Friday, April 11, 2014

Human Capital, Genetics and Behavior

See you in Chicago next week :-)
HCEO: Human Capital and Economic Opportunity Global Working Group

Conference on Genetics and Behavior

April 18, 2014 to April 19, 2014

This meeting will bring together researchers from a range of disciplines who have been exploring the role of genetic influences on socioeconomic outcomes. The approaches taken to incorporating genes into social science models differ widely. The first goal of the conference is to provide a forum in which alternative frameworks are discussed and critically evaluated. Second, we are hopeful that the meeting will trigger extended interactions and even future collaboration. Third, the meeting will help focus future genetics-related initiatives by the Human Capital and Economic Opportunity Global Working Group, which is pursuing the study of inequality and social mobility over the next several years.

PROGRAM

9:00 to 11:00
Genes and Socioeconomic Aggregates
Gregory Cochran University of Utah
Steven Durlauf University of Wisconsin–Madison
Henry Harpending University of Utah
Aldo Rustichini University of Minnesota
Enrico Spolaore Tufts University

11:30 to 1:30
Population-Based Studies
Sara Jaffee University of Pennsylvania
Matthew McGue University of Minnesota
Peter Molenaar
Jenae Neiderhiser

2:30 to 4:30
Genome-Wide Association Studies (GWAS)
Daniel Benjamin Cornell University
David Cesarini New York University
Dalton Conley New York University/NBER
Jason Fletcher University of Wisconsin–Madison
Philipp Koellinger University of Amsterdam

APRIL 19, 2014

9:00 to 11:00
Neuroscience
Paul Glimcher New York University
Jonathan King National Institute on Aging
Aldo Rustichini University of Minnesota

11:30 to 1:30
Intelligence
Stephen Hsu Michigan State University
Wendy Johnson University of Edinburgh
Rodrigo Pinto The University of Chicago

2:30 to 4:30
Role of Genes in Understanding Socioeconomic Status
Gabriella Conti University College London
Steven Durlauf University of Wisconsin–Madison
Felix Elwert University of Wisconsin–Madison
James Lee University of Minnesota
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10 comments:

ronthehedgehog said...

If only the institute for Historical Review were holding a conference in Skokie y'all could get together. I was surprised to find that Brouchard understood behavioral genetics much better than any of the attendees I've read. http://web.missouri.edu/~segerti/1000H/Bouchard.pdf. But he still seems beholden to the phenotype random variable = sum of random variables approximation. I wonder if Cochran has ever heard of "reaction norms".

I came up with a still facile but better way to think about heritability/correlation and variance. I'm too lazy and stupid to do the integral analytically.

What follows is the probability, given h^2, that one whose phenotype is +2 SDs above another's will actually have a lower genotype (first column) or that the other might have had a higher phenotype given the same range of environments.

h^2 G G+E
0.35 14.97% 29.86%
0.4 12.40% 26.85%
0.45 10.04% 23.80%
0.5 7.86% 20.71%
0.55 5.90% 17.58%
0.6 4.16% 14.44%
0.65 2.70% 11.32%
0.7 1.54% 8.28%
0.75 0.71% 5.44%
0.8 0.23% 2.97%
0.85 0.04% 1.12%
0.9 0.00% 0.17%

6:54 PM
a last a loved a long the said...

What does that map mean? Weren't S Korea and China just added recently?

Imagine the same map 4000 years ago. The whole of Europe, even Greece, would be dark...a dark continent like Africa.

Or just go back 2000 years. Today the descendants of greasy illiterate human sacrifice practicing Germans lecture the Greeks, think the Greeks inferior.

The death rattle of hbd will be, "reaction norms..."

7:58 PM
Endre Bakken Stovner said...

Will there be any slides/videos or at least links to related papers by the presenters?

4:57 AM
ronthehedgehog said...

If only the Institute for Historical Review were holding a conference
in Skokie y'all could get together. I was surprised to find that
Bouchard understood behavioral genetics much better than any of the
attendees I've read. http://web.missouri.edu/~seger....
But he still seems beholden to the phenotype random variable = sum of
random variables approximation. I wonder if Cochran has ever heard of
"reaction norms".

A still facile but better way to think about
heritability/correlation and variance...I'm too stupid and lazy to do
the integral analytically (if that's possible)...so the results are
copied from a spreadsheet.

What follows is the probability, given
h^2 (first column), that one whose phenotype is +2 SDs above another's
will actually have a lower genotype (G, second column) or that that
"another" might have had a higher phenotype (P=G+E, third column) given
the same range of environments and the ridiculous P=G+E assumption.

h^2 G G+E
0.35 14.97% 29.86%
0.4 12.40% 26.85%
0.45 10.04% 23.80%
0.5 7.86% 20.71%
0.55 5.90% 17.58%
0.6 4.16% 14.44%
0.65 2.70% 11.32%
0.7 1.54% 8.28%
0.75 0.71% 5.44%
0.8 0.23% 2.97%
0.85 0.04% 1.12%
0.9 0.00% 0.17%

and for 1 SD

h^2 G G+E
0.35 30.19% 39.58%
0.4 28.19% 37.88%
0.45 26.12% 36.08%
0.5 23.97% 34.15%
0.55 21.72% 32.07%
0.6 19.32% 29.79%
0.65 16.76% 27.27%
0.7 14.00% 24.41%
0.75 11.03% 21.13%
0.8 7.86% 17.29%
0.85 4.62% 12.69%
0.9 1.69% 7.21%

6:27 AM
Butch said...

I hope this group has enough influence to get the academic world as well as the political world to start realising how they can actually help the poor. If I could just show Xi Jingping your lecture on this topic... I am convinced I could make him persue the greatest economic reform in human history since now.


I hope there is a video of this confrence online, it would be greatly appreciated.

2:09 PM
a last a loved a long the said...

The sample correlation is sufficiently unbiased that a weighted sum of Bouchard's four sample correlations is a good estimate of the population correlation given the four groups of MZAs are drawn from the same population.This estimate is .70, with 95% interval (.59, .78).

Does Bouchard give a mean absolute difference though? This can be used to come up with an estimate of the heritability and may obviate restriction of range. A correlation/heritability of .7 would correspond to an expected absolute difference of 9.3 points I calculate.

1:13 AM
stevesailer said...

"Steven Durlauf University of Wisconsin–Madison"



I used to debate Durlauf in high school in 1973-75. He struck me as the smartest kid in speech and debate in the greater L.A. area.

12:36 AM
ronthehedgehog said...

Right. No need for a spreadsheet. The expectation of the absolute difference between X + e1 and X + e2, given a correlation of rho, is sqrt(1-rho)E(abs(e1-e2)) = sqrt(1 - rho)*2/sqrt(pi).

So Jensen's 6.6 is the md for rho = .8479...

2:10 AM
a last a loved a long the said...

WAIS-R protocols from 2 vocational counseling clients (aged 34 and 41 yrs) were scored by 19 psychologists and 20 graduate students. Regardless of scorer's experience level, mechanical scoring error produced summary scores varying by as much as 4–28 IQ points. For both protocols and both scoring groups, scoring agreement with the actual Full Scale IQ scores ranged from 32 to 35%. Over 77% of the scores were within 1 standard error of measurement (SEM) of the true scores. It is urged that IQs be reported in conjunction with a precision range based on the SEM of the test.

So many scorers are less reliable than one? Yet the test makers claim reliability of .97. Should've asked the twins what their SATs. It looks like the psychologist administered iq test is a racket.

7:49 AM
ronthehedgehog said...

It is odd that in a post on the misinterpretation, misleading interpretation of correlation and variance, Steve should confuse the conditional distribution's SD with the error's SD in a test-retest regression, and further confuse the SD with the expected test-retest difference.

Perhaps he forgot that the expectation of the folded normal distribution, folded at 0, is sqrt(2/pi), whereas the expected absolute difference between two independent N(0,1) variables is 2/sqrt(pi). And perhaps that the conditional distribution of the retest score isn't centered at the test score.

sqrt(2/pi) = .798, but
2/sqrt(pi) = 1.128

If a mathlete, who identifies mathematical ability with general intelligence, can make such an error, there's no hope for the rest of us.

11:31 PM

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