities can explain themselves why they were colonized, what conditions existed for Dalits to support the government in their ancestral homes and how to impose values on all Hindu Dalits.

A glimpse into a Hindu nationalist’s mind.

If everything else is equal, the Asian (though improving) male population is asked to step up and do STEM/STEM education; the Latino female population is asked to head start an obstetrics practice.

If only these male Asians would step up and do STEM …

Equally, I would like to add credit to try to clarify what (other than Jungian/Marxist Othering) science education is meant to teach. It is morally wrong to pursue “biological determinism” and “one of many evolutionary processes” to “conform to one’s moral outlook” and then exaggerate through social engineering to “profit by social engineering” and “make money by social engineering” in the process, and that is what mentioned below. …(incoherent blathering)… Our philosophy is the same as George Orwell’s Gestapo; still gags anyone who loses to us (our ideas are so evil!)

GTP-2 simulating a Marxist, only with unusual self-knowledge.

Black Governance and Crime

In the US all kinds of statistics are meticulously recorded on the basis of race or ethnicity. This kind of data is a luxury not easily afforded in many European countries, where we have endless debates about whether some ethnic group or other might be more prone to violent crime. In the US it is well known that African-Americans are roughly three-fold overrepresented in violent crime. This means that in US cities, the rate of violent crime is mostly determined by the percentage of the black population.

So in the US, instead, the debate can, though equally fruitlessly, cover the topic of possible causes and what best to do about them. One of these causes, eagerly championed by Steve Sailer, is the so called Ferguson effect. In the aftermath of black-lives-matter protests, crime rates allegedly spike, because the police walks back on discriminating but effective measure like racial profiling in frisk-and-stop.

Together with the Jussie Smollet case, this lead me to wonder whether black governance had an effect on black crime. If your key voters would punish you for the most effective measures against black crime, maybe you wouldn’t be so eager to implement these. Or maybe for a black mayor crime in the black community is a bigger priority and insights into how to alleviate the problem are more common?

So I created a list of cities with their general and specific violent crime rates, as well as their black percentage [1] and cross-bred it with a list of black mayors [2]. Then I plotted black percentage against crime with every red dot a city with a black mayor and every blue dot a city without a black mayor. I am not sure how accurate and up to date these data is. And I am sure this analysis could be done in a much more principled way, but as a first look into the topic this gives already gives a clear tendency.

Violent crime – black percentage correlation:0.638, p<7.0e-06
Murder – black percentage correlation 0.757, p<9.9e-09
Rape – black percentage: 0.304, p< 0.054
Robbery – black percentage correlation 0.574, p<6.80e-05
Aggravated assault – black percentage correlation:0.542, p< 0.0002

The correlation of black percentage and rape is much less tight than the other correlations. Would be interesting whether this is real or a result of more unreported cases.

Overall we see that cities with black mayor have less violent crime than other cities with the same black percentage. This trend is observable for all types of crime. And though there is a lot to be desired in our methodology, it seems unlikely that a more thorough investigation would yield the opposite result.

[1] Crime rate by city
https://en.wikipedia.org/wiki/List_of_United_States_cities_by_crime_rate

[2] Black mayors
https://blackdemographics.com/culture/black-politics/black-mayors/

[3] Black percentage
https://en.wikipedia.org/wiki/List_of_U.S._cities_with_large_African-American_populations

GDP and low IQ immigration II

In the last blogpost we saw that the GDP-IQ relationship in multiethnic societies is probably roughly the weighted sum of the GDP predicted by the mean IQs of all ethnic groups. This indicates that low IQ immigration retards economic growth in a predictable fashion.

I don’t analyze this question to assign blame or even to make policy proposals, but simply to predict the future. It is a fact that in many Western countries the relatively high IQ native population is slowly being replaced by immigrants that on the whole don’t show the same cognitive performance (as measured for example by IQ-tests, PISA or other scholastic tests [1]).

In developed countries, economic growth generally isn’t very high. If the relative growth of the lower performing section of the population manages to significantly retard or even completely stall economic growth, we are in for a bad time.

In our first simulation we will compare the per capita economic output of a country, where the native population has a fertility rate of 1.4 and the missing kids are replaced by immigrants that show a 10 point IQ gap compared to the natives. This is roughly the situation of Germany and both in birth rate and IQ gap relatively extreme.

We use only the population between 20 and 65 for the calculation of GDP, the working age population. We also assume a simplified scenario, where suddenly the birthrate drops from replacement rate to 1.4 and the missing working age people are replaced by a homogenous immigrant group to hold the population exactly steady. Also all mothers are exactly 30 years old.

These artificial assumptions lead to a slightly bumpy ride. We see that the first twenty years nothing happens, because the missing kids aren’t working age yet. Then further thirty years down the road the decline speeds up, because the next generation of of mothers is now already coming from a low fertility generation. Then we hit the point where immigrants start leaving the workforce …

But the overall picture of a more detailed simulation wouldn’t be very different. Over 80 years the GDP drops to 66% of what it would be with a completely native population. Of course in reality this happens against the backdrop of economic growth stemming from productivity growth through innovation.

In developed countries the annual growth rate seems to hover around 2% [2]. This should already include immigration effects, but we can use it as a conservative estimate of possible future growth. In the second figure we see how the effect of low IQ immigrants making up an increasing share of the population plays out against a backdrop of 2% growth.

Fortunately, even these relatively extreme settings with low fertility and a big IQ gap do not seem to threaten to stall the economy completely. This makes sense because a 34% decline over 80 years corresponds to a shrinking of half a percent per year, which can still be set off by a normal rate of growth.

[1] IQ gap via PISA
https://akarlin.com/2012/05/berlin-gets-bad-news-from-pisa/
[2] Euro zone economic growth
https://tradingeconomics.com/euro-area/gdp-growth-annual