nhaliday + nonlinearity   55

Complexity no Bar to AI - Gwern.net
Critics of AI risk suggest diminishing returns to computing (formalized asymptotically) means AI will be weak; this argument relies on a large number of questionable premises and ignoring additional resources, constant factors, and nonlinear returns to small intelligence advantages, and is highly unlikely. (computer science, transhumanism, AI, R)
created: 1 June 2014; modified: 01 Feb 2018; status: finished; confidence: likely; importance: 10
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april 2018 by nhaliday
The Hanson-Yudkowsky AI-Foom Debate - Machine Intelligence Research Institute
How Deviant Recent AI Progress Lumpiness?: http://www.overcomingbias.com/2018/03/how-deviant-recent-ai-progress-lumpiness.html
I seem to disagree with most people working on artificial intelligence (AI) risk. While with them I expect rapid change once AI is powerful enough to replace most all human workers, I expect this change to be spread across the world, not concentrated in one main localized AI system. The efforts of AI risk folks to design AI systems whose values won’t drift might stop global AI value drift if there is just one main AI system. But doing so in a world of many AI systems at similar abilities levels requires strong global governance of AI systems, which is a tall order anytime soon. Their continued focus on preventing single system drift suggests that they expect a single main AI system.

The main reason that I understand to expect relatively local AI progress is if AI progress is unusually lumpy, i.e., arriving in unusually fewer larger packages rather than in the usual many smaller packages. If one AI team finds a big lump, it might jump way ahead of the other teams.

However, we have a vast literature on the lumpiness of research and innovation more generally, which clearly says that usually most of the value in innovation is found in many small innovations. We have also so far seen this in computer science (CS) and AI. Even if there have been historical examples where much value was found in particular big innovations, such as nuclear weapons or the origin of humans.

Apparently many people associated with AI risk, including the star machine learning (ML) researchers that they often idolize, find it intuitively plausible that AI and ML progress is exceptionally lumpy. Such researchers often say, “My project is ‘huge’, and will soon do it all!” A decade ago my ex-co-blogger Eliezer Yudkowsky and I argued here on this blog about our differing estimates of AI progress lumpiness. He recently offered Alpha Go Zero as evidence of AI lumpiness:

...

In this post, let me give another example (beyond two big lumps in a row) of what could change my mind. I offer a clear observable indicator, for which data should have available now: deviant citation lumpiness in recent ML research. One standard measure of research impact is citations; bigger lumpier developments gain more citations that smaller ones. And it turns out that the lumpiness of citations is remarkably constant across research fields! See this March 3 paper in Science:

I Still Don’t Get Foom: http://www.overcomingbias.com/2014/07/30855.html
All of which makes it look like I’m the one with the problem; everyone else gets it. Even so, I’m gonna try to explain my problem again, in the hope that someone can explain where I’m going wrong. Here goes.

“Intelligence” just means an ability to do mental/calculation tasks, averaged over many tasks. I’ve always found it plausible that machines will continue to do more kinds of mental tasks better, and eventually be better at pretty much all of them. But what I’ve found it hard to accept is a “local explosion.” This is where a single machine, built by a single project using only a tiny fraction of world resources, goes in a short time (e.g., weeks) from being so weak that it is usually beat by a single human with the usual tools, to so powerful that it easily takes over the entire world. Yes, smarter machines may greatly increase overall economic growth rates, and yes such growth may be uneven. But this degree of unevenness seems implausibly extreme. Let me explain.

If we count by economic value, humans now do most of the mental tasks worth doing. Evolution has given us a brain chock-full of useful well-honed modules. And the fact that most mental tasks require the use of many modules is enough to explain why some of us are smarter than others. (There’d be a common “g” factor in task performance even with independent module variation.) Our modules aren’t that different from those of other primates, but because ours are different enough to allow lots of cultural transmission of innovation, we’ve out-competed other primates handily.

We’ve had computers for over seventy years, and have slowly build up libraries of software modules for them. Like brains, computers do mental tasks by combining modules. An important mental task is software innovation: improving these modules, adding new ones, and finding new ways to combine them. Ideas for new modules are sometimes inspired by the modules we see in our brains. When an innovation team finds an improvement, they usually sell access to it, which gives them resources for new projects, and lets others take advantage of their innovation.

...

In Bostrom’s graph above the line for an initially small project and system has a much higher slope, which means that it becomes in a short time vastly better at software innovation. Better than the entire rest of the world put together. And my key question is: how could it plausibly do that? Since the rest of the world is already trying the best it can to usefully innovate, and to abstract to promote such innovation, what exactly gives one small project such a huge advantage to let it innovate so much faster?

...

In fact, most software innovation seems to be driven by hardware advances, instead of innovator creativity. Apparently, good ideas are available but must usually wait until hardware is cheap enough to support them.

Yes, sometimes architectural choices have wider impacts. But I was an artificial intelligence researcher for nine years, ending twenty years ago, and I never saw an architecture choice make a huge difference, relative to other reasonable architecture choices. For most big systems, overall architecture matters a lot less than getting lots of detail right. Researchers have long wandered the space of architectures, mostly rediscovering variations on what others found before.

Some hope that a small project could be much better at innovation because it specializes in that topic, and much better understands new theoretical insights into the basic nature of innovation or intelligence. But I don’t think those are actually topics where one can usefully specialize much, or where we’ll find much useful new theory. To be much better at learning, the project would instead have to be much better at hundreds of specific kinds of learning. Which is very hard to do in a small project.

What does Bostrom say? Alas, not much. He distinguishes several advantages of digital over human minds, but all software shares those advantages. Bostrom also distinguishes five paths: better software, brain emulation (i.e., ems), biological enhancement of humans, brain-computer interfaces, and better human organizations. He doesn’t think interfaces would work, and sees organizations and better biology as only playing supporting roles.

...

Similarly, while you might imagine someday standing in awe in front of a super intelligence that embodies all the power of a new age, superintelligence just isn’t the sort of thing that one project could invent. As “intelligence” is just the name we give to being better at many mental tasks by using many good mental modules, there’s no one place to improve it. So I can’t see a plausible way one project could increase its intelligence vastly faster than could the rest of the world.

Takeoff speeds: https://sideways-view.com/2018/02/24/takeoff-speeds/
Futurists have argued for years about whether the development of AGI will look more like a breakthrough within a small group (“fast takeoff”), or a continuous acceleration distributed across the broader economy or a large firm (“slow takeoff”).

I currently think a slow takeoff is significantly more likely. This post explains some of my reasoning and why I think it matters. Mostly the post lists arguments I often hear for a fast takeoff and explains why I don’t find them compelling.

(Note: this is not a post about whether an intelligence explosion will occur. That seems very likely to me. Quantitatively I expect it to go along these lines. So e.g. while I disagree with many of the claims and assumptions in Intelligence Explosion Microeconomics, I don’t disagree with the central thesis or with most of the arguments.)
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april 2018 by nhaliday
Open Thread, 11/26/2017 – Gene Expression
A few days ago there was a Twitter thing about top five books that have influenced you. It’s hard for me to name five, but I put three books down for three different reasons:

- Principles of Population Genetics, because it gives you a model for how to analyze and understand evolutionary processes. There are other books out there besides Principles of Population Genetics. But if you buy this book you don’t need to buy another (at SMBE this year I confused Andy Clark with Mike Lynch for a second when introducing myself. #awkward)
- The Fall of Rome. A lot of historical writing can be tendentious. I’ve also noticed an unfortunate tendency of historians dropping into contemporary arguments and pretty much lying through omission or elision to support their political side (it usually goes “actually, I’m a specialist in this topic and my side is 100% correct because of obscure-stuff where I’m shading the facts”). The Fall of Rome illustrates the solidity that an archaeological and materialist take can give the field. This sort of materialism isn’t the final word, but it needs to be the start of the conversation.
- From Dawn to Decadence: 1500 to the Present: 500 Years of Western Cultural Life. To know things is important in and of itself. My own personal experience is that the returns to knowing things in a particular domain or area do not exhibit a linear return. Rather, it exhibits a logistic curve. Initially, it’s hard to make sense of anything from the facts, but at some point comprehension and insight increase rapidly, until you reach the plateau of diminishing marginal returns.

If you haven’t, I recommend you subscribe to Patrick Wyman’s Tides of History podcast. I pretty much wait now for every new episode.
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november 2017 by nhaliday
Fitting a Structural Equation Model
seems rather unrigorous: nonlinear optimization, possibility of nonconvergence, doesn't even mention local vs. global optimality...
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november 2017 by nhaliday
Reconsidering the Heritability of Intelligence in Adulthood: Taking Assortative Mating and Cultural Transmission into Account
Heritability estimates of general intelligence in adulthood generally range from 75 to 85%, with all heritability due to additive genetic influences, while genetic dominance and shared environmental factors are absent, or too small to be detected. These estimates are derived from studies based on the classical twin design and are based on the assumption of random mating. Yet, considerable positive assortative mating has been reported for general intelligence. Unmodeled assortative mating may lead to biased estimates of the relative magnitude of genetic and environmental factors.

...

Under the preferred phenotypic assortment model, the variance of intelligence in adulthood was not only due to non-shared environmental (18%) and additive genetic factors (44%) but also to non-additive genetic factors (27%) and phenotypic assortment (11%).This non-additive nature of genetic influences on intelligence needs to be accommodated in future GWAS studies for intelligence.
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november 2017 by nhaliday
1 Genetics and Crime
The broader construct of antisocial behavior – which includes criminal offending, as well as aggression – also shows substantial genetic influence. In a meta-analysis combining effect sizes in 51 twin and adoption studies, Rhee and Waldman (2002) reported a heritability estimate of 41 per cent, with the remaining 59 per cent of variance being due to environmental factors. Interestingly, when comparing results for various definitions of antisocial behavior, only criminal offending appeared to be influenced by both additive genetic effects and non-additive genetic effects – possibly due to genetic dominance and epistatic interactions between genes – based on a pattern of results whereby, on average, identical (monozygotic) twin correlations are more than twice the value of fraternal (dizygotic) twin correlations, and also that biological parent–offspring correlations are less than fraternal twin correlations. Such non-additive genetic effects could arise if one or more high risk alleles act in a recessive fashion, or if certain alleles at one locus affect gene expression at other loci (epistasis).

One intriguing aspect of the literature on genetics and crime is that the strong and consistent genetic influence seen for property offending does not hold true for violent criminal convictions. None of the major adoption studies in Scandinavia or the United States found any elevated risk for violent convictions as a function of either biological or adoptive parent criminal offending, although one early twin study did find greater identical (monozygotic) than fraternal (dizygotic) concordance for violent convictions (see Cloninger and Gottesman, 1987). This pattern of twin, but not parent-offspring, similarity for violent criminal behavior suggests the possibility of non-additive genetic effects due to dominance or epistasis, which would result in increased resemblance for siblings (and twins), but not for parents and offspring. Thus, there may be genetic risk for violent crimes such as murder and rape, which may stem from rare recessive genes, or specific combinations of alleles that do not appear in studies of vertical transmission across generations.

A Swedish national twin study of criminal behavior and its violent, white-collar and property subtypes: https://www.cambridge.org/core/journals/psychological-medicine/article/a-swedish-national-twin-study-of-criminal-behavior-and-its-violent-white-collar-and-property-subtypes/0D9A88185ED0FD5525A5EBD5D2EBA117
For all criminal convictions, heritability was estimated at around 45% in both sexes, with the shared environment accounting for 18% of the variance in liability in females and 27% in males. The correlation of these risk factors across sexes was estimated at +0.63. In men, the magnitudes of genetic and environmental influence were similar in the three criminal conviction subtypes. However, for violent and white-collar convictions, nearly half and one-third of the genetic effects were respectively unique to that criminal subtype. About half of the familial environmental effects were unique to property convictions.

Heritability, Assortative Mating and Gender Differences in Violent Crime: Results from a Total Population Sample Using Twin, Adoption, and Sibling Models: https://link.springer.com/article/10.1007/s10519-011-9483-0
Using 36k twins, violent crime was moderately heritable (~ 55%) w/ 13% shared environment influence. Using 1.5 mil siblings, heritability was higher for males, & family environment higher for females. Moderate assortative mating for violent crime (r = .4).

The impact of neighbourhood deprivation on adolescent violent criminality and substance misuse: A longitudinal, quasi-experimental study of the total Swedish population: https://academic.oup.com/ije/article/42/4/1057/656274/The-impact-of-neighbourhood-deprivation-on
In the crude model, an increase of 1 SD in neighbourhood deprivation was associated with a 57% increase in the odds of being convicted of a violent crime (95% CI 52%–63%). The effect was greatly attenuated when adjustment was made for a number of observed confounders (OR 1.09, 95% CI 1.06–1.11). When we additionally adjusted for unobserved familial confounders, the effect was no longer present (OR 0.96, 95% CI 0.84–1.10). Similar results were observed for substance misuse. The results were not due to poor variability either between neighbourhoods or within families.

Childhood family income, adolescent violent criminality and substance misuse: quasi-experimental total population study: http://bjp.rcpsych.org/content/early/2014/08/14/bjp.bp.113.136200
https://www.economist.com/news/science-and-technology/21613303-disturbing-study-link-between-incomes-and-criminal-behaviour-have-and
What did surprise him was that when he looked at families which had started poor and got richer, the younger children—those born into relative affluence—were just as likely to misbehave when they were teenagers as their elder siblings had been. Family income was not, per se, the determining factor.

Indicators of domestic/intimate partner violence are structured by genetic and nonshared environmental influences: https://www.researchgate.net/publication/233737219_Indicators_of_domesticintimate_partner_violence_are_structured_by_genetic_and_nonshared_environmental_influences
Three indicators of IPV were measured and genetic factors accounted for 24% of the variance in hitting one's partner, 54% of the variance in injuring one's partner, and 51% of the variance in forcing sexual activity on one's partner. The shared environment explained none of the variance across all three indicators and the nonshared environment explained the remainder of the variance.
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october 2017 by nhaliday
Why are children in the same family so different from one another? - PubMed - NCBI
- Plomin et al

The article has three goals: (1) To describe quantitative genetic methods and research that lead to the conclusion that nonshared environment is responsible for most environmental variation relevant to psychological development, (2) to discuss specific nonshared environmental influences that have been studied to date, and (3) to consider relationships between nonshared environmental influences and behavioral differences between children in the same family. The reason for presenting this article in BBS is to draw attention to the far-reaching implications of finding that psychologically relevant environmental influences make children in a family different from, not similar to, one another.
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october 2017 by nhaliday
Social Animal House: The Economic and Academic Consequences of Fraternity Membership by Jack Mara, Lewis Davis, Stephen Schmidt :: SSRN
We exploit changes in the residential and social environment on campus to identify the economic and academic consequences of fraternity membership at a small Northeastern college. Our estimates suggest that these consequences are large, with fraternity membership lowering student GPA by approximately 0.25 points on the traditional four-point scale, but raising future income by approximately 36%, for those students whose decision about membership is affected by changes in the environment. These results suggest that fraternity membership causally produces large gains in social capital, which more than outweigh its negative effects on human capital for potential members. Alcohol-related behavior does not explain much of the effects of fraternity membership on either the human capital or social capital effects. These findings suggest that college administrators face significant trade-offs when crafting policies related to Greek life on campus.

- III. Methodology has details
- it's an instrumental variable method paper

Table 5: Fraternity Membership and Grades
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september 2017 by nhaliday
Culture, Ethnicity, and Diversity - American Economic Association
We investigate the empirical relationship between ethnicity and culture, defined as a vector of traits reflecting norms, values, and attitudes. Using survey data for 76 countries, we find that ethnic identity is a significant predictor of cultural values, yet that within-group variation in culture trumps between-group variation. Thus, in contrast to a commonly held view, ethnic and cultural diversity are unrelated. Although only a small portion of a country’s overall cultural heterogeneity occurs between groups, we find that various political economy outcomes (such as civil conflict and public goods provision) worsen when there is greater overlap between ethnicity and culture. (JEL D74, H41, J15, O15, O17, Z13)

definition of chi-squared index, etc., under:
II. Measuring Heterogeneity

Table 5—Incidence of Civil Conflict and Diversity
Table 6—Public Goods Provision and Diversity

https://twitter.com/GarettJones/status/924002043576115202
https://archive.is/oqMnC
https://archive.is/sBqqo
https://archive.is/1AcXn
χ2 diversity: raising the risk of civil war. Desmet, Ortuño-Ortín, Wacziarg, in the American Economic Review (1/N)

What predicts higher χ2 diversity? The authors tell us that, too. Here are all of the variables that have a correlation > 0.4: (7/N)

one of them is UK legal origin...

online appendix (with maps, Figures B1-3): http://www.anderson.ucla.edu/faculty_pages/romain.wacziarg/downloads/2017_culture_appendix.pdf
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september 2017 by nhaliday
The Determinants of Trust
Both individual experiences and community characteristics influence how much people trust each other. Using data drawn from US localities we find that the strongest factors that reduce trust are: i) a recent history of traumatic experiences, even though the passage of time reduces this effect fairly rapidly; ii) belonging to a group that historically felt discriminated against, such as minorities (black in particular) and, to a lesser extent, women; iii) being economically unsuccessful in terms of income and education; iv) living in a racially mixed community and/or in one with a high degree of income disparity. Religious beliefs and ethnic origins do not significantly affect trust. The latter result may be an indication that the American melting pot at least up to a point works, in terms of homogenizing attitudes of different cultures, even though racial cleavages leading to low trust are still quite high.

Understanding Trust: http://www.nber.org/papers/w13387
In this paper we resolve this puzzle by recognizing that trust has two components: a belief-based one and a preference based one. While the sender's behavior reflects both, we show that WVS-like measures capture mostly the belief-based component, while questions on past trusting behavior are better at capturing the preference component of trust.

MEASURING TRUST: http://scholar.harvard.edu/files/laibson/files/measuring_trust.pdf
We combine two experiments and a survey to measure trust and trustworthiness— two key components of social capital. Standard attitudinal survey questions about trust predict trustworthy behavior in our experiments much better than they predict trusting behavior. Trusting behavior in the experiments is predicted by past trusting behavior outside of the experiments. When individuals are closer socially, both trust and trustworthiness rise. Trustworthiness declines when partners are of different races or nationalities. High status individuals are able to elicit more trustworthiness in others.

What is Social Capital? The Determinants of Trust and Trustworthiness: http://www.nber.org/papers/w7216
Using a sample of Harvard undergraduates, we analyze trust and social capital in two experiments. Trusting behavior and trustworthiness rise with social connection; differences in race and nationality reduce the level of trustworthiness. Certain individuals appear to be persistently more trusting, but these people do not say they are more trusting in surveys. Survey questions about trust predict trustworthiness not trust. Only children are less trustworthy. People behave in a more trustworthy manner towards higher status individuals, and therefore status increases earnings in the experiment. As such, high status persons can be said to have more social capital.

Trust and Cheating: http://www.nber.org/papers/w18509
We find that: i) both parties to a trust exchange have implicit notions of what constitutes cheating even in a context without promises or messages; ii) these notions are not unique - the vast majority of senders would feel cheated by a negative return on their trust/investment, whereas a sizable minority defines cheating according to an equal split rule; iii) these implicit notions affect the behavior of both sides to the exchange in terms of whether to trust or cheat and to what extent. Finally, we show that individual's notions of what constitutes cheating can be traced back to two classes of values instilled by parents: cooperative and competitive. The first class of values tends to soften the notion while the other tightens it.

Nationalism and Ethnic-Based Trust: Evidence from an African Border Region: https://u.osu.edu/robinson.1012/files/2015/12/Robinson_NationalismTrust-1q3q9u1.pdf
These results offer microlevel evidence that a strong and salient national identity can diminish ethnic barriers to trust in diverse societies.

One Team, One Nation: Football, Ethnic Identity, and Conflict in Africa: http://conference.nber.org/confer//2017/SI2017/DEV/Durante_Depetris-Chauvin.pdf
Do collective experiences that prime sentiments of national unity reduce interethnic tensions and conflict? We examine this question by looking at the impact of national football teams’ victories in sub-Saharan Africa. Combining individual survey data with information on over 70 official matches played between 2000 and 2015, we find that individuals interviewed in the days after a victory of their country’s national team are less likely to report a strong sense of ethnic identity and more likely to trust people of other ethnicities than those interviewed just before. The effect is sizable and robust and is not explained by generic euphoria or optimism. Crucially, national victories do not only affect attitudes but also reduce violence. Indeed, using plausibly exogenous variation from close qualifications to the Africa Cup of Nations, we find that countries that (barely) qualified experience significantly less conflict in the following six months than countries that (barely) did not. Our findings indicate that, even where ethnic tensions have deep historical roots, patriotic shocks can reduce inter-ethnic tensions and have a tangible impact on conflict.

Why Does Ethnic Diversity Undermine Public Goods Provision?: http://www.columbia.edu/~mh2245/papers1/HHPW.pdf
We identify three families of mechanisms that link diversity to public goods provision—–what we term “preferences,” “technology,” and “strategy selection” mechanisms—–and run a series of experimental games that permit us to compare the explanatory power of distinct mechanisms within each of these three families. Results from games conducted with a random sample of 300 subjects from a slum neighborhood of Kampala, Uganda, suggest that successful public goods provision in homogenous ethnic communities can be attributed to a strategy selection mechanism: in similar settings, co-ethnics play cooperative equilibria, whereas non-co-ethnics do not. In addition, we find evidence for a technology mechanism: co-ethnics are more closely linked on social networks and thus plausibly better able to support cooperation through the threat of social sanction. We find no evidence for prominent preference mechanisms that emphasize the commonality of tastes within ethnic groups or a greater degree of altruism toward co-ethnics, and only weak evidence for technology mechanisms that focus on the impact of shared ethnicity on the productivity of teams.

does it generalize to first world?

Higher Intelligence Groups Have Higher Cooperation Rates in the Repeated Prisoner's Dilemma: https://ideas.repec.org/p/iza/izadps/dp8499.html
The initial cooperation rates are similar, it increases in the groups with higher intelligence to reach almost full cooperation, while declining in the groups with lower intelligence. The difference is produced by the cumulation of small but persistent differences in the response to past cooperation of the partner. In higher intelligence subjects, cooperation after the initial stages is immediate and becomes the default mode, defection instead requires more time. For lower intelligence groups this difference is absent. Cooperation of higher intelligence subjects is payoff sensitive, thus not automatic: in a treatment with lower continuation probability there is no difference between different intelligence groups

Why societies cooperate: https://voxeu.org/article/why-societies-cooperate
Three attributes are often suggested to generate cooperative behaviour – a good heart, good norms, and intelligence. This column reports the results of a laboratory experiment in which groups of players benefited from learning to cooperate. It finds overwhelming support for the idea that intelligence is the primary condition for a socially cohesive, cooperative society. Warm feelings towards others and good norms have only a small and transitory effect.

individual payoff, etc.:

Trust, Values and False Consensus: http://www.nber.org/papers/w18460
Trust beliefs are heterogeneous across individuals and, at the same time, persistent across generations. We investigate one mechanism yielding these dual patterns: false consensus. In the context of a trust game experiment, we show that individuals extrapolate from their own type when forming trust beliefs about the same pool of potential partners - i.e., more (less) trustworthy individuals form more optimistic (pessimistic) trust beliefs - and that this tendency continues to color trust beliefs after several rounds of game-play. Moreover, we show that one's own type/trustworthiness can be traced back to the values parents transmit to their children during their upbringing. In a second closely-related experiment, we show the economic impact of mis-calibrated trust beliefs stemming from false consensus. Miscalibrated beliefs lower participants' experimental trust game earnings by about 20 percent on average.

The Right Amount of Trust: http://www.nber.org/papers/w15344
We investigate the relationship between individual trust and individual economic performance. We find that individual income is hump-shaped in a measure of intensity of trust beliefs. Our interpretation is that highly trusting individuals tend to assume too much social risk and to be cheated more often, ultimately performing less well than those with a belief close to the mean trustworthiness of the population. On the other hand, individuals with overly pessimistic beliefs avoid being cheated, but give up profitable opportunities, therefore underperforming. The cost of either too much or too little trust is comparable to the income lost by forgoing college.

...

This framework allows us to show that income-maximizing trust typically exceeds the trust level of the average person as well as to estimate the distribution of income lost to trust mistakes. We find that although a majority of individuals has well calibrated beliefs, a non-trivial proportion of the population (10%) has trust beliefs sufficiently poorly calibrated to lower income by more than 13%.

Do Trust and … [more]
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august 2017 by nhaliday
Stages of Diversification
This paper studies the evolution of sectoral concentration in relation to the level of per capita income. We show that various measures of sectoral concentration follow a U-shaped pattern across a wide variety of data sources: countries first diversify, in the sense that economic activity is spread more equally across sectors, but there exists, relatively late in the development process, a point at which they start specializing again. We discuss this finding in light of existing theories of trade and growth, which generally predict a monotonic relationship between income and diversification. (JEL F43, F15, O40)

seems unhealthy to me (complacency)
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june 2017 by nhaliday
There Is No Such Thing as Decreasing Returns to Scale — Confessions of a Supply-Side Liberal
Besides pedagogical inertia—enforced to some extent by textbook publishers—I am not quite sure what motivates the devotion in so many economics curricula to U-shaped average cost curves. Let me make one guess: there is a desire to explain why firms are the size they are rather than larger or smaller. To my mind, such an explanation should proceed in one of three ways, appropriate to three different situations.
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may 2017 by nhaliday
Estimating the number of unseen variants in the human genome
To find all common variants (frequency at least 1%) the number of individuals that need to be sequenced is small (∼350) and does not differ much among the different populations; our data show that, subject to sequence accuracy, the 1000 Genomes Project is likely to find most of these common variants and a high proportion of the rarer ones (frequency between 0.1 and 1%). The data reveal a rule of diminishing returns: a small number of individuals (∼150) is sufficient to identify 80% of variants with a frequency of at least 0.1%, while a much larger number (> 3,000 individuals) is necessary to find all of those variants.

A map of human genome variation from population-scale sequencing: http://www.internationalgenome.org/sites/1000genomes.org/files/docs/nature09534.pdf

Scientists using data from the 1000 Genomes Project, which sequenced one thousand individuals from 26 human populations, found that "a typical [individual] genome differs from the reference human genome at 4.1 million to 5.0 million sites … affecting 20 million bases of sequence."[11] Nearly all (>99.9%) of these sites are small differences, either single nucleotide polymorphisms or brief insertion-deletions in the genetic sequence, but structural variations account for a greater number of base-pairs than the SNPs and indels.[11]

Human genetic variation: https://en.wikipedia.org/wiki/Human_genetic_variation

Singleton Variants Dominate the Genetic Architecture of Human Gene Expression: https://www.biorxiv.org/content/early/2017/12/15/219238
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may 2017 by nhaliday
Concentration and Growth | Dietrich Vollrath
Ultimately, and this is my impression, not some kind of established fact, concentration likely lowers innovative activity. Put it this way, the null hypothesis should probably be that concentration lowers innovation. An individual industry needs to provide evidence they are on the “right side of the curve” in the first AAH figure to believe concentration would be good for productivity growth in the long run.
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may 2017 by nhaliday
Missing heritability problem - Wikipedia
The "missing heritability" problem[1][2][3][4][5][6] can be defined as the fact that single genetic variations cannot account for much of the heritability of diseases, behaviors, and other phenotypes. This is a problem that has significant implications for medicine, since a person's susceptibility to disease may depend more on "the combined effect of all the genes in the background than on the disease genes in the foreground", or the role of genes may have been severely overestimated.

The 'missing heritability' problem was named as such in 2008. The Human Genome Project led to optimistic forecasts that the large genetic contributions to many traits and diseases (which were identified by quantitative genetics and behavioral genetics in particular) would soon be mapped and pinned down to specific genes and their genetic variants by methods such as candidate-gene studies which used small samples with limited genetic sequencing to focus on specific genes believed to be involved, examining the SNP kinds of variants. While many hits were found, they often failed to replicate in other studies.

The exponential fall in genome sequencing costs led to the use of GWAS studies which could simultaneously examine all candidate-genes in larger samples than the original finding, where the candidate-gene hits were found to almost always be false positives and only 2-6% replicate;[7][8][9][10][11][12] in the specific case of intelligence candidate-gene hits, only 1 candidate-gene hit replicated,[13] and of 15 neuroimaging hits, none did.[14] The editorial board of Behavior Genetics noted, in setting more stringent requirements for candidate-gene publications, that "the literature on candidate gene associations is full of reports that have not stood up to rigorous replication...it now seems likely that many of the published findings of the last decade are wrong or misleading and have not contributed to real advances in knowledge".[15] Other researchers have characterized the literature as having "yielded an infinitude of publications with very few consistent replications" and called for a phase out of candidate-gene studies in favor of polygenic scores.[16]

This led to a dilemma. Standard genetics methods have long estimated large heritabilities such as 80% for traits such as height or intelligence, yet none of the genes had been found despite sample sizes that, while small, should have been able to detect variants of reasonable effect size such as 1 inch or 5 IQ points. If genes have such strong cumulative effects - where were they? Several resolutions have been proposed, that the missing heritability is some combination of:

...

7. Genetic effects are indeed through common SNPs acting additively, but are highly polygenic: dispersed over hundreds or thousands of variants each of small effect like a fraction of an inch or a fifth of an IQ point and with low prior probability: unexpected enough that a candidate-gene study is unlikely to select the right SNP out of hundreds of thousands of known SNPs, and GWASes up to 2010, with n<20000, would be unable to find hits which reach genome-wide statistical-significance thresholds. Much larger GWAS sample sizes, often n>100k, would be required to find any hits at all, and would steadily increase after that.
This resolution to the missing heritability problem was supported by the introduction of Genome-wide complex trait analysis (GCTA) in 2010, which demonstrated that trait similarity could be predicted by the genetic similarity of unrelated strangers on common SNPs treated additively, and for many traits the SNP heritability was indeed a substantial fraction of the overall heritability. The GCTA results were further buttressed by findings that a small percent of trait variance could be predicted in GWASes without any genome-wide statistically-significant hits by a linear model including all SNPs regardless of p-value; if there were no SNP contribution, this would be unlikely, but it would be what one expected from SNPs whose effects were very imprecisely estimated by a too-small sample. Combined with the upper bound on maximum effect sizes set by the GWASes up to then, this strongly implied that the highly polygenic theory was correct. Examples of complex traits where increasingly large-scale GWASes have yielded the initial hits and then increasing numbers of hits as sample sizes increased from n<20k to n>100k or n>300k include height,[23] intelligence,[24] and schizophrenia.
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may 2017 by nhaliday
Educational Romanticism & Economic Development | pseudoerasmus
https://twitter.com/GarettJones/status/852339296358940672
deleeted

https://twitter.com/GarettJones/status/943238170312929280
https://archive.is/p5hRA

Did Nations that Boosted Education Grow Faster?: http://econlog.econlib.org/archives/2012/10/did_nations_tha.html
On average, no relationship. The trendline points down slightly, but for the time being let's just call it a draw. It's a well-known fact that countries that started the 1960's with high education levels grew faster (example), but this graph is about something different. This graph shows that countries that increased their education levels did not grow faster.

Where has all the education gone?: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1016.2704&rep=rep1&type=pdf

https://twitter.com/GarettJones/status/948052794681966593
https://archive.is/kjxqp

https://twitter.com/GarettJones/status/950952412503822337
https://archive.is/3YPic

https://twitter.com/pseudoerasmus/status/862961420065001472
http://hanushek.stanford.edu/publications/schooling-educational-achievement-and-latin-american-growth-puzzle

The Case Against Education: What's Taking So Long, Bryan Caplan: http://econlog.econlib.org/archives/2015/03/the_case_agains_9.html

The World Might Be Better Off Without College for Everyone: https://www.theatlantic.com/magazine/archive/2018/01/whats-college-good-for/546590/
Students don't seem to be getting much out of higher education.
- Bryan Caplan

College: Capital or Signal?: http://www.economicmanblog.com/2017/02/25/college-capital-or-signal/
After his review of the literature, Caplan concludes that roughly 80% of the earnings effect from college comes from signalling, with only 20% the result of skill building. Put this together with his earlier observations about the private returns to college education, along with its exploding cost, and Caplan thinks that the social returns are negative. The policy implications of this will come as very bitter medicine for friends of Bernie Sanders.

Doubting the Null Hypothesis: http://www.arnoldkling.com/blog/doubting-the-null-hypothesis/

Is higher education/college in the US more about skill-building or about signaling?: https://www.quora.com/Is-higher-education-college-in-the-US-more-about-skill-building-or-about-signaling
ballpark: 50% signaling, 30% selection, 20% addition to human capital
more signaling in art history, more human capital in engineering, more selection in philosophy

Econ Duel! Is Education Signaling or Skill Building?: http://marginalrevolution.com/marginalrevolution/2016/03/econ-duel-is-education-signaling-or-skill-building.html
Marginal Revolution University has a brand new feature, Econ Duel! Our first Econ Duel features Tyler and me debating the question, Is education more about signaling or skill building?

Against Tulip Subsidies: https://slatestarcodex.com/2015/06/06/against-tulip-subsidies/

https://www.overcomingbias.com/2018/01/read-the-case-against-education.html

https://nintil.com/2018/02/05/notes-on-the-case-against-education/

https://www.nationalreview.com/magazine/2018-02-19-0000/bryan-caplan-case-against-education-review

https://spottedtoad.wordpress.com/2018/02/12/the-case-against-education/
Most American public school kids are low-income; about half are non-white; most are fairly low skilled academically. For most American kids, the majority of the waking hours they spend not engaged with electronic media are at school; the majority of their in-person relationships are at school; the most important relationships they have with an adult who is not their parent is with their teacher. For their parents, the most important in-person source of community is also their kids’ school. Young people need adult mirrors, models, mentors, and in an earlier era these might have been provided by extended families, but in our own era this all falls upon schools.

Caplan gestures towards work and earlier labor force participation as alternatives to school for many if not all kids. And I empathize: the years that I would point to as making me who I am were ones where I was working, not studying. But they were years spent working in schools, as a teacher or assistant. If schools did not exist, is there an alternative that we genuinely believe would arise to draw young people into the life of their community?

...

It is not an accident that the state that spends the least on education is Utah, where the LDS church can take up some of the slack for schools, while next door Wyoming spends almost the most of any state at $16,000 per student. Education is now the one surviving binding principle of the society as a whole, the one black box everyone will agree to, and so while you can press for less subsidization of education by government, and for privatization of costs, as Caplan does, there’s really nothing people can substitute for it. This is partially about signaling, sure, but it’s also because outside of schools and a few religious enclaves our society is but a darkling plain beset by winds.

This doesn’t mean that we should leave Caplan’s critique on the shelf. Much of education is focused on an insane, zero-sum race for finite rewards. Much of schooling does push kids, parents, schools, and school systems towards a solution ad absurdum, where anything less than 100 percent of kids headed to a doctorate and the big coding job in the sky is a sign of failure of everyone concerned.

But let’s approach this with an eye towards the limits of the possible and the reality of diminishing returns.

https://westhunt.wordpress.com/2018/01/27/poison-ivy-halls/
https://westhunt.wordpress.com/2018/01/27/poison-ivy-halls/#comment-101293
The real reason the left would support Moander: the usual reason. because he’s an enemy.

https://westhunt.wordpress.com/2018/02/01/bright-college-days-part-i/
I have a problem in thinking about education, since my preferences and personal educational experience are atypical, so I can’t just gut it out. On the other hand, knowing that puts me ahead of a lot of people that seem convinced that all real people, including all Arab cabdrivers, think and feel just as they do.

One important fact, relevant to this review. I don’t like Caplan. I think he doesn’t understand – can’t understand – human nature, and although that sometimes confers a different and interesting perspective, it’s not a royal road to truth. Nor would I want to share a foxhole with him: I don’t trust him. So if I say that I agree with some parts of this book, you should believe me.

...

Caplan doesn’t talk about possible ways of improving knowledge acquisition and retention. Maybe he thinks that’s impossible, and he may be right, at least within a conventional universe of possibilities. That’s a bit outside of his thesis, anyhow. Me it interests.

He dismisses objections from educational psychologists who claim that studying a subject improves you in subtle ways even after you forget all of it. I too find that hard to believe. On the other hand, it looks to me as if poorly-digested fragments of information picked up in college have some effect on public policy later in life: it is no coincidence that most prominent people in public life (at a given moment) share a lot of the same ideas. People are vaguely remembering the same crap from the same sources, or related sources. It’s correlated crap, which has a much stronger effect than random crap.

These widespread new ideas are usually wrong. They come from somewhere – in part, from higher education. Along this line, Caplan thinks that college has only a weak ideological effect on students. I don’t believe he is correct. In part, this is because most people use a shifting standard: what’s liberal or conservative gets redefined over time. At any given time a population is roughly half left and half right – but the content of those labels changes a lot. There’s a shift.

https://westhunt.wordpress.com/2018/02/01/bright-college-days-part-i/#comment-101492
I put it this way, a while ago: “When you think about it, falsehoods, stupid crap, make the best group identifiers, because anyone might agree with you when you’re obviously right. Signing up to clear nonsense is a better test of group loyalty. A true friend is with you when you’re wrong. Ideally, not just wrong, but barking mad, rolling around in your own vomit wrong.”
--
You just explained the Credo quia absurdum doctrine. I always wondered if it was nonsense. It is not.
--
Someone on twitter caught it first – got all the way to “sliding down the razor blade of life”. Which I explained is now called “transitioning”

What Catholics believe: https://theweek.com/articles/781925/what-catholics-believe
We believe all of these things, fantastical as they may sound, and we believe them for what we consider good reasons, well attested by history, consistent with the most exacting standards of logic. We will profess them in this place of wrath and tears until the extraordinary event referenced above, for which men and women have hoped and prayed for nearly 2,000 years, comes to pass.

https://westhunt.wordpress.com/2018/02/05/bright-college-days-part-ii/
According to Caplan, employers are looking for conformity, conscientiousness, and intelligence. They use completion of high school, or completion of college as a sign of conformity and conscientiousness. College certainly looks as if it’s mostly signaling, and it’s hugely expensive signaling, in terms of college costs and foregone earnings.

But inserting conformity into the merit function is tricky: things become important signals… because they’re important signals. Otherwise useful actions are contraindicated because they’re “not done”. For example, test scores convey useful information. They could help show that an applicant is smart even though he attended a mediocre school – the same role they play in college admissions. But employers seldom request test scores, and although applicants may provide them, few do. Caplan says ” The word on the street… [more]
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april 2017 by nhaliday
Fertility trends by social status
The study reveals that as fertility declines, there is a general shift from a positive to a negative or neutral status-fertility relation. Those with high income/wealth or high occupation/social class switch from having relatively many to fewer or the same number of children as others. Education, however, depresses fertility for as long as this relation is observed (from early in the 20th century).

- good survey with trends for different regions, including UK+North America
- Figure 4: quadratic for UK+NA, crossing zero around 1800 or so and quickly leveling off
http://imgur.com/a/xjwO1
- also Figure 5: fertility differential by total TFR (quadratic trend), so worst dysgenics in middle of demographic transition
- dataset: http://www.demographic-research.org/volumes/vol18/5/files/StatusFertilityDataset.xls

This article discusses how fertility relates to social status with the use of a new dataset, several times larger than the ones used so far. The status-fertility relation is investigated over several centuries, across world regions and by the type of status-measure. The study reveals that as fertility declines, there is a general shift from a positive to a negative or neutral status-fertility relation. Those with high income/wealth or high occupation/social class switch from having relatively many to fewer or the same number of children as others. Education, however, depresses fertility for as long as this relation is observed (from early in the 20th century).
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march 2017 by nhaliday
Was the Wealth of Nations Determined in 1000 BC?
Our most interesting, strong, and robust results are for the association of 1500 AD technology with per capita income and technology adoption today. We also find robust and significant technological persistence from 1000 BC to 0 AD, and from 0 AD to 1500 AD.

migration-adjusted ancestry predicts current economic growth and technology adoption today

https://economix.blogs.nytimes.com/2010/08/02/was-todays-poverty-determined-in-1000-b-c/

Putterman-Weil:
Post-1500 Population Flows and the Long Run Determinants of Economic Growth and Inequality: http://www.nber.org/papers/w14448
Persistence of Fortune: Accounting for Population Movements, There Was No Post-Columbian Reversal: http://sci-hub.tw/10.1257/mac.6.3.1
Extended State History Index: https://sites.google.com/site/econolaols/extended-state-history-index
Description:
The data set extends and replaces previous versions of the State Antiquity Index (originally created by Bockstette, Chanda and Putterman, 2002). The updated data extends the previous Statehist data into the years before 1 CE, to the first states in Mesopotamia (in the fourth millennium BCE), along with filling in the years 1951 – 2000 CE that were left out of past versions of the Statehist data.
The construction of the index follows the principles developed by Bockstette et al (2002). First, the duration of state existence is established for each territory defined by modern-day country borders. Second, this duration is divided into 50-year periods. For each half-century from the first period (state emergence) onwards, the authors assign scores to reflect three dimensions of state presence, based on the following questions: 1) Is there a government above the tribal level? 2) Is this government foreign or locally based? 3) How much of the territory of the modern country was ruled by this government?

Creators: Oana Borcan, Ola Olsson & Louis Putterman

State History and Economic Development: Evidence from Six Millennia∗: https://drive.google.com/file/d/1cifUljlPpoURL7VPOQRGF5q9H6zgVFXe/view
The presence of a state is one of the most reliable historical predictors of social and economic development. In this article, we complete the coding of an extant indicator of state presence from 3500 BCE forward for almost all but the smallest countries of the world today. We outline a theoretical framework where accumulated state experience increases aggregate productivity in individual countries but where newer or relatively inexperienced states can reach a higher productivity maximum by learning from the experience of older states. The predicted pattern of comparative development is tested in an empirical analysis where we introduce our extended state history variable. Our key finding is that the current level of economic development across countries has a hump-shaped relationship with accumulated state history.

nonlinearity confirmed in this other paper:
State and Development: A Historical Study of Europe from 0 AD to 2000 AD: https://ideas.repec.org/p/hic/wpaper/219.html
After addressing conceptual and practical concerns on its construction, we present a measure of the mean duration of state rule that is aimed at resolving some of these issues. We then present our findings on the relationship between our measure and local development, drawing from observations in Europe spanning from 0 AD to 2000 AD. We find that during this period, the mean duration of state rule and the local income level have a nonlinear, inverse U-shaped relationship, controlling for a set of historical, geographic and socioeconomic factors. Regions that have historically experienced short or long duration of state rule on average lag behind in their local wealth today, while those that have experienced medium-duration state rule on average fare better.

Figure 1 shows all borders that existed during this period
Figure 4 shows quadratic fit

I wonder if U-shape is due to Ibn Kaldun-Turchin style effect on asabiya? They suggest sunk costs and ossified institutions.
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march 2017 by nhaliday
Social Epistasis Amplifies the Fitness Costs of Deleterious Mutations, Engendering Rapid Fitness Decline Among Modernized Populations | SpringerLink
- Michael A. Woodley

We argue that in social species, interorganismal gene-gene interactions, which in previous literatures have been termed social epistasis, allow genomes carrying deleterious mutations to reduce via group-level pleiotropy the fitness of others, including noncarriers. This fitness reduction occurs by way of degradation of group-level processes that optimize the reproductive ecology of a population for intergroup competition through, among other mechanisms, suppression of free-riding.

--

Fitness indicators theory (Houle 2000; Miller 2000) predicts that the behavioral and physiological condition of prospective partners strongly influences female mate choice in particular, as these constitute honest indicators of underlying genetic quality. Furthermore, as deleterious mutations are pleiotropic (i.e., they can influence the development of multiple traits simultaneously), they are a source of genetic correlation among diverse behavioral and physiological domains, yielding a latent general fitness factor( f ). This optimizes the efficiency of sexual selection, as selection for quality with respect to one domain will increase the probability of selection for quality “across the board” (Houle 2000; Miller 2000). If purifying selection is primarily cryptic—working by virtue of those lower in f simply being less successful in competition for mates and therefore producing fewer offspring relative to those higher in the factor—then considerably less reproductive failure is needed to solve the mutation load paradox (19% instead of 88% based on simulations in Leseque et al. 2012).

...

Theoretical work involving humans suggests a loss of intrinsic fitness of around 1% per generation in the populations of modernized countries (Lynch 2016; Muller 1950). Thus, these might yet be undergoing mutational meltdown, albeit very gradually (i.e., over the course of centuries)

...

An interesting observation is that the fitness of the populations of modernized nations does appear to be rapidly decreasing—although not in a manner consonant with the direct action of deleterious mutations on the fitness of individuals (as per the mutation load paradox).

...

Increased education has furthermore encouraged individuals to trade fertility against opportunities to enhance their social status and earning power, with the largest fitness losses occurring among those with high status who potentially carry fewer deleterious mutations (i.e., by virtue of possessing higher levels of traits that exhibit some sensitivity to mutation load, such as general intelligence; Spain et al. 2015; Woodley of Menie et al. 2016a). Hitherto not considered is the possibility that the demographic transition represents a potential change in the fitness characteristics of the group-level extended phenotype of modernized populations, indicating that there might exist pathways through which deleterious mutations that accumulate due to ecological mildness could pathologically alter fertility tradeoffs in ways that might account for the maladaptive aspects of the fertility transition (e.g., subreplacement fertility; Basten, Lutz and Scherbov, 2013).

...

Cooperation, though offering significant fitness benefits to individual organisms and groups, involves some costs for cooperators in order to realize mutual gains for all parties. Free riders are individuals that benefit from cooperation without suffering any of the costs needed to sustain it. Hence, free riders enjoy a fitness advantage relative to cooperators via the former’s parasitism on the latter.

...

The balance of selection can alternate between the different levels depending on the sorts of selective challenges that a population encounters. For example, group selection may operate on human populations during times of intergroup conflict (i.e., warfare), whereas during times of peace, selection may tend to favor the fitness of individuals instead (Woodley and Figueredo 2013; Wilson 2002). A major factor that seems to permit group-level selection to be viable under certain ecological regimes is the existence of free-rider controls, i.e., features of the group’s social ecology that curb the reproductive fitness of the carriers of “selfish” genetic variants (MacDonald 1994; Wilson 2002).

...

High-status individuals participate in the generation and vertical cultural transmission of free-rider controls—these take the form of religious and ideological systems which make a virtue out of behaviors that overtly benefit the group, and a vice out of those that only favor individual-level fitness, via the promotion of ethnocentrism, martyrdom, and displays of commitment (MacDonald 1994, 2009, 2010; Wilson 2002). Humans are furthermore equipped with specialized mental adaptations for coordinating as part of a group, such as effortful control—the ability to override implicit behavioral drives via the use of explicit processing systems, which allow them to regulate their behavior based on what is optimal for the group (MacDonald 2008). The interaction between individuals of different degrees of status, i.e., those that generate and maintain cultural norms and those who are merely subject to them, therefore constitutes a form of social epistasis, as the complex patterns of interactions among genomes that characterize human culture have the effect of regulating both individual- and group-level (via the curbing of free-riding) fitness (MacDonald 2009, 2010).

Mutations that push the behavior of high-status individuals away from the promotion of group-selected norms may promote a breakdown of or otherwise alter these social epistatic interactions, causing dysregulation of the group’s reproductive ecology. Behavioral changes are furthermore a highly likely consequence of mutation accumulation, as “behavior” (construed broadly) is a large potential target for new mutations (Miller 2000; Lynch 2016) 1 owing to the fact that approximately 84% of all genes in the human genome are involved in some aspect of brain development and/or maintenance (Hawrylycz et al. 2012).

Consistent with the theorized role of group-level (cultural) regulatory processes in the maintenance of fitness optima, positive correlations exist between religiosity (a major freerider control; MacDonald 1994; Wilson 2002) and fertility, both at the individual differences and cross-cultural levels (Meisenberg 2011). Religiosity has declined in modernized nations—a process that has gone hand-in-hand with the rise of a values system called postmaterialism (Inglehart 1977), which is characterized by the proliferation of individualistic, secular, and antihierarchical values (Welzel 2013). The holding of these values is negatively associated with fertility, both at the individual level (when measured as political liberalism; Goldstone et al. 2011) and across time and cultures (Inglehart and Appel 1989). The rise of postmaterialist values is also associated with increasingly delayed onset of reproduction (Klien 1990) which directly increases the (population) mutation load.

Pathological Altruism

Some of the values embodied in postmaterialism have been linked to the pathological altruism phenomenon, i.e., forms of altruism that damage the intended recipients or givers of largesse (Oakley et al. 2012; Oakley 2013). Virtues associated with altruism such as kindness, fidelity, magnanimity, and heroism, along with quasi-moral traits associated with personality and mental health, may be under sexual selection and might therefore be sensitive, through the f factor, to the deleterious effects of accumulating mutations (Miller 2007).

...

Another form of pathologically altruistic behavior that Oakley (2013) documents is self-righteousness, which may be increasing, consistent with secular trend data indicating elevated levels of self-regarding behavior among Western populations (sometimes called the narcissism epidemic; Twenge and Campbell 2009). This sort of behavior constitutes a key component of the clever silly phenomenon in which the embrace of counterfactual beliefs is used to leverage social status via virtue signaling (e.g., the conflation of moral equality among individuals, sexes, and populations with biological equality) (Dutton and van der Linden 2015; Charlton 2009; Woodley 2010). There may be a greater number of influential persons inclined to disseminate such beliefs, in that the prevalence of phenotypes disposed toward egoistic behaviors may have increased in Western populations (per Twenge and coworkers’ research), and because egoists, specifically Machiavellians and narcissists, appear advantaged in the acquisition of elite societal stations (Spurk et al. 2015).

[Do Bad Guys Get Ahead or Fall Behind? Relationships of the Dark Triad of Personality With Objective and Subjective Career Success: http://sci-hub.tw/http://journals.sagepub.com/doi/abs/10.1177/1948550615609735

After controlling for other relevant variables (i.e., gender, age, job tenure, organization size, education, and work hours), narcissism was positively related to salary, Machiavellianism was positively related to leadership position and career satisfaction, and psychopathy was negatively related to all analyzed outcomes.]

...

By altering cultural norms, elite egoists may encourage the efflorescence of selfish behaviors against which some older and once highly influential cultural systems acted. For example, Christianity in various forms strongly promoted personal sacrifice for the good of groups and proscribed egoistic behaviors (Rubin 2015), but has declined significantly in terms of cultural power following modernization (Inglehart 1977). Thus, it is possible that a feedback loop exists wherein deleterious mutation accumulation raises population levels of egoism, either directly or indirectly, via the breakdown of developmental constraints on personality canalization; the resultantly greater number of egoists are then able to exploit relevant personality traits to attain positions of sociocultural influence; and through these … [more]
study  speculation  models  biodet  bio  sapiens  evolution  genetic-load  paternal-age  the-monster  slippery-slope  society  social-structure  free-riding  coordination  EGT  dynamical  🌞  fertility  dysgenics  eh  self-control  obesity  altruism  mutation  multi  twitter  social  commentary  perturbation  gnon  new-religion  science-anxiety  population-genetics  biophysical-econ  hmm  discipline  autism  scitariat  clown-world  epidemiology  malaise  sociology  demographic-transition  blowhards  model-organism  nonlinearity  civilization  expression-survival  universalism-particularism  order-disorder  trends  deep-materialism  values  ideology  domestication  cohesion  christopher-lasch  scale  patho-altruism  social-capital  behavioral-gen  madisonian  chart  nihil  aristos  piracy  theos  cultural-dynamics  roots  zeitgeist  rot  the-bones  counter-revolution  pdf  modernity  microfoundations  video  presentation  religion  christianity  health  longevity  ethnocentrism  genetic-correlation  👽  instinct 
march 2017 by nhaliday
List of games in game theory - Wikipedia
https://twitter.com/BretWeinstein/status/961503023854833665
https://archive.is/qLsD4
The most important patterns:

1. Prisoner's Dilemma
2. Race to the Bottom
3. Free Rider Problem / Tragedy of the Commons / Collective Action
4. Zero Sum vs. Non-Zero Sum
5. Externalities / Principal Agent
6. Diminishing Returns
7. Evolutionarily Stable Strategy / Nash Equilibrium
concept  economics  micro  models  examples  list  game-theory  GT-101  wiki  reference  cooperate-defect  multi  twitter  social  discussion  backup  journos-pundits  coordination  competition  free-riding  zero-positive-sum  externalities  rent-seeking  marginal  convexity-curvature  nonlinearity  equilibrium  top-n  metabuch  conceptual-vocab  alignment  contracts 
february 2017 by nhaliday
Information Processing: The joy of Turkheimer
In the talk Turkheimer gives the following definition of social science, which emphasizes why it is hard:

Social science is the attempt to explain the causes of complex human behavior when:
- There are a large number of potential causes.
- The potential causes are non-independent.
- Randomized experimentation is not possible.
hsu  scitariat  genetics  genomics  causation  hypothesis-testing  social-science  nonlinearity  iidness  correlation  links  slides  presentation  audio  things  lens  metabuch  thinking  GxE  commentary 
february 2017 by nhaliday
Information Processing: Epistasis vs additivity
On epistasis: why it is unimportant in polygenic directional selection: http://rstb.royalsocietypublishing.org/content/365/1544/1241.short
- James F. Crow

The Evolution of Multilocus Systems Under Weak Selection: http://www.genetics.org/content/genetics/134/2/627.full.pdf
- Thomas Nagylaki

Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000008
The relative proportion of additive and non-additive variation for complex traits is important in evolutionary biology, medicine, and agriculture. We address a long-standing controversy and paradox about the contribution of non-additive genetic variation, namely that knowledge about biological pathways and gene networks imply that epistasis is important. Yet empirical data across a range of traits and species imply that most genetic variance is additive. We evaluate the evidence from empirical studies of genetic variance components and find that additive variance typically accounts for over half, and often close to 100%, of the total genetic variance. We present new theoretical results, based upon the distribution of allele frequencies under neutral and other population genetic models, that show why this is the case even if there are non-additive effects at the level of gene action. We conclude that interactions at the level of genes are not likely to generate much interaction at the level of variance.
hsu  scitariat  commentary  links  study  list  evolution  population-genetics  genetics  methodology  linearity  nonlinearity  comparison  scaling-up  nibble  lens  bounded-cognition  ideas  bio  occam  parsimony  🌞  summary  quotes  multi  org:nat  QTL  stylized-facts  article  explanans  sapiens  biodet  selection  variance-components  metabuch  thinking  models  data  deep-materialism  chart  behavioral-gen  evidence-based  empirical  mutation  spearhead  model-organism  bioinformatics  linear-models  math  magnitude  limits  physics  interdisciplinary  stat-mech 
february 2017 by nhaliday
Information Processing: Success, Ability, and all that
Better to be Lucky than Good?: http://infoproc.blogspot.com/2018/03/better-to-be-lucky-than-good.html
The arXiv paper below looks at stochastic dynamical models that can transform initial (e.g., Gaussian) talent distributions into power law outcomes (e.g., observed wealth distributions in modern societies). While the models themselves may not be entirely realistic, they illustrate the potentially large role of luck relative to ability in real life outcomes.

...

Of course, it might be the case that better measurements would uncover a power law distribution of individual talents. But it's far more plausible to me that random fluctuations + nonlinear amplifications transform, over time, normally distributed talents into power law outcomes.
hsu  scitariat  meta:prediction  success  variance-components  correlation  signal-noise  street-fighting  methodology  multi  technocracy  quality  human-capital  money  wealth  compensation  distribution  inequality  winner-take-all  coming-apart  power-law  pareto  random  models  nonlinearity  roots  explanans 
january 2017 by nhaliday
The infinitesimal model | bioRxiv
Our focus here is on the infinitesimal model. In this model, one or several quantitative traits are described as the sum of a genetic and a non-genetic component, the first being distributed as a normal random variable centred at the average of the parental genetic components, and with a variance independent of the parental traits. We first review the long history of the infinitesimal model in quantitative genetics. Then we provide a definition of the model at the phenotypic level in terms of individual trait values and relationships between individuals, but including different evolutionary processes: genetic drift, recombination, selection, mutation, population structure, ... We give a range of examples of its application to evolutionary questions related to stabilising selection, assortative mating, effective population size and response to selection, habitat preference and speciation. We provide a mathematical justification of the model as the limit as the number M of underlying loci tends to infinity of a model with Mendelian inheritance, mutation and environmental noise, when the genetic component of the trait is purely additive. We also show how the model generalises to include epistatic effects. In each case, by conditioning on the pedigree relating individuals in the population, we incorporate arbitrary selection and population structure. We suppose that we can observe the pedigree up to the present generation, together with all the ancestral traits, and we show, in particular, that the genetic components of the individual trait values in the current generation are indeed normally distributed with a variance independent of ancestral traits, up to an error of order M^{-1/2}. Simulations suggest that in particular cases the convergence may be as fast as 1/M.

published version:
The infinitesimal model: Definition, derivation, and implications: https://sci-hub.tw/10.1016/j.tpb.2017.06.001

Commentary: Fisher’s infinitesimal model: A story for the ages: http://www.sciencedirect.com/science/article/pii/S0040580917301508?via%3Dihub
This commentary distinguishes three nested approximations, referred to as “infinitesimal genetics,” “Gaussian descendants” and “Gaussian population,” each plausibly called “the infinitesimal model.” The first and most basic is Fisher’s “infinitesimal” approximation of the underlying genetics – namely, many loci, each making a small contribution to the total variance. As Barton et al. (2017) show, in the limit as the number of loci increases (with enough additivity), the distribution of genotypic values for descendants approaches a multivariate Gaussian, whose variance–covariance structure depends only on the relatedness, not the phenotypes, of the parents (or whether their population experiences selection or other processes such as mutation and migration). Barton et al. (2017) call this rigorously defensible “Gaussian descendants” approximation “the infinitesimal model.” However, it is widely assumed that Fisher’s genetic assumptions yield another Gaussian approximation, in which the distribution of breeding values in a population follows a Gaussian — even if the population is subject to non-Gaussian selection. This third “Gaussian population” approximation, is also described as the “infinitesimal model.” Unlike the “Gaussian descendants” approximation, this third approximation cannot be rigorously justified, except in a weak-selection limit, even for a purely additive model. Nevertheless, it underlies the two most widely used descriptions of selection-induced changes in trait means and genetic variances, the “breeder’s equation” and the “Bulmer effect.” Future generations may understand why the “infinitesimal model” provides such useful approximations in the face of epistasis, linkage, linkage disequilibrium and strong selection.
study  exposition  bio  evolution  population-genetics  genetics  methodology  QTL  preprint  models  unit  len:long  nibble  linearity  nonlinearity  concentration-of-measure  limits  applications  🌞  biodet  oscillation  fisher  perturbation  stylized-facts  chart  ideas  article  pop-structure  multi  pdf  piracy  intricacy  map-territory  kinship  distribution  simulation  ground-up  linear-models  applicability-prereqs  bioinformatics 
january 2017 by nhaliday
The Genetic Architecture of Quantitative Traits Cannot Be Inferred from Variance Component Analysis
Classical quantitative genetic analyses estimate additive and non-additive genetic and environmental components of variance from phenotypes of related individuals without knowing the identities of quantitative trait loci (QTLs). Many studies have found a large proportion of quantitative trait variation can be attributed to the additive genetic variance (VA), providing the basis for claims that non-additive gene actions are unimportant. In this study, we show that arbitrarily defined parameterizations of genetic effects seemingly consistent with non-additive gene actions can also capture the majority of genetic variation. This reveals a logical flaw in using the relative magnitudes of variance components to indicate the relative importance of additive and non-additive gene actions. We discuss the implications and propose that variance component analyses should not be used to infer the genetic architecture of quantitative traits.
study  genetics  QTL  methodology  variance-components  critique  gotchas  nonlinearity  regularizer  🌞  biodet  pro-rata  roots  null-result  bioinformatics 
december 2016 by nhaliday
Epistasis and Entropy
Epistasis is a key concept in the theory of adaptation. Indicators of epistasis are of interest for large systems where systematic fitness measurements may not be possible. Some recent approaches depend on information theory. We show that considering shared entropy for pairs of loci can be misleading. The reason is that shared entropy does not imply epistasis for the pair. This observation holds true also in the absence of higher order epistasis. We discuss a method for reducing the number of false positives. However, our main conclusion is that entropy-based approaches have serious limitations in this context.
study  methodology  genetics  population-genetics  information-theory  nonlinearity  bio  gotchas  genomics  QTL  entropy-like 
december 2016 by nhaliday
Books, 2016 | West Hunter
1. The Peloponnesian War
2 The Empire of the Steppes
3. The Columbian Exchange
4. Breaking the Maya Code
5. War Before Civilization
6. The Discourses (Machiavelli)
7. Introduction to Algorithms
8. Rare Earth
9. The Wizard War
10. Night comes to the Cretaceous
11. Microbe Hunters
12. The Youngest Science
13. Plagues and Peoples
14. Project Orion
15. Extraordinary Popular Delusions and the Madness of Crowds
16. Godstalk, P. C. Hodgell
17. Footfall, Larry Niven and Jerry Pournelle
18. On Stranger Tides, Tim Powers
19. His Share of Glory, Cyril Kornbluth
20. Herodotus
21. The Secret History, Procopius

https://westhunt.wordpress.com/2016/12/04/books-2016/#comment-85575
Mukherjee is a moron. Next question?

He’s suggested that gene interactions are real important in IQ [epistatic rather than additive effects] but he is incorrect. If new to the field, it could take as much as an afternoon to find that out.
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december 2016 by nhaliday

bundles : abstractpatterns

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