WORDSUM is a variable in the General Social Survey. It is a 10 word vocabulary test. A score of 10 is perfect. A score of 0 means you didn’t know any of the vocabulary words. WORDSUM has a correlation of 0.71 with general intelligence. In other words, variation of WORDSUM can explain 50% of the variation of general intelligence. To the left is a distribution of WORDSUM results from the 2000s. As you can see, a score of 7 is modal. In the treatment below I will label 0-4 “Dumb,” 5-7 “Not Dumb,” and 8-10 “Smart.” Who says I’m not charitable? You also probably know that general intelligence has some correlation with income and wealth. But to what extent? One way you can look at this is inspecting the SEI variable in the GSS, which combines both monetary and non-monetary status and achievement, and see how it relates to WORDSUM. The correlation is 0.38. It’s there, but not that strong.
To further explore the issue I want to focus on two GSS variables, WEALTH and INCOME. WEALTH was asked in 2006, and it has a lot of categories of interest. INCOME has been asked a since 1974, but unfortunately its highest category is $25,000 and more, so there’s not much information at the non-low end of the scale (at least in current dollar values).
Below you see WEALTH crossed with WORDSUM. I’ve presented columns and rows adding up to 100%. Then you see INCOME crossed with WORDSUM. I’ve just created two categories, low, and non-low (less than $25,000 and more). Additionally, since the sample sizes were large I constrained to those 50 years and older for INCOME.
Long time readers know well my fascination with quantitative history. In particular, cliometrics and cliodynamics. These are fields which attempt to measure and model human historical phenomena and processes. Cliometrics is a well established field, insofar as it is a subset of economic history. But cliodynamics is new on the scene. At the heart of cliodynamics is the quantitative ecologist Peter Turchin. I highly recommend his readable series of books, Historical Dynamics, War and Peace and War, and Secular Cycles. Also, see my reviews of the first two books.
With that, I am rather excited by the debut of Cliodynamics: The Journal of Theoretical and Mathematical History. Here’s the description of the brief of the field and the journal:
‘Cliodynamics’ is a transdisciplinary area of research integrating historical macrosociology, economic history/cliometrics, mathematical modeling of long-term social processes, and the construction and analysis of historical databases. Cliodynamics: The Journal of Theoretical and Mathematical History is an international peer-reviewed web-based/free-access journal that will publish original articles advancing the state of theoretical knowledge in this discipline. ‘Theory’ in the broadest sense includes general principles that explain the functioning and dynamics of historical societies and models, usually formulated as mathematical equations or computer algorithms. It also has empirical content that deals with discovering general empirical patterns, determining empirical adequacy of key assumptions made by models, and testing theoretical predictions with the data from actual historical societies. A mature, or ‘developed theory,’ thus, integrates models with data; the main goal of Cliodynamics is to facilitate progress towards such theory in history.
The first issue has an article by Sergey Gavrilets, David G. Anderson, and Peter Turchin. Gavrilets is a familiar face. I know him from his work in evolutionary theory. The paper itself is a readable and plausible model of how “medium scale” societies rise and fall through conflict and consolidation. Basically the stage of human society described in Laurence Keely’s seminal War Before Civilization; after agriculture, but before major literate state systems. Their basic method is to take a mathematical model with a tractable number of plausible parameters and run simulations which give one a sense of how it changes dependent variables of interest. Polities were modeled as being circumscribed hexagons; so they had six neighbors (instead of four which would be the case if they were squares). The area modeled had an “edge,” and polities can exist in a flat hierarchical structure, or eventually aggregate so they exhibit rank order. Additionally, polities varied by economic productivity. Finally, there was a temporal aspect in that there was a potential conflict per generation where the probability of success was conditional upon the strength of the polity. Being more powerful increase the probability of victory, but does not guarantee it. Flukes do happen. The full model can be found in the paper, which is open access. The math isn’t totally opaque, so I’m really not going to distill it down. You can swallow it whole. Rather, let’s look at the list of parameters and statistics:
A few people have inquired of the PNAS paper On sharing genes with friends. I avoided comment in part because I’m skeptical of the findings. So much behavior genomics just hasn’t panned out over the long term, and is probably susceptible to the issues which fuel the “decline effect”. Statistical significance is a random variable too. The fundamental issue which I want to emphasize is this: many behavioral traits are highly heritable, insofar as the correlation between relatives of trait value is in direct proportion to their genetic correlation. But, just because a trait is heritable does not mean that you can affix the variation to a specific set of genes. That is because the character of genetic architecture varies, and it may be that for many behavioral traits with some biological basis the causal variants which are responsible for the range in trait values are distributed across thousands of genes, and so are of very small effect.
Carl Zimmer relayed the depth of skepticism in the scientific community yesterday, and today Dr. Daniel MacArthur reviewed the paper. Here are the top line reactions:
Today I received an email from an academic with whom I am acquainted about how to get a successful blog going. Obviously the main necessary condition is actually to keep plugging away. Though that’s not sufficient. Jason Goldman has a post up, In the Wake of Science Online (#scio11): Supporting New Bloggers. I don’t believe in supporting new bloggers just because. They have to be good. But assuming that the goodness criterion is met, what next? Last spring when I was on vacation I noticed that Jason Goldman retweeted one of my posts on Jewish genetics. I knew Jason’s blog from ResearchBlogging. Additionally, he moved into the ScienceBlogs neighborhood just as I moved out. But the topicality of our weblogs are far enough apart that the retweet got my attention.
In science, like most things, one prefers simple over complex whenever possible. You keep adding variables until the explanatory juice starts hitting diminishing marginal returns. So cystic fibrosis is due to a mutation at one gene, and the disease expresses recessively at that locus. The reality is that one mutation accounts for ~65-70% of cystic fibrosis cases around the world, and there are nearly ~1,400 known mutations on the CFTR locus. How about skin color? Mutations on a dozen genes can probably explain ~90% of the variance in the trait value across the world between populations. In fact, one single mutation on one base pair can explain ~30-40% of the trait value difference between Europeans and Africans. This is a more complex story that cystic fibrosis; you have not just many mutations, but many mutations across many genes. But, the number of genes and mutations are manageable. You can keep track of most of them in your head (e.g., I can tell you that SLC24A5, SLC45A2, KITLG, and HERC2, can explain most of the trait value difference between Africans and Europeans without looking it up).
Now think about something like height. The only gene I can think of off the top of my head is HMGA2. With obesity I know FTO. The reason is that there’s a veritable alphabet soup of genes which pop out of the numerous studies focusing on these traits. But the reality is that it seems possible that there are many genes which harbor variants of small effect size which in totality account for the range of the trait value. Abstractly this isn’t really that much more complex than the models above. You can imagine it as a concrete instantiation of the central limit theorem. But in practice it does change things when you can’t focus on one gene, or a few genes, but have to understand that there exists a huge class of genetic causes which modulate the expression of the phenotype.
We’ve reached a stage where the mapping from genotype to phenotype is getting a bit on the baroque side. We have come to confront and wrestle with ‘genetic architecture.’ Here’s what Wikipedia says about this term:
The day has come! Dr. Daniel MacArthur has finally gotten the stamp of approval from Tobias MacArthur, and Genetic Future is over at Wired, with all their other great science blogs. Since it can sometimes be a bit difficult to figure out where these diamonds-in-the-rough are in the bramble that is the Wired website, I recommend everyone switch to the new new RSS pronto. Oh, and lest anyone wonder about Wired‘s beefing up of their predominantly white male lineup, Dr. MacArthur and I are distant paternal kin. I’m sure you can connect the dots on the appropriate syllogisms.
PLoS Biology has four items of great interest out today:
– Synthetic Associations Created by Rare Variants Do Not Explain Most GWAS Results
– Synthetic Associations Are Unlikely to Account for Many Common Disease Genome-Wide Association Signals
– The Importance of Synthetic Associations Will Only Be Resolved Empirically
– Common Disease: Are Causative Alleles Common or Rare?
These are a response to last year’s paper on synthetic associations from the Goldstein lab. Here’s a critique of that that paper. I plan on reviewing the first in the list above soon. #3 is a response to #1 and #2 from David Goldstein, while #4 is a summation more aimed at the general audience.
Dienekes did another run of his data with K = 64. He posted a huge plot with the two largest dimensions of variation. He also posted an accompanying spreadsheet with the coordinates of where the Dodecad samples were. So I found my own position pretty quickly. Before going to that, I thought I’d repost a comparison between myself, the HapMap Gujaratis, the North Kannadi sample, and the HGDP Uygurs. This is at K = 10 in ADMIXTURE from Dodecad.
OK, with that in mind, here’s the full MDS with the two largest components of genetic variation. I’ve added large labels. Also, click the image for a larger file so you can read the small labels.
Cell has an interesting piece, profiling four diets, Cell Culture: New Year’s Diets. I know many of the readers of this weblog take an interest in this area. In particular, many subscribe to the Paleo diet or are avid fans of Gary Taubes’ Good Calories, Bad Calories, as well as Art Devany’s ideas. Personally I think one issue which we need to acknowledge more are individual differences. The returns on the margin for a given diet may differ from person to person. The morbidity cost to someone with a family history of type 2 diabetes who has a weakness for dessert is likely much higher than someone without such a family history.
The Cell article gives a scientific overview of the diets in question, and then has pointers to the scientific literature.
This is the feed:
If your ancestry is from these nations:
Read on! If not, “for entertainment purposes only”….
 Then the king of Assyria came up throughout all the land, and went up to Samaria, and besieged it three years.
 In the ninth year of Hoshea the king of Assyria took Samaria, and carried Israel away into Assyria, and placed them in Halah and in Habor by the river of Gozan, and in the cities of the Medes.
 Therefore the LORD was very angry with Israel, and removed them out of his sight: there was none left but the tribe of Judah only.
Most Americans are aware of the term “Assyria,” if they are, through the Bible. The above quotation is of some interest because it alludes to the scattering of the ten northern tribes of Israel during their conquest and assimilation into the Neo-Assyrian Empire. Neo because the Assyrian polity, based around a cluster of cities in the upper Tigris valley in northern Mesopotamia, pre-dates what is described in the Hebrew Bible by nearly 1,000 years. During the first half of the first millennium before Christ they were arguably the most antique society with a coherent self-conception still flourishing aside from their Babylonian cousins to the south and the Egyptians (other groups like the Hittites who may have been rivals in antiquity had disappeared in the late Bronze Age). The period of the Neo-Assyrian Empire, in particular under Ashurbanipal, was arguably the apogee of the tradition of statecraft which matured during the long simmer of civilization after the invention of literacy and the end of the Bronze Age. The Neo-Assyrian Empire marked the transition from cuneiform to the alphabet, from chariots to cavalry. Assyria’s political evisceration by its vassals and enemies was inevitable, as a agricultural society on the Malthusian margin can squeeze only so much marginal product out of so many for so long. Once social and cultural capital is gone, there’s a “run on the bank,” so to speak.
But the Assyrians are still with us! Baghdad Raids on Alcohol Sellers Stir Fears:
Eight men carrying handguns and steel pipes raided a Christian nongovernmental organization here on Thursday night, grabbing computers, cellphones and documents, and threatening the people inside, according to members of the group.
“They came in and said, ‘You are criminals. This is not your country. Leave immediately,’ ” said Sharif Aso, a board member of the organization, the Ashurbanipal Cultural Association. “They said, ‘This is an Islamic state.’ ”
The intruders wore civilian clothes, said Mr. Aso and others at the organization, but their arrival was preceded by three police vehicles that blocked off the street. He said the men stole his ring and bashed him on the leg with a pistol.
Poking around Google Data Explorer I reacquainted myself with an interesting fact: though the teen birth rate in Bangladesh is greater than that in Pakistan, the total fertility rate is far lower. The disjunction has emerged over the last generation, as Bangladesh’s TFR has dropped much faster than Pakistan’s. To the left you see a scatter plot, which shows teen fertility rates (age 15-19) as a function of total fertility rates. I’ve labeled a few nations, and also added the color coding by region. It is notable that the nations above the trend line seem to be Latin American, while those below are disproportionately Middle Eastern. That means that Latin American nations have higher teen fertility in relation to their total fertility, while Middle Eastern nations have lower teen fertility in relation to their total fertility. Sweden actually has a rather high fertility rate in relation to its teen birth rate. The expectation is generated by world wide patterns, so I thought I’d look more closely at the original data sets from the The World Bank. All the data is from 2008. The teen birth rates are per 1,000 of teens in the age range, with TFR’s are per woman.
My contention is this: those nations with high overall fertility despite low teen fertility rates indicate an ideological or operational pro-natalist cultural stance. That means that mature adult women in marriages are presumably having many children. The high teen fertility rates in Bangladesh vis-a-vis Pakistan is probably simply due to lower aggregate development (Pakistan is still higher up on the HDI ranking, though the gap is closing).
Below are some charts. First, a plot with lines of best fit (as generated by R’s loess function). Then, absolute deviations from the line of best fit as a function of fertility. Also, percentage deviations from the line of best fit as a function of fertility. I provide the weighted trend line, but rely on the unweighted fit for the rest of the charts.
That seems to be what John Dvorak is saying, Why I Don’t Use Facebook:
Which begs the question as to why anyone would use Facebook when it is essentially AOL done right? The fastest growing group on Facebook are people in their 70’s. Oldsters are flocking to Facebook the way they once did with AOL. Facebook is a simple system for the masses that do not really care about technology and do not want to learn anything new except something easy like Facebook.
Whenever someone tells me to check out something on Facebook, I recall the heyday of AOL with its keywords. “Go to the Internet at www.blah.com or AOL keyword: blah.” This was a common comment on the nightly news or in magazines. The AOL keyword is replaced by the Facebook page name.
There is no reason for anyone with any chops online to be remotely involved with Facebook, except to peruse it for lost relatives. So, next time you log on, remember it’s really AOL with a different layout.
Welcome to the past.
In broad qualitative strokes this seems about right. I’ve been hearing the Facebook-is-AOL analogy for years, and there are obvious similarities. I do have a Facebook page, but I don’t use it for much. If I want to say something that’s not substantial enough for a blog post, off to twitter. If I want to throw a few thousand words at a few thousand people, well, you know where I’m going to go with that. And of course, there’s razib.com if someone wants to find/contact me.
About 20 years ago I lived for a year in a rural area where Amish were a common feature of country roads and farmers’ markets. My parents, being Muslims, would sometimes buy chickens from the local Amish and slaughter them according to halal. We had a relationship with a particular family. They were nice people, though I have to admit that their chickens were a bit tougher than I was used to. In many ways the Amish lived predictably parallel lives from the “English” (we referred to them as “Dutchees”), but they’d always pop up from the background in unexpected ways. Amish don’t seem to have a problem with modern medicine, so we’d run into them at the hospital sometimes. Whenever my father saw an Amish fruit or vegetable stand on a country road he’d pull over, because they’d often let us sample a bit before we purchased (we always purchased watermelons from the Amish for this very reason). It’s been a long time, so I haven’t thought about the Amish in much depth. Living on the West coast you don’t run into their kind very often (I don’t recall ever running into Amish on the West coast in fact). But it turns out that the number of Amish in the United States of America has more than doubled in the past 20 years. Their population went from 123,000 in 1991 to 249,000 in 2010. The fertility of the traditionalist Old Older Amish is 6.2. Here’s the Old Older Amish fertility rates in an international perspective:
Several people have inquired as to my opinion on the OKCupid post The Mathematics Of Beauty. I’ve blogged data from this dating website in the past, in particular, the differential race consciousness of women vs. men. But that material is a different class than the current post. As I have noted before, there is a robust result in the social science research over the past decade which suggests that women express & reveal more race consciousness than men when it comes dating & mating. The previous OKCupid analysis wasn’t ground-breaking, it simply added some wrinkles into a series of patterns which were replicated in the literature. The current results are different insofar as I haven’t followed the academic literature which relates to this in detail. This matters because unlike most of my peers I’ve done very little online dating (basically 2 weeks in the summer of 2002), and so can’t bring a personal familiarity with the topic to the discussion. To be sure, plenty of my friends have discussed their issues with online dating with me, so I’m not ignorant of the phenomenon. My male friends routinely complain how difficult it is to get the attention of women who are bombarded by messages from all directions. A female friend who is in her mid-30s chronologically, but physically resembles a women in her 20s, has complained how men clearly have automatic age filters set for searches which are working against her.
Let’s start at the beginning. To the left you see a scatter plot of # of messages received by women per month as a function of their rated attractiveness. They controlled for background variables (e.g., race). On the one hand, the results aren’t surprising. You see that more attractive women receive more messages. But on the other hand, the residual (noise) around the trend line is enormous, especially in the top half the distribution. I am personally rather surprised at the enormous variance of message # at the higher ratings. But here’s an important point: this is the mean rating of attractiveness. It turns out there’s a substantial variance around the means of attractiveness for any given mean value. There are two ways to look at this. It seems there is a general consensus about a mean of a distribution as to someone’s attractiveness level. In other words, you don’t have a preponderance of uniform distributions, suggesting that attractiveness is extremely plastic. This is in line with what evolutionary psychologists have found: people from “small scale” societies can ascertain who is, or isn’t, attractive in a set of photos of Europeans. But there’s another part of the story: differences in opinions about physical attractiveness of the same person from the vantage point of outsiders.
Does the chart above strike you as strange? What it shows is that the mean fitness of a population drops as you increase the rate of deleterious mutation (many more mutations are deleterious than favorable)…but at some point the fitness of the population bounces back, despite (or perhaps because of?) the deleterious mutations! This would seem, to me, an illustration of bizzaro-world evolution. Worse is better! More is less! Deleterious is favorable? By definition deleterious isn’t favorable, so one would have to back up and check one’s premises.
And yet this seems just what a new paper in PLoS ONE is reporting. Purging Deleterious Mutations under Self Fertilization: Paradoxical Recovery in Fitness with Increasing Mutation Rate in Caenorhabditis elegans:
Compensatory mutations can be more frequent under high mutation rates and may alleviate a portion of the fitness lost due to the accumulation of deleterious mutations through epistatic interactions with deleterious mutations. The prolonged maintenance of tightly linked compensatory and deleterious mutations facilitated by self-fertilization may be responsible for the fitness increase as linkage disequilibrium between the compensatory and deleterious mutations preserves their epistatic interaction.
Got that? OK, you probably need some background first….