Rationality Quotes September 2011

Everyone doing nothing in a collective action problem is a Nash equilibrium, I believe.

Most of the relevant ideologies in question are ideologies that try to avoid this problem in economic contexts.

As I see it, The Truman Show is, at its core, a Gnostic parable similar to The Matrix, but better executed.

Your essay fails to account for the deep philosophical metaphors of guns, leather, gratuitous exaggerated action and nerds doing kung fu because of their non-comformist magic.

Yep. Respected and admired at a distance, certainly. But a lot of people who knew him personally tend to describe him as a manipulative jerk.

Fictional polemical evidence is not an argument; see my reply to private_messaging.

I can't find a citation, but the rationale I've heard is to make it easier to learn how to use a Macintosh (or a Lisa) by watching someone else use one.

And let's not forget how many millions of patents there are; I don't think there are that many millions of utopias, even if we let them differ as little as patents can differ.

You may be right here. Utopias are usually also quite uninnovative. "All people will be brothers and sisters with enough to eat and Bible (or something else stupid) reading in a community house every night".

Variations are not that great.

Can you invent a utopia? A utopia is an incoherent concept about a society that contains too many internal contradictions or impracticalities to ever exist. Thus, it cannot be invented any more than a perpetual motion machine can be.

If you do consider utopias inventable, what's the difference between "inventing a new utopia" and "having a new preference"? You want X; you dream of a world where you get X, inventing Utopia X.

I feel obliged to point out that Socialdemocracy is working quite well in Europe and elsewhere and we owe it, among other stuff, free universal health care and paid vacations.

It's not fair to say we 'owe' Socialdemocracy for free universal health care and paid vacations, because they aren't so much effects of the system as they are fundamental tenets of the system. It's much like saying we owe FreeMarketCapitalism for free markets - without these things we wouldn't recognize it as socialism. Rather, the question is whether the marginal gain in things like quality of living are worth the marginal losses in things like autonomy. Universal health care is not an end in itself.

I feel obliged to point out that Socialdemocracy is working quite well in Europe and elsewhere and we owe it, among other stuff, free universal health care and paid vacations.

Comfortable, well maintained social democracies where the result of a very peculiar set of circumstances and forces which seem very unlikely to return to Europe in the foreseeable future.

I think socialism must fail because humans once freed from material want will compete for status. Status inequality will activate much the same sentiments as material inequality did. To level status one needs to embark on a massive value engineering campaign. These have so far always created alternative status inequalities, thus creating internal contradictions which combined with increasing material costs eventually bring the dissolution of the system and a partial undoing of the engineering efforts.

If technology advances to the point where such massive social engineering becomes practical and is indeed used for such a purpose on the whim of experts in academia/a democratic consensus/revolutionary vanguard... the implications are simply horrifying.

That's helpful; thanks.

"Do you have to be smart to be rich? The impact of IQ on wealth, income and financial distress", Zagorsky 2007:

How important is intelligence to financial success? Using the NLSY79, which tracks a large group of young U.S. baby boomers, this research shows that each point increase in IQ test scores raises income by between $234 and $616 per year after holding a variety of factors constant. Regression results suggest no statistically distinguishable relationship between IQ scores and wealth. Financial distress, such as problems paying bills, going bankrupt or reaching credit card limits, is related to IQ scores not linearly but instead in a quadratic relationship. This means higher IQ scores sometimes increase the probability of being in financial difficulty.

One could also phrase this as: "if we control for factors which we know to because by intelligence, such as highest level of education, then mirabile dictu! intelligence no longer increases income or wealth very much!"; or, "regressions are hard, let's go shopping."

Apropos of http://lemire.me/blog/archives/2012/07/18/why-we-make-up-jobs-out-of-thin-air/

In the XXIst century within wealthy countries, people work hard primarily to gain social status. We often make the mistake of tying up wealth with social status, but most of the wealthy people we admire are also consumed by their great jobs. Celine Dion is very wealthy, yet she would still give one show every single day, including week-ends. I think most professors would feel exploited if they had to lecture every single day. Bill Gates is very wealthy and universally admired, however, as we may expect, he worked nights and week-ends as chairman of Microsoft. Every year he would read 100 papers from Microsoft employees about the state of the company.

...For many, wealth is merely a stepping stone to intense work. This may explain why people with higher IQs are not wealthier (Zagorsky, 2008): high IQ people may have an easier time getting rewarding work so they need less wealth....I used to openly worry that robots would steal our jobs and leave most of us in poverty. I have now concluded that I was underestimating the pull of prestige among human beings. We will make up jobs out of thin air if we need to.

Intelligence: A Unifying Construct for the Social Sciences, Lynn & Vanhanen 2012 (excerpts)

"IQ in the Ramsey Model: A Naïve Calibration", Jones 2006:

I show that in a conventional Ramsey model, between one-fourth and one-half of the global income distribution can be explained by a single factor: The effect of large, persistent differences in national average IQ on the private marginal product of labor. Thus, differences in national average IQ may be a driving force behind global income inequality. These persistent differences in cognitive ability - which are well-supported in the psychology literature - are likely to be somewhat malleable through better health care, better education, and especially better nutrition in the world’s poorest countries. A simple calibration exercise in the spirit of Bils and Klenow (2000) and Castro (2005) is conducted. I show that an IQ-augmented Ramsey model can explain more than half of the empirical relationship between national average IQ and GDP per worker. I provide evidence that little of the IQ-productivity relationship is likely to be due to reverse causality.

One question of interest is whether the IQ-productivity relationship has strengthened or weakened over the past few decades. Shocks such as the Great Depression and the Second World War were likely to move nations away from their steady-state paths. Further, many countries have embraced market economies in recent decades, a policy change which is likely to have removed non-IQ-related barriers to riches.11 Accordingly, one would expect the IQ-productivity relationship to have strengthened over the decades.

As Table 2 shows, I indeed found this to be the case. I used LV’s IQ data along with Penn World Table data for each decade from 1960 through 1990 (1950 only had 38 relevant observations, and so is omitted). As before, equation (3) was used to estimate the IQ-productivity relationship, while the IQ-elasticity of wages is assumed to equal 1 for simplicity. Both the unconditional R2 and the fraction of the variance explained by the IQ-wage relationship increase steadily across the decades. This is true regardless of the capital share parameter in question. Further, the log-slope of the IQ-productivity relationship has also increased.

• 11: Lynn and Vanhanen (2002) hypothesize that national average IQ and market institutions are the two crucial determinants of GDP per capita. They provide some bivariate regressions supporting this hypothesis; they show that both variables together explain much more—about 75% of the variance in the level of GDP per capita - than either variable alone, each of which can explain roughly 50%.

The Ramsey-style model of Manuelli and Seshadri (2005) would be a natural extension: In their model, ex-ante differences in total factor productivity of at most 27% interact with education decisions and fertility choices to completely replicate the span of the current global income distribution. In their calibration—less naïve and more complex then the one I present—a 1% rise in TFP (e.g., 1 IQ point) causes a 9% rise in steady- state productivity. Manuelli and Seshadri leave unanswered the question of what those ex-ante differences in TFP might be, but persistent differences in national average IQ are a natural candidate.

"Economic gains resulting from the reduction in children's exposure to lead in the United States", Grosse et al 2002 (fulltext)

We assumed the change in cognitive ability resulting from declines in BLLs, on the basis of published meta-analyses, to be between 0.185 and 0.323 IQ points for each 1 g/dL blood lead concentration. These calculations imply that, because of falling BLLs, U.S. preschool-aged children in the late 1990s had IQs that were, on average, 2.2-4.7 points higher than they would have been if they had the blood lead distribution observed among U.S. preschool-aged children in the late 1970s. We estimated that each IQ point raises worker productivity 1.76-2.38%. With discounted lifetime earnings of $723,300 for each 2-year-old in 2000 dollars, the estimated economic benefit for each year's cohort of 3.8 million 2-year-old children ranges from $110 billion to $319 billion.

...We calculated the economic benefit realized by reduced lead exposure in the United States since the late 1970s through a series of steps, each associated with a component of the model in Figure 1. First, we estimated the amount by which BLLs have fallen over time through secondary analysis of data from the National Health and Nutrition Examination Surveys (NHANES). Second, we applied estimates from published studies of the strength, shape, and magnitude of the association between BLLs and cognitive ability test scores. In particular, we examined two published meta-analyses to arrive at estimates of the ratio of change in BLL to change in IQ. Third, on the basis of a brief review of literature on the association between cognitive ability and earning potential, we estimated the percentage change in earnings associated with absolute differences in IQ levels. Fourth, we calculated the present value (2000 dollars) of the percentage change in earnings.

...Schwartz (6) calculated that the total effect of a 1-point difference in cognitive ability is a 1.76% difference in earnings. Of this amount, 0.5% is the direct effect of ability on earnings. Schwartz (6) took this estimate from an econometric study by Griliches (19) that was representative of other econometric studies from the 1970s. Schwartz (6) assumed that a given difference in IQ scores observed in school-aged children can be expected to lead to a comparable difference in achieved cognitive ability in young adults.

The indirect effect of ability on earnings, which accounts for the remaining 1.26% difference, is modeled through two pathways. One is the effect of ability on years of schooling multiplied by the effect of years of schooling on hourly earnings. Needleman et al. (20) reported that a 4.5-point difference in IQ between groups with high tooth lead and with low tooth lead was associated with a 0.59 difference in grade level attained. The ratio of the two numbers implies a difference of 0.131 years of schooling for 1 IQ point. If each additional year of schooling results in a 6% increase in hourly wages, 1 IQ point would lead to a 0.79% increase in expected earnings through years of education. Second, Schwartz (6) modeled ability as influencing employment participation through influence on high school graduation. On the basis of the analysis of Needleman et al. (20) and 1978 survey data reported by Krupnick and Cropper (21), Schwartz (6) calculated that 1 point in IQ is associated with a 4.5% difference in probability of graduating from high school and that high school graduation is associated with a 10.5% difference in labor force participation. On the assumption of an equivalent percentage change in annual earnings, this leads to a 0.47% difference in expected earnings. Salkever (22) published an alternate estimation of the effect of cognitive ability on earnings. Salkever directly estimated the effect of ability on annual earnings, among those with earnings. The estimated association of ability with annual earnings incorporates both the effect of ability on hourly earnings and its effect on annual hours of work. He also added a direct pathway from ability to work participation independent of education.

According to Salkever (22), a 1-point difference in ability is associated with a 1.931% difference in earnings for males and a 3.225% difference for females. The direct effect on earnings is 1.24% for males and 1.40% for females. Salkever (22) analyzed income and educational attainment data from the 1990 wave of the National Longitudinal Study of Youth (NLSY) in combination with AFQT scores collected during 1979–1980, when the respondents were 14–23 years of age.

For the indirect effect of ability on schooling attainment, Salkever (22) reported that a 1-point difference was associated with 0.1007 years of schooling attained for both males and females in the NLSY data. Also, 1 year of schooling attainment raised hourly earnings by 4.88% for males and 10.08% for females in the 1990 NLSY data. According to these results, a 1-point difference in ability is associated, through an indirect effect on schooling, with a 0.49% difference in earnings for males and a 1.10% difference in earnings for females.

Salkever (22) reported that the direct effect of a 1-point difference in ability was a 0.1602 percentage point difference in probability of labor force participation for males and a 0.3679 percentage point difference for females. In addition, he calculated that 1 year of schooling raised labor force participation rates by 0.3536 percentage points for males and 2.8247 percentage points for females. Subtracting the other components from the totals, a 1-point change in cognitive ability is associated with a difference in earnings of 0.20% for males and 0.72% for females through effects on labor force participation. Finally, in an analysis of the 1990 NLSY earnings data, Neal and Johnson (23) reported smaller estimates of the effect of cognitive ability on earnings. They included workers who took the AFQT test when they were 14–18 years of age and excluded those who took the AFQT test at 19–23 years of age to make the test scores more comparable. They also estimated the total effect of ability on hourly earnings by excluding schooling variables. Their estimates indicate that a 1point difference in AFQT scores is associated with a 1.15% difference in earnings for men and a 1.52% difference for women. Their estimate of the direct effect of ability on hourly earnings, controlling for schooling, is 0.83% for men; they reported no estimate for women.

The analysis of Neal and Johnson (23) has no link from ability to labor force participation. According to Salkever (22), a 1-point difference in ability leads to a 0.20% difference for males and 0.72% for females. If we add Salkever’s figures (22) to the estimates from Neal and Johnson (23), the total effect of a 1-point difference in ability on earnings is 1.35% for males and 2.24% for females.

Their summary estimate from pg5/567 is a lower-middle-upperbound of each IQ point is worth, in net present value 2000 dollars: 12,700-14,500-17,200.

(Note that these figures, as usual, are net estimates of the value to an individual: so they are including zero-sum games and positional benefits. They aren't giving estimates of the positive externalities or marginal benefits.)

"Quality of Institutions : Does Intelligence Matter?", Kalonda-Kanyama & Kodila-Tedika 2012:

We analyze the effect of the average level of intelligence on different measures of the quality of institutions, using a 2006 cross-sectional sample of 113 countries. The results show that average IQ positively affects all the measures of institutional quality considered in our study, namely government eciency, regulatory quality, rule of law, political stability and voice and accountability. The positive effect of intelligence is robust to controlling for other determinants of institutional quality.

"IQ and Permanent Income: Sizing Up the “IQ Paradox”":

I used data from the NLSY79 which is an ongoing longitudinal study that follows the lives of a large sample of Americans born in 1957-64. Specifically, I used the nationally representative subsample comprising more than 6000 individuals...The unstandardized slope coefficient is 0.025 (95% CI: 0.023-0.027). Because the dependent variable is logarithmic, this coefficient, when multiplied by 100, can be (approximately) interpreted as the percent change in income in (unlogged) dollars associated with a 1 IQ point change.[Note] Therefore, one additional IQ point predicts a 2.5% boost in income. The standardized effect size, or correlation, is 0.36 and the R squared is 13%.

"Are Smarter Groups More Cooperative? Evidence from Prisoner's Dilemma Experiments, 1959-2003", Jones 2008:

A meta-study of repeated prisoner's dilemma experiments run at numerous universities suggests that students cooperate 5% to 8% more often for every 100 point increase in the school's average SAT score.

Later: http://econlog.econlib.org/archives/2012/10/group_iq_one_so.html

This finding was the first of its kind: In prisoner's dilemmas, smarter groups really were more cooperative. Since then other researchers have found similar results, some of which I discuss in Section III of this article for the Asian Development Review. It looks like intelligence is a form of social intelligence...Does that happen in the real world? If it does, does it mean that there are negative political externalities to low-skill immigration? That's a topic for a later time. Another worthy question: Why would high IQ groups be more cooperative anyway? Isn't cynicism intelligent? Sure, sometimes, but the political entrepreneur who can find a way to sustain a truce can probably skim quite a lot of the resulting prosperity off for herself. And people who are better at solving the puzzles in an IQ test are probably better at solving the puzzles of human interaction.

"IQ in the Production Function: Evidence from Immigrant Earnings", Jones & Schneider 2008:

We show that a country’s average IQ score is a useful predictor of the wages that immigrants from that country earn in the U.S., whether or not one adjusts for immigrant education. Just as in numerous microeconomic studies, 1 IQ point predicts 1% higher wages, suggesting that IQ tests capture an important difference in cross-country worker productivity. In a cross-country development accounting exercise, about one-sixth of the global inequality in log income can be explained by the effect of large, persistent differences in national average IQ on the private marginal product of labor. Taken together with the results of Jones and Schneider (2006) and Hanushek and Kimko (2000), this suggests that cognitive skills matter more for groups than for individuals.

"Costs and benefits of iodine supplementation for pregnant women in a mildly to moderately iodine-deficient population: a modelling analysis" (mirror; appendices), Monahan et al 2015

Background: Results from previous studies show that the cognitive ability of off spring might be irreversibly damaged as a result of their mother's mild iodine deficiency during pregnancy. A reduced intelligence quotient (IQ) score has broad economic and societal cost implications because intelligence affects wellbeing, income, and education outcomes. Although pregnancy and lactation lead to increased iodine needs, no UK recommendations for iodine supplementation have been issued to pregnant women. We aimed to investigate the cost-effectiveness of iodine supplementation versus no supplementation for pregnant women in a mildly to moderately iodine-deficient population for which a population- based iodine supplementation programme-for example, universal salt iodisation-did not exist.

Methods: We systematically searched MEDLINE, Embase, EconLit, and NHS EED for economic studies that linked IQ and income published in all languages until Aug 21, 2014. We took clinical data relating to iodine deficiency in pregnant women and the effect on IQ in their children aged 8-9 years from primary research. A decision tree was developed to compare the treatment strategies of iodine supplementation in tablet form with no iodine supplementation for pregnant women in the UK. Analyses were done from a health service perspective (analysis 1; taking direct health service costs into account) and societal perspective (analysis 2; taking education costs and the value of an IQ point itself into account), and presented in terms of cost (in sterling, relevant to 2013) per IQ point gained in the off spring. We made data-supported assumptions to complete these analyses, but used a conservative approach that limited the benefits of iodine supplementation and overestimated its potential harms.

Findings: Our systematic search identified 1361 published articles, of which eight were assessed to calculate the monetary value of an IQ point. A discounted lifetime value of an additional IQ point based on earnings was estimated to be £3297 (study estimates range from £1319 to £11 967) for the off spring cohort. Iodine supplementation was cost saving from both a health service perspective (saving £199 per pregnant woman [sensitivity analysis range -£42 to £229]) and societal perspective (saving £4476 per pregnant woman [sensitivity analysis range £540 to £4495]), with a net gain of 1·22 IQ points in each analysis. Base case results were robust to sensitivity analyses.

Interpretation: Iodine supplementation for pregnant women in the UK is potentially cost saving. This finding also has implications for the 1·88 billion people in the 32 countries with iodine deficiency worldwide. Valuation of IQ points should consider non-earnings benefits-eg, health benefits associated with a higher IQ not germane to earnings.

IQ estimates:

Our systematic search identified 1361 published articles, of which eight studies 47-54 passed quality criteria and were assessed to calculate the monetary value of an IQ point (appendix p 4). The quality criteria were as follows: an individual's IQ is used and is not a proxy; variables are clearly specified; IQ measure follows a conventional normal distribution with a mean of 100 and standard deviation of 15 or sufficient information is included in the study to allow the IQ measure's distribution to be converted into one (for cross study comparability); and the results reported in currency form have the applicable year stated. Most of the studies valued an IQ point on the basis of its effect on an individual's income (appendix p 3). The issue of differences in scaling of IQ tests hindered the comparability across studies. The value of an IQ point, derived from the systematic search and applied to the unborn cohort, comes from the lifetime earnings premium of an additional IQ point. This is calculated to be £3297 (study estimates range from £1319 to £11967; after adjustment with life tables).

All the details are in the Monahan et al 2015 appendices

One study looked at people's willingness to pay (WTP) for an additional IQ point. 4 Five studies used econometric regressions to determine the individuals IQ's effect on their subsequent income, 5-9 whereas two studies were cost benefit analysis on reducing lead exposure. 10,11 Only one of the studies included in the systematic literature search was not set in the USA. 5...In keeping with the conservative nature of the model, the relatively high earnings premium from IQ points from Schwartz 10 and Salkever 11 are excluded on the basis that the effect may be overstated.

The 8 studies are listed on pg8 of the appendix, Table 1:

• Fletcher J. "Friends or Family? Revisiting the Effects of High School Popularity on Adult Earnings". 2013. National Bureau of Economic Research Working Papers: 19232
• Lutter RW. "Valuing children's health: A reassessment of the benefits of lower lead levels". AEI-Brookings Joint Center Working Paper No. 00-02. 2000.
• Mueller G, Plug E. "Estimating the Effect of Personality on Male and Female Earnings". Ind Lab Relat Rev. 2006;60(1):3-22.
• Salkever DS. "Updated estimates of earnings benefits from reduced exposure of children to environmental lead". Environ Res. 1995;70(1):1-6.
• Schwartz J. "Societal benefits of reducing lead exposure". Environ Res. 1994;66(1):105-24.
• de Wolff P, van Slijpe ARD. "The Relation Between Income, Intelligence, Education and Social Background". Europ Econ Rev. 1973;4(3):235-64.
• Zax JS, Rees DI. IQ, "Academic Performance, Environment, and Earnings". Rev Econ Stat. 2002;84(4):600-16
• Zagorsky JL. "Do you have to be smart to be rich? The impact of IQ on wealth, income and financial distress". Intelligence. 2007;35(5):489-501.

(Note that by including covariates that are obviously caused by IQ rather than independent, and excluding any attempt at measuring the many positive externalities of greater intelligence, these numbers can usually be considered substantial underestimates of country-wide benefits.)

"The High Cost of Low Educational Performance: the long-run economic impact of improving PISA outcomes", Hanushek & Woessmann 2010:

This report uses recent economic modelling to relate cognitive skills – as measured by PISA and other international instruments – to economic growth. The relationship indicates that relatively small improvements in the skills of a nation’s labour force can have very large impacts on future well-being.

...A modest goal of having all OECD countries boost their average PISA scores by 25 points over the next 20 years – which is less than the most rapidly improving education system in the OECD, Poland, achieved between 2000 and 2006 alone – implies an aggregate gain of OECD GDP of USD 115 trillion over the lifetime of the generation born in 2010 (as evaluated at the start of reform in terms of real present value of future improvements in GDP) (Figure 1). Bringing all countries up to the average performance of Finland, OECD’s best performing education system in PISA, would result in gains in the order of USD 260 trillion (Figure 4). The report also shows that it is the quality of learning outcomes, not the length of schooling, which makes the difference. Other aggressive goals, such as bringing all students to a level of minimal proficiency for the OECD (i.e. reaching a PISA score of 400), would imply aggregate GDP increases of close to USD 200 trillion according to historical growth relationships (Figure 2).

...Using data from international student achievement tests, Hanushek and Kimko (2000) demonstrate a statistically and economically significant positive effect of cognitive skills on economic growth in 1960-90. Their estimates suggest that one country-level standard deviation higher test performance would yield around one percentage point higher annual growth rates. The country-level standard deviation is equivalent to 47 test-score points in the PISA 2000 mathematics assessment. Again, in terms of the PISA 2000 mathematics scores, 47 points would be roughly the average difference between Sweden and Japan (the best performer among OECD countries in 2000) or between the average Greek student and the OECD average score. One percentage point difference in growth is itself a very large value, because the average annual growth of OECD countries has been roughly 1.5%.

Their estimate stems from a statistical model that relates annual growth rates of real GDP per capita to the measure of cognitive skills, years of schooling, the initial level of income and a wide variety of other variables that might affect growth including in different specifications the population growth rates, political measures, or openness of the economies.

...The relationship between cognitive skills and economic growth has now been demonstrated in a range of studies. As reviewed in Hanushek and Woessmann (2008), these studies employ measures of cognitive skills that draw upon the international testing of PISA and of TIMSS (Trends in International Mathematics and Science Study) (along with earlier versions of these).7 The uniform result is that the international achievement measures provide an accurate measure of the skills of the labour force in different countries and that these skills are closely tied to economic outcomes.8

...While the PISA tests are now well-known throughout the OECD, the history of testing is less understood. Between 1964 and 2003, 12 different international tests of mathematics, science, or reading were administered to a voluntarily participating group of countries (see Annex Tables A1 and A2). These include 36 different possible scores for year-age-test combinations (e.g. science for students of grade 8 in 1972 as part of the First International Science Study or mathematics of 15-year-olds in 2000 as a part of the Programme on International Student Assessment). Only the United States participated in all possible tests. The assessments are designed to identify a common set of expected skills, which were then tested in the local language. It is easier to do this in mathematics and science than in reading, and a majority of the international testing has focused on mathematics and science. Each test is newly constructed, until recently with no effort to link to any of the other tests. While the analysis here focuses on mathematics and science, these scores are highly correlated with reading test scores and employing just mathematics and science performance does not distort the growth relationship that is estimated; see Hanushek and Woessmann (2009). The goal here is construction of consistent measures at the national level that will allow comparing performance across countries, even when they did not each participate in a common assessment.

...The simplest overview of the relationship is found in Figure 6 that plots regional growth in real per capita GDP between 1960 and 2000 against average test scores after allowing for differences in initial GDP per capita in 1960.14 Regional annual growth rates, which vary from 1.4% in Sub-Saharan Africa to 4.5% in East Asia, fall on a straight line.15 But school attainment, when added to this regression, is unrelated to growth- rate differences. Figure 6 suggests that, conditional on initial income levels, regional growth over the last four decades is completely described by differences in cognitive skills.

Second, to tackle the most obvious reverse-causality issues, Hanushek and Woessmann (2009) separate the timing of the analysis by estimating the effect of scores on tests conducted until the early 1980s on economic growth in 1980-2000. In this analysis, available for a smaller sample of countries only, test scores pre-date the growth period. The estimate shows a significant positive effect that is about twice as large as the coefficient used in the simulations here.

Needless to say, "cognitive skills" here is essentially an euphemism for intelligence/IQ.

Education and general intelligence both serve to inform opinions, but do they lead to greater attitude extremity? We use questions on economic policy, social issues, and environmental issues from the General Social Survey to test the impact of education and intelligence on attitude extremity, as measured by deviation from centrist or neutral positions. Using quantile regression modeling, we find that intelligence is a moderating force across the entire distribution in economic, social, and environmental policy beliefs. Completing high school strongly correlates to reduced extremity, particularly in the upper quantiles. College education increases attitude extremity in the lower tail of environmental beliefs. The relevance of the low extremity tail (lower quantiles) to potential swing-voters and the high extremity tail (upper quantiles) to a political party’s core are discussed.

"Education, Intelligence, and Attitude Extremity", Makowsky & Miller 2012

The authors analysed data from the 2007 Adult Psychiatric Morbidity Survey in England. The participants were adults aged 16 years or over, living in private households in 2007. Data from 6870 participants were included in the study...Happiness is significantly associated with IQ. Those in the lowest IQ range (70–99) reported the lowest levels of happiness compared with the highest IQ group (120–129). Mediation analysis using the continuous IQ variable found dependency in activities of daily living, income, health and neurotic symptoms were strong mediators of the relationship, as they reduced the association between happiness and IQ by 50 %

"The relationship between happiness and intelligent quotient: the contribution of socio-economic and clinical factors", Ali et al 2012; effect is weakened once you take into account all the relevant variables but does sort of still exist.

I think that you might be confusing causation and correlation here. Countries that started to industrialize earlier have higher average IQ and higher GDP per capita. That would produce the effect you refer to. Whether or not the increased intelligence then contributes to further economic growth is a different matter.

Above link is dead. Here is a new one

http://mason.gmu.edu/~gjonesb/JonesADR

"Exponential correlation of IQ and the wealth of nations", Dickerson 2006:

Plots of mean IQ and per capita real Gross Domestic Product for groups of 81 and 185 nations, as collected by Lynn and Vanhanen, are best fitted by an exponential function of the form: GDP = a 10^b\(IQ), where a and b are empirical constants. Exponential fitting yields markedly higher correlation coefficients than either linear or quadratic. The implication of exponential fitting is that a given increment in IQ, anywhere along the IQ scale, results in a given percentage in GDP, rather than a given dollar increase as linear fitting would predict. As a rough rule of thumb, an increase of 10 points in mean IQ results in a doubling of the per capita GDP.

....In their book, IQ and the Wealth of Nations, Lynn and Vanhanen (2002) present a table listing for 81 nations the measured mean IQ and the per capita real Gross Domestic Product as of 1998 (their Table 7.7). They subsequently extend this to all 185 nations, using estimated IQs for the 104 new entries based chiefly on IQ values for immediate neighbors (their Table 8.9). In both cases they observe a significant correlation between IQ and GDP, with linear correlation factors R^2 = 0.537 for the 81-nation group and 0.389 for 185 nations. McDaniel and Whetzel have extended the examination of correlations to quadratic fitting in a paper that demonstrates the robustness of these correlations to minor variations in individual IQ values (McDaniel & Whetzel, in press). But an even stronger correlation is found if the fitting is exponential rather than linear or quadratic.

lynn <- read.table(stdin(),header=TRUE,sep="")
Country IQ rGDPpc
Argentina  96  12013
Australia 98 22452
Austria 102 23166
Barbados 78 12001
Belgium 100 23223
Brazil 87 6625
Bulgaria 93 4809
Canada 97 23582
China 100 3105
Colombia 89 6006
Congo 65 822
Congo 73 995
Croatia 90 6749
Cuba 85 3967
CzechRepublic 97 12362
Denmark 98 24218
Ecuador 80 3003
Egypt 83 3041
EquatorialGuinea 59 1817
Ethiopia 63 574
Fiji 84 4231
Finland 97 20847
France 98 21175
Germany 102 22169
Ghana 71 1735
Greece 92 13943
Guatemala 79 3505
Guinea 66 1782
HongKong 107 20763
Hungary 99 10232
India 81 2077
Indonesia 89 2651
Iran 84 5121
Iraq 87 3197
Ireland 93 21482
Israel 94 17301
Italy 102 20585
Jamaica 72 3389
Japan 105 23257
Kenya 72 980
Lebanon 86 4326
Malaysia 92 8137
MarshallIslands 84 3000
Mexico 87 7704
Morocco 85 3305
Nepal 78 1157
Netherlands 102 22176
NewZealand 100 17288
Nigeria 67 795
Norway 98 26342
Peru 90 4282
Philippines 86 3555
Poland 99 7619
Portugal 95 14701
PuertoRico 84 8000
Qatar 78 20987
Romania 94 5648
Russia 96 6460
SierraLeone 64 458
Singapore 103 24210
Slovakia 96 9699
Slovenia 95 14293
SouthAfrica 72 8488
SouthKorea 106 13478
Spain 97 16212
Sudan 72 1394
Suriname 89 5161
Sweden 101 20659
Switzerland 101 25512
Taiwan 104 13000
Tanzania 72 480
Thailand 91 5456
Tonga 87 3000
Turkey 90 6422
UKingdom 100 20336
Uganda 73 1074
UnitedStates 98 29605
Uruguay 96 8623
WesternSamoa 87 3832
Zambia 77 719
Zimbabwe 66 2669

em <- lm(log(lynn$rGDPpc) ~ lynn$IQ); summary(em)

Call:
lm(formula = log(lynn$rGDPpc) ~ lynn$IQ)

Residuals:
    Min      1Q  Median      3Q     Max
-1.6124 -0.3866 -0.0429  0.3363  2.0311

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  1.77760    0.51848    3.43  0.00097
lynn$IQ      0.07876    0.00583   13.52  < 2e-16

Residual standard error: 0.624 on 79 degrees of freedom
Multiple R-squared: 0.698,    Adjusted R-squared: 0.694
F-statistic:  183 on 1 and 79 DF,  p-value: <2e-16
# plot
plot (log(lynn$rGDPpc) ~ lynn$IQ)
abline(em)

# an attempt at La Griffe
erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
sf <- function(iq) ((69321/2) * (1 + erf(((iq - 108)/15) / sqrt(2))))
# check for sigmoid
# plot(c(85:130), sf(c(85:130)))
lg <- lm(log(lynn$rGDPpc) ~ sf(lynn$IQ)); summary(lg)
Call:
lm(formula = log(lynn$rGDPpc) ~ sf(lynn$IQ))

Residuals:
    Min      1Q  Median      3Q     Max
-2.5788 -0.6857  0.0678  1.0521  1.5901

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.705620   0.126152   69.01   <2e-16
sf(lynn$IQ) 0.000121   0.000102    1.19     0.24

Residual standard error: 1.13 on 79 degrees of freedom
Multiple R-squared: 0.0175,    Adjusted R-squared: 0.0051
F-statistic: 1.41 on 1 and 79 DF,  p-value: 0.239
# same plotting code

sf <- function(iq,c) ((69321/2) * (1 + erf(((iq - c)/15) / sqrt(2))))

lg <- lm(lynn$rGDPpc ~ sf(lynn$IQ,108)); summary(lg)

sf <- function(iq,c) ((1/2) * (1 + erf((iq-c)/(15*sqrt(2)))))

R> sf <- function(iq,f,c) ((c/2) * (1 + erf((iq-f)/(15*sqrt(2)))))
R> summary(nls(rGDPpc ~ sf(IQ,f,c), lynn, start=list(f=110,c=40000)))

Formula: rGDPpc ~ sf(IQ, f, c)

Parameters:
  Estimate Std. Error t value Pr(>|t|)
f    99.64       3.07   32.44  < 2e-16
c 34779.17    6263.90    5.55  3.7e-07

Residual standard error: 5310 on 79 degrees of freedom

Number of iterations to convergence: 4
Achieved convergence tolerance: 8.22e-06

> sf <- function(iq,iq0) ((1+erf((iq-iq0)/(15*sqrt(2))))/2)
> egdp <- function(iq,iq0,m,b) (m*sf(iq,iq0)+b)
> summary(nls(rGDPpc ~ egdp(IQ,iq0,m,b), lynn2, start=list(iq0=110,m=40000,b=0)))
Formula: rGDPpc ~ egdp(IQ, iq0, m, b)
Parameters:
Estimate Std. Error t value Pr(>|t|)
iq0   110.019      4.305  25.556  < 2e-16 ***
m   77694.174  26708.502   2.909  0.00486 **
b     679.688   1039.144   0.654  0.51520
Residual standard error: 4054 on 70 degrees of freedom

> gwe <- lm(lynn2$rGDPpc ~ sf(lynn2$IQ,99.64)); summary(gwe)
Call:
lm(formula = lynn2$rGDPpc ~ sf(lynn2$IQ, 99.64))
Residuals:
 Min       1Q   Median       3Q      Max
-10621.6  -2463.1    442.6   1743.4  12439.7
Coefficients:
                Estimate Std. Error t value Pr(>|t|)
(Intercept)          -1345.7      886.6  -1.518    0.133
sf(lynn2$IQ, 99.64)  40552.7     2724.1  14.887   <2e-16 ***
Residual standard error: 4241 on 71 degrees of freedom
Multiple R-squared: 0.7574,     Adjusted R-squared: 0.7539
F-statistic: 221.6 on 1 and 71 DF,  p-value: < 2.2e-16

> opt <- lm(lynn2$rGDPpc ~ sf(lynn2$IQ,110)); summary(opt)
Call:
lm(formula = lynn2$rGDPpc ~ sf(lynn2$IQ, 110))
Residuals:
 Min       1Q   Median       3Q      Max
-11030.3  -1540.1   -416.8   1308.6  12493.5
Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept)          676.4      731.5   0.925    0.358
sf(lynn2$IQ, 110)  77577.0     4869.6  15.931   <2e-16 ***
Residual standard error: 4025 on 71 degrees of freedom
Multiple R-squared: 0.7814,     Adjusted R-squared: 0.7783
F-statistic: 253.8 on 1 and 71 DF,  p-value: < 2.2e-16

> his <- lm(lynn2$rGDPpc ~ sf(lynn2$IQ,108.2)); summary(his)
Call:
lm(formula = lynn2$rGDPpc ~ sf(lynn2$IQ, 108.2))
Residuals:
 Min       1Q   Median       3Q      Max
-11014.0  -1710.0   -196.9   1396.3  12432.9
Coefficients:
                Estimate Std. Error t value Pr(>|t|)
(Intercept)            362.6      748.0   0.485    0.629
sf(lynn2$IQ, 108.2)  67711.5     4258.5  15.900   <2e-16 ***
Residual standard error: 4031 on 71 degrees of freedom
Multiple R-squared: 0.7807,     Adjusted R-squared: 0.7777
F-statistic: 252.8 on 1 and 71 DF,  p-value: < 2.2e-16

> em <- lm(log(lynn2$rGDPpc) ~ lynn2$IQ); summary(em)
Call:
lm(formula = log(lynn2$rGDPpc) ~ lynn2$IQ)
Residuals:
 Min       1Q   Median       3Q      Max
-1.12157 -0.34268 -0.00503  0.29596  1.41540
Coefficients:
        Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.812650   0.446308   1.821   0.0728 .
lynn2$IQ    0.089439   0.005051  17.706   <2e-16 ***
Residual standard error: 0.4961 on 71 degrees of freedom
Multiple R-squared: 0.8153,     Adjusted R-squared: 0.8127
F-statistic: 313.5 on 1 and 71 DF,  p-value: < 2.2e-16

egdp <- function(iq,iq0,m,b) (m*sf(iq,iq0)+b)

R> erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
R> sf <- function(iq,iq0) ((1+erf((iq-iq0)/(15*sqrt(2))))/2)
R> egdp <- function(iq,iq0,m,b) (m*sf(iq,iq0)+b)
R> summary(nls(rGDPpc ~ egdp(IQ,iq0,m,b), lynn, start=list(iq0=110,m=40000,b=0)))

Formula: rGDPpc ~ egdp(IQ, iq0, m, b)

Parameters:
    Estimate Std. Error t value Pr(>|t|)
iq0   102.08       4.89   20.88  < 2e-16
m   37108.87    9107.73    4.07  0.00011
b    1140.94    1445.76    0.79  0.43241

Residual standard error: 5320 on 78 degrees of freedom

Number of iterations to convergence: 7
Achieved convergence tolerance: 5.09e-06

"Salt Iodization and the Enfranchisement of the American Worker", Adhvaryu et al 2013:

...We find substantial impacts of salt iodization. High school completion rose by 6 percentage points, and labor force participation went up by 1 point. Analysis of income transitions by quantile shows that the new labor force joiners entered at the bottom of the wage distribution and took up blue collar labor, pulling down average wage income conditional on employment. Our results inform the ongoing debate on salt iodization in many low-income countries. We show that large-scale iodized salt distribution had a targeted impact, benefiting the worker on the margin of employment, and generating sizeable economic returns at low cost...The recent study by Feyrer et al. (2013) estimates that Morton Salt Co.’s decision to iodize may have increased IQ by 15 points, accounting for a significant part of the Flynn Effect, the steady rise IQ in the US over the twentieth century. Our estimates, paired with this number, suggest that each IQ point accounts for nearly one tenth of a point increase in labor force participation.

If, in the 1920s, 10 IQ points could increase your labor participation rate by 1%, then what on earth does the multiplier look like now? The 1920s weren't really known for their demands on intelligence, after all.

And note the relevance to discussions of technological unemployment: since the gains are concentrated in the low end (think 80s, 90s) due to the threshold nature of iodine & IQ, this employment increase means that already, a century ago, people in the low-end range were having trouble being employed.

A 2012 Jones followup: "Will the intelligent inherit the earth? IQ and time preference in the global economy"

Social science research has shown that intelligence is positively correlated with patience and frugality, while growth theory predicts that more patient countries will save more. This implies that if nations differ in national average IQ, countries with higher average cognitive skills will tend to hold a greater share of the world’s tradable assets. I provide empirical evidence that in today’s world, countries whose residents currently have the highest average IQs have higher savings rates, higher ratios of net foreign assets to GDP, and higher ratios of U.S. Treasuries to GDP. These nations tend to be in East Asia and its offshoots. The relationship between national average IQ and net foreign assets has strengthened since the end of Bretton Woods.

...And time preference differs across countries in part because psychometric intelligence, a key predictor of patient behavior, differs persistently across countries (Wicherts et al., 2010a,b; Jones and Schneider, 2010)....John Rae (1834) provides a precursor of the approach presented here: Chapter Six of his treatise (cited in Becker and Mulligan, 1997, and Frederick et al., 2002) focuses on individual determinants of savings, including differences in rates of time preference, while his Chapter Seven draws out the cross-country implications...A recent meta-analysis of 24 studies by Shamosh and Gray concluded: “[A]cross studies, higher intelligence was associated with lower D[elay] D[iscounting]...” Their meta-study drew on experiments with preschool children and college students, drug addicts and relatively healthy populations: With few exceptions, they found a reliable relationship between measured intelligence and patience. And recent work by economists (Frederick, 2005; Benjamin et al. 2006; Burks et al, 2009; Chabris et al., 2007) has demonstrated that low-IQ individuals tend to act in a more “behavioral,” more impulsive fashion when facing decisions between smaller rewards sooner versus larger rewards later.

On its own, I don't consider this strong evidence for the greater productivity of the IQ elite. If they were contributions to open-source projects, that would be one thing. But people doing work that generates patents which don't lead to higher income - that raises some questions for me. Is it possible that extremely high IQ is associated with a tendency to become "addicted" to a game like patenting? Added: I think Gwern and I agree more than many people might think reading this comment.

Perhaps economic returns to IQ as so low because there are other skills which are good for getting economic returns, and those skills don't correlate strongly with IQ.

A gentle introduction to the mythos.

E.g. good players are less prone to confirmation bias

True; see 2004 "Chess Masters' Hypothesis Testing" Cowley & Bryne:

But experimental evidence from studies of reasoning shows that people often find falsification difficult. We suggest that domain expertise may facilitate falsification. We consider new experimental data about chess experts’ hypothesis testing. The results show that chess masters were readily able to falsify their plans. They generated move sequences that falsified their plans more readily than novice players, who tended to confirm their plans. The finding that experts in a domain are more likely to falsify their hypotheses has important implications for the debate about human rationality.

I think that this is transferable

Well... The chess literature and general literature on learning rarely finds transfer. From the Nature coverage of that study:

Byrne and Cowley now hope to study developing chess players to find out how and when they develop falsification strategies. They also want to test chess masters in other activities that involve testing hypotheses - such as logic problems - to discover if their falsification skill is transferable. On this point Orr is more sceptical: "I've never felt that chess skills cross over like that, it's a very specific skill."

Checking Google Scholar, I see only one apparent followup, the 2005 paper by the same authors, "When falsification is the only path to truth":

Can people consistently attempt to falsify, that is, search for refuting evidence, when testing the truth of hypotheses? Experimental evidence indicates that people tend to search for confirming evidence. We report two novel experiments that show that people can consistently falsify when it is the only helpful strategy. Experiment 1 showed that participants readily falsified somebody else’s hypothesis. Their task was to test a hypothesis belonging to an ‘imaginary participant’ and they knew it was a low quality hypothesis. Experiment 2 showed that participants were able to falsify a low quality hypothesis belonging to an imaginary participant more readily than their own low quality hypothesis. The results have important implications for theories of hypothesis testing and human rationality.

While interesting and very relevant to some things (like programmers' practice of 'rubber ducking' - explaining their problem to an imaginary creature), it doesn't directly address chess transfer.

I agree-- pleasure in thinking might not be part of the study of rationality, but it could very much be part of raising sanity waterline.

What real scale and era, if any, is even roughly modeled?

Vladimir is talking about reversed stupidity in the LW sense; but I don't think it applies to cwillu's quote. Asserting that a false statement is false is not "reversed stupidity".

Not so, I can get very inventive trying to counter what I perceive as wrong or offensive. Disproving sources to offering countering and contradictory postulations; all are better when flung back. One of my great joys is when my snotty, off-hand comment makes someone go after real data to prove me wrong. If this is applied to some theoretical position, who knows where it could lead you. I'm pretty sure there is at least one Edison joke about this.

What is new here? It reads like the same old, wrong, group selection argument.

His complaint is from here:

and groups who hold them.

Group selection doesn't work. if you were to delete those two words, it would be fine, but if you start talking about increasing the reproductive fitness of a group as a whole, evolutionary biologists and other scientists will tend to dismiss what you say.

I think what's mildly confusing is the normatively positive use of the word "bias," which typically suggests deviation from ideal reasoning. But I suppose it is a bias in the sense that one could go too far and update on every little piece of random noise...

"Inductive bias" is a technical term, where the word bias isn't meant negatively.

I think that's it, though there are at least two sorts of bad bias. The one you describe (nothing is important enough to notice or remember) is one, but there's also having a bad theory ("that annoying person is aiming it all at me", for example, which would lead to not noticing evidence of things going wrong which have nothing to do with malice).

This is reminding me of one of my favorite bits from Illuminatus!. There's a man with filing cabinets [1] full of information about the first Kennedy assassination. He's convinced that someday, he'll find the one fact which will make it all make sense. He doesn't realize that half of what's he's got is lies people made up to cover their asses.

In the novel, there were five conspiracies to kill JFK-- but that character isn't going to find out about them.

[1] The story was written before the internet.

I don't disagree with any of this. Most LGBTQ activists wouldn't either. I used the hedging language "appears" because I don't know for sure what kind of agency Multiheaded thinks people have over their sexuality.

Yeah, I meant something like that.

... I like the way you talk. This goes a long way into explaining the same person saying "homosexuality is not a choice" and "I have been with qute a few straight guys", as well as the treatment bi people get as "fence-sitters" and the resentment they generate by having an easier time in the closet.

Link.

Sure, but that implies a rather inefficient market - not even exploring major possibilities! Wouldn't work on Wall Street, I don't think.

I suspect that the intent of the original quote is that they'll assess us by our curiosity towards, and effectiveness in discovering, our origins. As Dawkins is a biologist, he is implying that evolution by natural selection is an important part of it, which of course is true. An astronomer or cosmologist might consider a theory on the origins of the universe itself to be more important, a biochemist might consider abiogenesis to be the key, and so on.

Personally, I can see where he's coming from, though I can't say I feel like I know enough about the evolution of intelligence to come up with a valid argument as to whether an alien species would consider this to be a good metric to evaluate us with. One could argue that interest in oneself is an important aspect of intelligence, and scientific enquiry important to the development of space travel, and so a species capable of travelling to us would have those qualities and look for them in the creatures they found.

This is my time posting here, so I'm probably not quite up to the standards of the rest of you just yet. Sorry if I said something stupid.

It's anthropomorphism to assume that it would occur to advanced aliens to try to understand us empathetically rather than causally/technically in the first place, though.

Selective breeding isn't necessarily the same as artificial selection, however. The taming of dogs and cats was largely considered accidental; the neotenous animals were more human-friendly and thus able to access greater amounts of food supplies from humans until eventually they could directly interact, whereupon (at least in dogs) "usefulness" became a valued trait.

There wasn't purposefulness in this; people just fed the better dogs more and disliked the 'worse' dogs. It wasn't until the mid-1700's that dog 'breeds' became a concept.

I mean we'd do more than twice as well with one question than with two, and more than twice as well with three than with two. Usually, diminishing returns leads us to learn less from each additional question, but not here. How do I express that?

when you have only two data points.

I have zero data points, I'm comparing hypothetical situations in which I ask aliens one or more questions about their technology. (It seems Dawkins' scenario got inverted somewhere along the way, but I don't think that makes any difference.)

cringe. Please don't use "exponentially" to mean a lot when you have only two data points.

I heard a Calculus teacher do this with even less justification a few days ago.

EDIT: was this downvoted for irrelevancy, or some other reason?

To get either reaction to happen on a large scale, they must recognize and successfully separate isotopes, which is a significant technical accomplishment.

Is it possible the right isotopes might be lying around? Like here, but more concentrated and dispersed?

Alright, I will add a bit more context:

Academics are familiar with a related example: finishing our papers almost always takes us longer than we expected. We all know this and often say so. Why then do we continue to make the same error? Here again, the outside view does not inform judgments of particular cases.

From Timid Choices and Bold Forecasts A Cognitive Perspective on Risk Taking by Daniel Kahneman and Dan Lovallo

.