Greek influence on Indian astronomy

Greek influence lies at the heart of Indian astronomy (and, unfortunately, astrology).

[By the way, anyone interested in the interaction between Greek and Indian thought, needs to read ‘The Shape of Ancient Thought‘, by Thomas McEvilly.]

In the words of the 6th century Indian astronomer and mathematician Varahamihira, “The Greeks, though impure, must be honored since they were trained in sciences and therein, excelled others…..” He is echoed by the Garga Samhita, which says: “The Yavanas are barbarians, yet the science of astronomy originated with them and for this they must be reverenced like gods.”

The influence began with the Alexandrian conquest of North Western influence, but continued throughout the Greco-Roman period, as several astronomical texts are known to have been translated into Sanskrit in the early centuries of our era.

The influence is apparent by looking at the founders of Indian astronomy (passages taken from Encyclopedia of Ancient Natural Scientists: The Greek Tradition and its Many Heirs):

Yavanesvara (149/150 CE)

“Anonymous author in 149/150 CE of a Sanskrit prose translation of an unidentified Greek (probably Alexandrian) text on horoscopy, a translation known only from the surviving Sanskrit verse version Yavanajataka composed by Sphujidhvaja in 269/270. The title Yavanesvara then meant literally “lord of the Greeks,” evidently a high position among the Greek residents of western India under the western Ksatrapa rulers in the Saka or Skuthian dominion of the area… His text as known through the Yavanajataka became the chief inspiration for Indian horoscopic astrology”

Sphujidhvaja (269/270 CE)

“Composed in 269/270 CE a Sanskrit verse adaptation, entitled Yavanajataka (YJ) or Greek Horoscopy, of a prose translation from Greek by an anonymous Yavanesvara (“lord of the Greeks”)… clearly reveals its Greek origin, including many transliterated Greek technical terms.”

Minaraja (ca 300 – 325 CE)

“Minaraja was a yavanadhiraja, i.e., person of authority in the settlements of Greeks under the western Ksatrapas in what is now Gujarat and Rajasthan in western India. He wrote a long astrological compendium, the Vr.ddhayavanajataka, covering every subject of astrology, in 71 chapters. The work is based on Sphujidhvaja’s Yavanajataka and a lost work of Satya..”

[text] Paitamahasiddhanta (ca 425 CE?)

“The extant form of the Paitamahasiddhanta still contains many of the basic features of classical Indian astronomy that were apparently derived from Hellenistic spherical astronomy models. These include large-integer period relations used to calculate mean celestial positions, planetary epicycles and equations for correcting mean positions on the assumption of circular orbits, and orbital sizes and geocentric distances. Their details reflect a rather chaotic mix of (among other things) Babylonian and Aristotelian notions invoked by various early Hellenistic theories that fell into oblivion after Ptolemy. Indian astronomers combined these concepts with other parameters and techniques in their astronomical tradition to produce the cosmological and computational models that became standard in siddhantas… appears to be the inspiration for much of the classical siddhanta tradition in Indian mathematical astronomy. The siddhanta is a standard treatise format that explains universal computations for all significant astronomical phenomena… The core siddhantas of the two earliest major schools or paksas of Indian astronomy (upon which the later schools are based) – namely, the Aryabhatiya of Aryabhata(ca 500 CE) in the Aryapaks.a and the Brahmasphutasiddhanta of Brahmagupta (628 CE) in the Brahmapaksa – both claim to follow a treatise of Brahma. Similarities in content strongly indicate this Paitamahasiddhanta as the treatise referred to in both cases. It is considered to be the founding text of the Brahmapaksa, although its original version has long been lost.”

Aryabhata (circa 500 AD)

“The planetary model used by Aryabhata is derived from a pre-Ptolemaic Greek model, which sought to preserve the Aristotelian principle of concentricity. The mean planet moves in a circle around the Earth, and centered around the mean planet are one or two epicycles, depending on whether the planet is one of the two luminaries or a star-planet. Pingree (DSB 15.590) believes that the mean motions of the planets in the Aryabhatıya, apparently unrelated to those of the Brahmapaksa, were derived from a Greek table of mean longitudes corresponding to noon on 21 March 499 CE.”

Varahamira (circa 550 AD)

“A descendant of Zoroastrian immigrants from Iran to India and a resident of the area near Ujjain, and a prolific writer, whose works cover all aspects of traditional Indian astrology and astronomy. His Pañcasiddhantika is a summary of five astronomical works current at his time, but now lost: the Paitamahasiddhanta, which expounds astronomy influenced by Mesopotamia… the Vasisthasiddhanta, the Paulisasiddhanta, the Romakasiddhanta and the Suryasiddhanta, which all expound Indian versions of Greco-Babylonian astronomy. The Pañcasiddhantika is an important work both in shedding light on the Indian astronomical tradition prior to 500 CE, and in recording pre-Ptolemaic Greek astronomy from which the Indian tradition borrowed. Varahamihira authored three works on divination… On genethlialogy, Varahamihira authored two works… both based on the Indian adaptation of Greek material in the works of Sphujidhvaja and others.”

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Gender and attitudes towards immigration

There’s a huge Ipsos Reid survey (2011) on the attitudes towards immigration of almost 18,000 people in 23 countries. Though the variation between countries in how they feel about immigration is large, the difference between the attitudes of men and women is very small, with women tending to have slightly less positive perceptions.

First, here’s an image that gives a sense of the variability between countries:

The following two tables represent the global average in attitudes towards immigration (in the second table, the percentages represent agreement with the statement):

Women are slightly less likely to think immigration has had a positive impact, slightly more likely to think it’s had a negative impact, and slightly more likely to say there are too many immigrants.

The pattern varies by nation. Let’s compare 4 western nations: U.S., Australia, Sweden & Belgium. First their general impressions on immigration:

In the U.S. and Australia, men have a more ‘very/fairly positive’ view of immigration than do women by a large margin. In Australia that represents women having a more negative view than men, but in the U.S. it represents a larger chunk of women being neutral. In Belgium, almost no-one has a positive view, but there women are even less positive and more negative then men. In Sweden, men are more neutral than women, who are over-represented in both positive & negative feelings (but moreso negative).

Then we have their answers to a series of questions. In the U.S., despite men seeming to have more positive feelings about immigration, they tend to answer the questions in ways which would suggest more antipathy. In all three of the other nations, women expressed less immigrant-friendly views in line with previously expressed more negative assessment of the impact of immigration (most drastic: 53% of Swedish women think there are too many immigrants vs. 39% of men). The percentage, again, represents those who agreed with the statement either ‘strongly’ or ‘tend to agree’:

In any case, in general there is not too much difference between genders in attitudes towards immigration. The apparently slightly more favourable feelings of one gender in one country may be reversed in another.

There do appear to some differences in specifics though: women are less inclined to give preference to more skilled immigrants (7% less globally), and are less likely to believe immigration helps the economy (6% less globally). Another study (pdf) of European nations found a similar trend of women being less supportive of immigration from wealthy countries and more supportive of immigration from poorer countries. However the researchers note that the differences were only “flirting” with significance. The differences, such as there are, are in line with previous studies that suggest women are more protectionist.

Other studies I have seen suggest similar variability in support. One study in Canada found more support among men than women (pdf) for immigration – 63.3% to 53.7%. In the U.S., while the above study finds women less hostile towards immigration, a 2003 Gallup poll found greater skepticism among women.

Genetic predisposition for supporting slavery

The Daily Mail and The Telegraph have been running stories about the slave-owning past of Richard Dawkin’s ancestors. The family tree is to the right.

From the Daily Mail: “Equality groups are now calling on him to apologise for his family’s past.”

But I especially like this recounting by Dawkins of a telephone call from the journalist who wrote the story:

“I’d scarcely had time to re-open my lecture notes when he rang back: ‘Darwinian natural selection has a lot to do with genes, do you agree?’ Of course I agreed. ‘Well, some people might suggest that you could have inherited a gene for supporting slavery from Henry Dawkins.'”

Distribution of light hair and eyes in Europe

The first two maps have been taken from eupedia, which has colourized the maps contained in a paper by Peter Frost (2006), which in Frost’s words, were “reproduced from an anthropology textbook (Beals & Hoijer 1965, pp. 213-214). Beals and Hoijer, in turn, cite a textbook by another anthropologist, Frederick Hulse (1963: p. 328). Unfortunately, Hulse does not indicate the provenance of his data. I suspect he was using data from military recruits, with a lot of interpolation. Or perhaps he was using even earlier maps.”

Next, we have a map from Carleton Coon (‘The Races of Europe’), and attributed to ‘Elmer Rising’ (1939):

This map produced by Bertil Lundman (1965):

Here is a map created with 23andme data on a simple scale of more blue eyes to more brown:

From the paper “DNA-based eye colour prediction across Europe with the IrisPlex system.” These samples are drawn from specific regions, so they don’t necessarily represent the whole country. For example, the northern Italy sample has higher proportions of blue eyes than it would if it included southern Italy. The sample from Northern Ireland has higher proportion of blue eyes than I would expect based on other measurements of Ireland and Britain.

Finally, some extra data (locally collected, not national):

Two studies of different schools in Sweden found 79% and 79.6% blue eyes (“Frequency and Distribution Pattern of Melanocytic Naevi inSwedish 8–9-year-old Children” & “Prevalence of common and dysplastic naevi in a Swedish population”)

And another of a similar type in Estonia found 82% in Estonia (“Frequency and distribution pattern of melanocytic naevi in Estonian children and the influence of atopic dermatitis”)

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Compare the above maps to the following genetic map of the averaged frequencies of 3 genes that are involved in light hair and eyes:

Update:

From a 2013 Slovenian study with 105 subjects (Prediction of eye color in the Slovenian population using the IrisPlex SNPs), 44.7% had blue eyes, 29.6% brown eyes, and 25.7% intermediate colours.

A world map of predicted eye colour based on genetic variants, from IrisPlex: A sensitive DNA tool for accurate prediction of blue and brown eye colour in the absence of ancestry information (2011)

2011 world eye map predictor colour

A world map of predicted hair colour based on genetic variants, from The HIrisPlex system for simultaneous prediction of hair and eye colour from DNA (2013)

2013 prediction of hair colour

Distribution of eye colours, based on phenotypes (pie chart shows size of sample), from Further development of forensic eye color predictive tests (2013)

ruiz et al 2013 - actual phenotype - pie chart proportional to size of sample

Phenotypes of sample in Spain, from Gender is a major factor explaining discrepancies in eye colour prediction based on HERC2/OCA2 genotype and the IrisPlex model (2013). Striped grey is blue eyes, light grey is intermediate, and dark grey is brown-eyes.

2013 spain map

Update II:

Data from the Blue Eyes Project run by ScotslandsDNA, map found on the Daily Mail:

article-2738224-20ECE9AF00000578-656_634x632

 

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Of course, light hair and eyes exist well outside of Europe as well.

See also my post on how the distribution of eye and hair colour is not evenly distributed between men and women.

Social Mobility

There’s been talk of a new paper by Gregory Clark measuring social mobility in England over 200 years by using rare surnames (“Are there Ruling Classes? Surnames and Social Mobility in England, 1800-2011“). Here is a graph from the paper:

The abstract:

Using rare surnames we track the socio-economic status of descendants of a sample of English rich and poor in 1800, until 2011. We measure social status through wealth, education, occupation, and age at death. Our method allows unbiased estimates of mobility rates. Paradoxically, we find two things. Mobility rates are lower than conventionally estimated. There is considerable persistence of status, even after 200 years. But there is convergence with each generation. The 1800 underclass has already attained mediocrity. And the 1800 upper class will eventually dissolve into the mass of society, though perhaps not for another 300 years, or longer.

Sex Differences in Intelligence

There have been well known claims about sex differences in intelligences: men have IQs higher by several points in adulthood, that men have greater variance in IQ (more geniuses, but also more stupid men), better visuo-spatial skills, etc.

More recent studies have shown that there is no difference in general cognitive ability (g) between men women, and the apparent emergence of a gap during the  teenage years in some studies was a result of study designs and samples, not real population differences (see Deary’s 2012 ‘Intelligence’ review).

The question of greater male variance in IQ is interesting, because, within populations that variance has been quite consistent. From a 2012 global study on math achievement:

The VR [variance ratio] measured for any given nation was quite reproducible, that is, it rarely differed by more than 20 percent from one test administration year to the next, among students in different grades, or between the PISA and TIMSS; typically, it differed by at most 10 percent…

However, while it is consistent within nations, it is very different across nations:

White bars = Muslim countries; Grey bars = non-Muslim countries

Here’s a list of countries and their variance ratios (boy’s variance / girl’s variance):

From this they conclude:

… that both variance and VR in mathematics performance vary greatly among countries. Confirming our earlier finding ([21]), we also conclude that VR is reproducibly essentially unity for some countries. These findings are inconsistent with the greater male variability hypothesis

They also point out the issue of the influence of sampling in causing differences in variance, similarly to how sampling issues seems to have been the cause of the erroneous findings of men having higher IQs – at least in explaining certain countries, such as these:

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On another note, in Iran apparently 70% of science and engineering students are women (source)

Status and Interracial Marriages

PEW has a new study (pdf) out on the characteristics of interracial marriages in the U.S.

As you can see, not only is there a clear racial hierarchy in terms of education and earnings, but the interracial marriages have a similar hierarchy. Interesting findings: while asian-asian marriages have higher levels of both spouses having college education than white-asian marriages, white-asian marriages earn more; also, white male/black female marriages make slightly more than the white-white average, but white female/black male, make significantly less

Here is graph showing that those whites marrying asians are significantly better educated – whereas, among white women, there is a trend for those marrying blacks to be less educated:

Among blacks and hispanics, both men and women marrying whites tended to be better educated than those who married within their race, but by varying amounts (black women were 7% more likely to have a college degree, but black men only 2%; hispanic women were 20% more likely to have a college degree, and hispanic men, 12%). For asians, there was very little difference in education whether they married whites or asians.

Final note: given the prevalence of cohabitation, and declining marriage rates, this data is probably somewhat misleading. I already went over the differences in rates of interracial cohabitation vs marriage. If those relationships were counted, it would probably widen the gap found here. For example, asian-male/white-female marriages were more likely than black-male/white-female marriages, given their respective proportions of the population. However, that was strongly reversed in cohabitation rates. Since marriage vs. cohabitation is partly a class issue, this fits into the data here, and would probably make it stronger.