Random Thoughts – Discrimination

I
The investigation into a lot of societal questions comes down to base rates. If a black guy is called a fucking n***r, in many cases this undoubtedly comes down to racism. If he and other black persons are often insulted in such fashion one can probably make a reasonable case for a racist society.

On the other hand I was called a fucking bastard just last week for no reason. Would that person have availed himself to a racist insult if I’d been a darker shade of pale? Quite possibly. Insults are meant to hurt and racism fits that bill.

So a better measure for the prevalence of racism in a society would probably be whether minorities are more often insulted than people of the majority all else equal and not so much how those insults are couched. People who hate everybody equally are not racist.

II
I wonder whether displays of homophobia, especially among adolescents, aren’t often better explained by status games than, well, actual homophobia. If gays are lower status, implying somebody is gay, or denying to be gay is not motivated by homophobia. It is merely a try to lower somebodies status or to defend against such an attack.

Of course, one could say that gays being lower status is due to homophobia. But that is not a foregone conclusion. There are systematic trait differences between gays and heteros, so gays might be lower status because they are more feminine. Or they are lower status because they miss out on one key aspect of intra-male competition. In that case a teenager calling somebody gay, is just saying „you don’t compete for females!“. And the forceful denial is just „but I do, I do“ bangs chest.

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The most ethnic names

Because counting names is one of my modus operandi, today we are going to take a quick look at the names that are really specific to each ethnic group in the US according to the US census. To that end we order the names by percentage of the population that belongs to the given ethnic group and bears that name. We restrict ourselves to names borne by at least 1000 people.

For whites these are

99.65 NEWSWANGER
99.46 BORNTREGER
99.4 WAGLER
99.15 BORNTRAGER
99.14 MEISELS
99.1 BEECHY
99.09 BORKHOLDER
99.03 SHEVCHENKO
99.0 STOLTZFUS
98.99 SWARTZENTRUBER
98.87 NOLT
98.86 HERSCHBERGER
98.81 ESH
98.71 KUSHNIR
98.63 HODZIC
98.59 IVANOVA
98.58 STASKO
98.54 DWORAK
98.54 BALLWEG
98.52 ROHRBAUGH
98.52 PUSKAR
98.52 KOVALENKO
98.44 LENGACHER
98.39 BARHORST
98.38 HUETHER
98.37 REINEKE
98.34 PETERSHEIM
98.3 WENGERD
98.29 FLATEN
98.25 GEERS
98.23 HOCHSTETLER
98.21 SARGSYAN
98.21 HISSONG
98.2 GUREVICH
98.2 BERKEBILE
98.19 SCHMUCKER
98.19 BYLER
98.13 KATS
98.12 SOBOLEWSKI
98.11 KACZYNSKI
98.11 CHOINIERE
98.09 GOODLING
98.08 HERSHBERGER
98.08 BEACHY
98.04 IVANOV

Germans, Dutch, Eastern Europeans of different couleur including Ashkenazim. Latecomers that didn’t have the opportunity to gift their names to the African American population.

For African Americans the top names are:

97.75 KOROMA
97.7 ACHEAMPONG
97.46 AGYEMANG
97.37 WARSAME
97.21 MEKONNEN
97.06 ANYANWU
96.78 ALTIDOR
96.75 BOATENG
96.69 FRIMPONG
96.63 OSEI
96.62 BALOGUN
96.6 OKAFOR
96.59 NJOKU
96.56 NWOSU
96.5 TESFAYE
96.5 OPOKU
96.49 YEBOAH
96.46 JALLOH
96.4 ASSEFA
96.2 ADJEI

Same pattern applies. These seem to be genuinely African names. Maybe some latecomers also. If we increase the cutoff to 10.000 people bearing the name, we get 90.49 SMALLS, 87.53 WASHINGTON, 86.74 PIERRE, 82.86 MUHAMMAD, 80.85 HAIRSTON, 80.4 RUFFIN. All the African names are gone and the names become very unspecific.

98.82 VOONG
98.63 ZHEN
98.38 KUANG
98.26 CHUONG
98.25 XU
98.23 ZHU
98.21 QIU
98.2 ZHOU
98.19 XIE
98.11 ZHAO
98.07 XIONG
98.06 ZHANG
98.05 QIAN
98.02 ZHUANG

Boring. Yes, East Asian Names are common and really specific. It takes a while until an 97.54 BALASUBRAMANIAN comes along.

98.84 LOPEZPEREZ
98.8 GARCIARAMIREZ
98.75 CARACHURE
98.72 GARCIAMARTINEZ
98.64 EQUIHUA
98.63 VILLAFAN
98.61 HERNANDEZLOPEZ
98.53 GONZALEZLOPEZ
98.5 PEREZHERNANDEZ
98.44 RAMIREZLOPEZ
98.39 PEREZPEREZ
98.38 SANCHEZGARCIA
98.36 GARCIAPEREZ
98.28 MARTINEZLOPEZ
98.26 LUCATERO
98.25 GARCIAHERNANDE
98.23 OREGEL
98.22 SOLACHE
98.22 ALAVEZ
98.19 CUAUTLE

The hispanic names are dominated by the Spanish style double name. Makes sense that these are recent immigrants still following that naming convention. If we increase the cutoff to at least 10.000 people bearing the name, these double names completely vanish.

95.65 BEGAYE
95.0 TSOSIE
94.56 YAZZIE
94.3 BENALLY
93.84 BEGAY
92.13 NEZ
76.52 HARJO
72.9 LOCKLEAR
72.55 BAHE
71.85 POITRA
68.37 JIM
67.77 AZURE
63.82 SHORTY
63.68 OXENDINE
57.85 CHARLIE
57.51 BELGARDE
56.18 CHARLEY
52.6 TSO
51.4 CHEE

And finally we have a really short list of native American names. There just aren’t all that many Natives around and most of those are mixed in some way or form, which means that their names have likely bled into the white population.

These investigation shows the difficulty of assessing ethnicity by name: Only recent immigrants still bear really distinguishing names.

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.