nhaliday + plots   59

An adaptability limit to climate change due to heat stress
Despite the uncertainty in future climate-change impacts, it is often assumed that humans would be able to adapt to any possible warming. Here we argue that heat stress imposes a robust upper limit to such adaptation. Peak heat stress, quantified by the wet-bulb temperature TW, is surprisingly similar across diverse climates today. TW never exceeds 31 °C. Any exceedence of 35 °C for extended periods should induce hyperthermia in humans and other mammals, as dissipation of metabolic heat becomes impossible. While this never happens now, it would begin to occur with global-mean warming of about 7 °C, calling the habitability of some regions into question. With 11–12 °C warming, such regions would spread to encompass the majority of the human population as currently distributed. Eventual warmings of 12 °C are possible from fossil fuel burning. One implication is that recent estimates of the costs of unmitigated climate change are too low unless the range of possible warming can somehow be narrowed. Heat stress also may help explain trends in the mammalian fossil record.

Trajectories of the Earth System in the Anthropocene: http://www.pnas.org/content/early/2018/07/31/1810141115
We explore the risk that self-reinforcing feedbacks could push the Earth System toward a planetary threshold that, if crossed, could prevent stabilization of the climate at intermediate temperature rises and cause continued warming on a “Hothouse Earth” pathway even as human emissions are reduced. Crossing the threshold would lead to a much higher global average temperature than any interglacial in the past 1.2 million years and to sea levels significantly higher than at any time in the Holocene. We examine the evidence that such a threshold might exist and where it might be.
study  org:nat  environment  climate-change  humanity  existence  risk  futurism  estimate  physics  thermo  prediction  temperature  nature  walls  civilization  flexibility  rigidity  embodied  multi  manifolds  plots  equilibrium  phase-transition  oscillation  comparison  complex-systems  earth 
august 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
How do you measure the mass of a star? (Beginner) - Curious About Astronomy? Ask an Astronomer
Measuring the mass of stars in binary systems is easy. Binary systems are sets of two or more stars in orbit about each other. By measuring the size of the orbit, the stars' orbital speeds, and their orbital periods, we can determine exactly what the masses of the stars are. We can take that knowledge and then apply it to similar stars not in multiple systems.

We also can easily measure the luminosity and temperature of any star. A plot of luminocity versus temperature for a set of stars is called a Hertsprung-Russel (H-R) diagram, and it turns out that most stars lie along a thin band in this diagram known as the main Sequence. Stars arrange themselves by mass on the Main Sequence, with massive stars being hotter and brighter than their small-mass bretheren. If a star falls on the Main Sequence, we therefore immediately know its mass.

In addition to these methods, we also have an excellent understanding of how stars work. Our models of stellar structure are excellent predictors of the properties and evolution of stars. As it turns out, the mass of a star determines its life history from day 1, for all times thereafter, not only when the star is on the Main Sequence. So actually, the position of a star on the H-R diagram is a good indicator of its mass, regardless of whether it's on the Main Sequence or not.
nibble  q-n-a  org:junk  org:edu  popsci  space  physics  electromag  measurement  mechanics  gravity  cycles  oscillation  temperature  visuo  plots  correlation  metrics  explanation  measure  methodology 
december 2017 by nhaliday
[1509.02504] Electric charge in hyperbolic motion: The early history and other geometrical aspects
We revisit the early work of Minkowski and Sommerfeld concerning hyperbolic motion, and we describe some geometrical aspects of the electrodynamic interaction. We discuss the advantages of a time symmetric formulation in which the material points are replaced by infinitesimal length elements.

SPACE AND TIME: An annotated, illustrated edition of Hermann Minkowski's revolutionary essay: http://web.mit.edu/redingtn/www/netadv/SP20130311.html
nibble  preprint  papers  org:mat  physics  electromag  relativity  exposition  history  mostly-modern  pre-ww2  science  the-trenches  discovery  intricacy  classic  explanation  einstein  giants  plots  manifolds  article  multi  liner-notes  org:junk  org:edu  absolute-relative 
november 2017 by nhaliday
Hyperbolic angle - Wikipedia
A unit circle {\displaystyle x^{2}+y^{2}=1} x^2 + y^2 = 1 has a circular sector with an area half of the circular angle in radians. Analogously, a unit hyperbola {\displaystyle x^{2}-y^{2}=1} {\displaystyle x^{2}-y^{2}=1} has a hyperbolic sector with an area half of the hyperbolic angle.
nibble  math  trivia  wiki  reference  physics  relativity  concept  atoms  geometry  ground-up  characterization  measure  definition  plots  calculation  nitty-gritty  direction  metrics  manifolds 
november 2017 by nhaliday
The Wilson Effect: the increase in heritability of IQ with age. - PubMed - NCBI
FIGURE 2 Estimates of genetic and shared environmental influence on g by age. The age scale is not linear (see text for details).
study  biodet  behavioral-gen  iq  psychology  cog-psych  metabuch  stylized-facts  variance-components  developmental  data  visualization  twin-study  correlation  🌞  pdf  piracy  age-generation  plots  psychometrics 
november 2017 by nhaliday
Dimensions - Geert Hofstede
http://geerthofstede.com/culture-geert-hofstede-gert-jan-hofstede/6d-model-of-national-culture/

https://www.reddit.com/r/europe/comments/4g88kt/eu28_countries_ranked_by_hofstedes_cultural/
https://archive.is/rXnII

https://hbdchick.wordpress.com/2013/09/07/national-individualism-collectivism-scores/

Individualism and Collectivism in Israeli Society: Comparing Religious and Secular High-School Students: https://sci-hub.tw/https://link.springer.com/article/10.1023/A:1016945121604
A common collective basis of mutual value consensus was found in the two groups; however, as predicted, there were differences between secular and religious students on the three kinds of items, since the religious scored higher than the secular students on items emphasizing collectivist orientation. The differences, however, do not fit the common theoretical framework of collectivism-individualism, but rather tend to reflect the distinction between in-group and universal collectivism.

Individualism and Collectivism in Two Conflicted Societies: Comparing Israeli-Jewish and Palestinian-Arab High School Students: https://sci-hub.tw/http://journals.sagepub.com/doi/10.1177/0044118X01033001001
Both groups were found to be more collectivistic than individualistic oriented. However, as predicted, the Palestinians scored higher than the Israeli students on items emphasizing in-group collectivist orientation (my nationality, my country, etc.). The differences between the two groups tended to reflect some subdistinctions such as different elements of individualism and collectivism. Moreover, they reflected the historical context and contemporary influences, such as the stage where each society is at in the nation-making process.

Religion as culture: religious individualism and collectivism among american catholics, jews, and protestants.: https://www.ncbi.nlm.nih.gov/pubmed/17576356
We propose the theory that religious cultures vary in individualistic and collectivistic aspects of religiousness and spirituality. Study 1 showed that religion for Jews is about community and biological descent but about personal beliefs for Protestants. Intrinsic and extrinsic religiosity were intercorrelated and endorsed differently by Jews, Catholics, and Protestants in a pattern that supports the theory that intrinsic religiosity relates to personal religion, whereas extrinsic religiosity stresses community and ritual (Studies 2 and 3). Important life experiences were likely to be social for Jews but focused on God for Protestants, with Catholics in between (Study 4). We conclude with three perspectives in understanding the complex relationships between religion and culture.

Inglehart–Welzel cultural map of the world: https://en.wikipedia.org/wiki/Inglehart%E2%80%93Welzel_cultural_map_of_the_world
Live cultural map over time 1981 to 2015: https://www.youtube.com/watch?v=ABWYOcru7js

https://en.wikipedia.org/wiki/Post-materialism
prof  psychology  social-psych  values  culture  cultural-dynamics  anthropology  individualism-collectivism  expression-survival  long-short-run  time-preference  uncertainty  outcome-risk  gender  egalitarianism-hierarchy  things  phalanges  group-level  world  tools  comparison  data  database  n-factor  occident  social-norms  project  microfoundations  multi  maps  visualization  org:junk  psych-architecture  personality  hari-seldon  discipline  self-control  geography  shift  developing-world  europe  the-great-west-whale  anglosphere  optimate  china  asia  japan  sinosphere  orient  MENA  reddit  social  discussion  backup  EU  inequality  envy  britain  anglo  nordic  ranking  top-n  list  eastern-europe  germanic  gallic  mediterranean  cog-psych  sociology  guilt-shame  duty  tribalism  us-them  cooperate-defect  competition  gender-diff  metrics  politics  wiki  concept  society  civilization  infographic  ideology  systematic-ad-hoc  let-me-see  general-survey  chart  video  history  metabuch  dynamic  trends  plots  time-series  reference  water  mea 
june 2017 by nhaliday
Lanchester's laws - Wikipedia
Lanchester's laws are mathematical formulae for calculating the relative strengths of a predator–prey pair, originally devised to analyse relative strengths of military forces.
war  meta:war  models  plots  time  differential  street-fighting  methodology  strategy  tactics  wiki  reference  history  mostly-modern  pre-ww2  world-war  britain  old-anglo  giants  magnitude  arms  identity 
june 2017 by nhaliday
PRE-INDUSTRIAL INEQUALITY*
Fig. 1: maximum possible Gini index still allowing subsistence of population (all surplus redistributed to 1 head honcho)
Fig. 2: scatter plot of Gini vs income, as well as possibility frontier

Ye Olde Inæqualitee Shoppe: https://pseudoerasmus.com/2014/10/01/inequality-possibility-frontier/
Gini indices, mean income, maximum feasible Gini, and "inequality extraction ratios" (gini2/max poss. inequality): https://pseudoerasmus.files.wordpress.com/2014/09/blwpg263.pdf
Growth and inequality in the great and little divergence debate: a Japanese perspective: http://onlinelibrary.wiley.com/doi/10.1111/ehr.12071/epdf
pdf  study  pseudoE  economics  growth-econ  broad-econ  inequality  industrial-revolution  agriculture  compensation  wealth-of-nations  wealth  britain  history  medieval  early-modern  europe  the-great-west-whale  🎩  cultural-dynamics  econ-metrics  data  multi  article  modernity  rent-seeking  vampire-squid  elite  india  asia  japan  civilization  time-series  plots  volo-avolo  malthus  manifolds  database  iron-age  mediterranean  the-classics  conquest-empire  germanic  gallic  latin-america  world  china  leviathan  usa  measurement  crosstab  pro-rata  MENA  africa  developing-world  distribution  archaeology  taxes  redistribution  egalitarianism-hierarchy  feudal 
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.
econotariat  economics  micro  plots  scale  marginal  industrial-org  business  econ-productivity  efficiency  cost-benefit  explanation  critique  clarity  intricacy  curvature  convexity-curvature  nonlinearity  input-output 
may 2017 by nhaliday
Pearson correlation coefficient - Wikipedia
https://en.wikipedia.org/wiki/Coefficient_of_determination
what does this mean?: https://twitter.com/GarettJones/status/863546692724858880
deleted but it was about the Pearson correlation distance: 1-r
I guess it's a metric

https://en.wikipedia.org/wiki/Explained_variation

http://infoproc.blogspot.com/2014/02/correlation-and-variance.html
A less misleading way to think about the correlation R is as follows: given X,Y from a standardized bivariate distribution with correlation R, an increase in X leads to an expected increase in Y: dY = R dX. In other words, students with +1 SD SAT score have, on average, roughly +0.4 SD college GPAs. Similarly, students with +1 SD college GPAs have on average +0.4 SAT.

this reminds me of the breeder's equation (but it uses r instead of h^2, so it can't actually be the same)

https://www.reddit.com/r/slatestarcodex/comments/631haf/on_the_commentariat_here_and_why_i_dont_think_i/dfx4e2s/
stats  science  hypothesis-testing  correlation  metrics  plots  regression  wiki  reference  nibble  methodology  multi  twitter  social  discussion  best-practices  econotariat  garett-jones  concept  conceptual-vocab  accuracy  causation  acm  matrix-factorization  todo  explanation  yoga  hsu  street-fighting  levers  🌞  2014  scitariat  variance-components  meta:prediction  biodet  s:**  mental-math  reddit  commentary  ssc  poast  gwern  data-science  metric-space  similarity  measure  dependence-independence 
may 2017 by nhaliday
Is Economic Activity Really “Distributed Less Evenly” Than It Used To Be?
http://xenocrypt.github.io/CountyIncomeHistory.html

First, imagine if you had a bar chart with every county in the United States sorted from lowest to highest by wages per capita, with the width of each bar proportional to the population of the county.

In fact, whenever anyone talks about “clustering” and “even distributions”, they’re mostly really talking about ways of comparing monotonic curves with integral one, whether they realize it or not.
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may 2017 by nhaliday
Get Ready to See This Globalization 'Elephant Chart' Over and Over Again - Bloomberg
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may 2017 by nhaliday
'Capital in the Twenty-First Century' by Thomas Piketty, reviewed | New Republic
by Robert Solow (positive)

The data then exhibit a clear pattern. In France and Great Britain, national capital stood fairly steadily at about seven times national income from 1700 to 1910, then fell sharply from 1910 to 1950, presumably as a result of wars and depression, reaching a low of 2.5 in Britain and a bit less than 3 in France. The capital-income ratio then began to climb in both countries, and reached slightly more than 5 in Britain and slightly less than 6 in France by 2010. The trajectory in the United States was slightly different: it started at just above 3 in 1770, climbed to 5 in 1910, fell slightly in 1920, recovered to a high between 5 and 5.5 in 1930, fell to below 4 in 1950, and was back to 4.5 in 2010.

The wealth-income ratio in the United States has always been lower than in Europe. The main reason in the early years was that land values bulked less in the wide open spaces of North America. There was of course much more land, but it was very cheap. Into the twentieth century and onward, however, the lower capital-income ratio in the United States probably reflects the higher level of productivity: a given amount of capital could support a larger production of output than in Europe. It is no surprise that the two world wars caused much less destruction and dissipation of capital in the United States than in Britain and France. The important observation for Piketty’s argument is that, in all three countries, and elsewhere as well, the wealth-income ratio has been increasing since 1950, and is almost back to nineteenth-century levels. He projects this increase to continue into the current century, with weighty consequences that will be discussed as we go on.

...

Now if you multiply the rate of return on capital by the capital-income ratio, you get the share of capital in the national income. For example, if the rate of return is 5 percent a year and the stock of capital is six years worth of national income, income from capital will be 30 percent of national income, and so income from work will be the remaining 70 percent. At last, after all this preparation, we are beginning to talk about inequality, and in two distinct senses. First, we have arrived at the functional distribution of income—the split between income from work and income from wealth. Second, it is always the case that wealth is more highly concentrated among the rich than income from labor (although recent American history looks rather odd in this respect); and this being so, the larger the share of income from wealth, the more unequal the distribution of income among persons is likely to be. It is this inequality across persons that matters most for good or ill in a society.

...

The data are complicated and not easily comparable across time and space, but here is the flavor of Piketty’s summary picture. Capital is indeed very unequally distributed. Currently in the United States, the top 10 percent own about 70 percent of all the capital, half of that belonging to the top 1 percent; the next 40 percent—who compose the “middle class”—own about a quarter of the total (much of that in the form of housing), and the remaining half of the population owns next to nothing, about 5 percent of total wealth. Even that amount of middle-class property ownership is a new phenomenon in history. The typical European country is a little more egalitarian: the top 1 percent own 25 percent of the total capital, and the middle class 35 percent. (A century ago the European middle class owned essentially no wealth at all.) If the ownership of wealth in fact becomes even more concentrated during the rest of the twenty-first century, the outlook is pretty bleak unless you have a taste for oligarchy.

Income from wealth is probably even more concentrated than wealth itself because, as Piketty notes, large blocks of wealth tend to earn a higher return than small ones. Some of this advantage comes from economies of scale, but more may come from the fact that very big investors have access to a wider range of investment opportunities than smaller investors. Income from work is naturally less concentrated than income from wealth. In Piketty’s stylized picture of the United States today, the top 1 percent earns about 12 percent of all labor income, the next 9 percent earn 23 percent, the middle class gets about 40 percent, and the bottom half about a quarter of income from work. Europe is not very different: the top 10 percent collect somewhat less and the other two groups a little more.

You get the picture: modern capitalism is an unequal society, and the rich-get-richer dynamic strongly suggest that it will get more so. But there is one more loose end to tie up, already hinted at, and it has to do with the advent of very high wage incomes. First, here are some facts about the composition of top incomes. About 60 percent of the income of the top 1 percent in the United States today is labor income. Only when you get to the top tenth of 1 percent does income from capital start to predominate. The income of the top hundredth of 1 percent is 70 percent from capital. The story for France is not very different, though the proportion of labor income is a bit higher at every level. Evidently there are some very high wage incomes, as if you didn’t know.

This is a fairly recent development. In the 1960s, the top 1 percent of wage earners collected a little more than 5 percent of all wage incomes. This fraction has risen pretty steadily until nowadays, when the top 1 percent of wage earners receive 10–12 percent of all wages. This time the story is rather different in France. There the share of total wages going to the top percentile was steady at 6 percent until very recently, when it climbed to 7 percent. The recent surge of extreme inequality at the top of the wage distribution may be primarily an American development. Piketty, who with Emmanuel Saez has made a careful study of high-income tax returns in the United States, attributes this to the rise of what he calls “supermanagers.” The very highest income class consists to a substantial extent of top executives of large corporations, with very rich compensation packages. (A disproportionate number of these, but by no means all of them, come from the financial services industry.) With or without stock options, these large pay packages get converted to wealth and future income from wealth. But the fact remains that much of the increased income (and wealth) inequality in the United States is driven by the rise of these supermanagers.

and Deirdre McCloskey (p critical): https://ejpe.org/journal/article/view/170
nice discussion of empirical economics, economic history, market failures and statism, etc., with several bon mots

Piketty’s great splash will undoubtedly bring many young economically interested scholars to devote their lives to the study of the past. That is good, because economic history is one of the few scientifically quantitative branches of economics. In economic history, as in experimental economics and a few other fields, the economists confront the evidence (as they do not for example in most macroeconomics or industrial organization or international trade theory nowadays).

...

Piketty gives a fine example of how to do it. He does not get entangled as so many economists do in the sole empirical tool they are taught, namely, regression analysis on someone else’s “data” (one of the problems is the word data, meaning “things given”: scientists should deal in capta, “things seized”). Therefore he does not commit one of the two sins of modern economics, the use of meaningless “tests” of statistical significance (he occasionally refers to “statistically insignificant” relations between, say, tax rates and growth rates, but I am hoping he does not suppose that a large coefficient is “insignificant” because R. A. Fisher in 1925 said it was). Piketty constructs or uses statistics of aggregate capital and of inequality and then plots them out for inspection, which is what physicists, for example, also do in dealing with their experiments and observations. Nor does he commit the other sin, which is to waste scientific time on existence theorems. Physicists, again, don’t. If we economists are going to persist in physics envy let us at least learn what physicists actually do. Piketty stays close to the facts, and does not, for example, wander into the pointless worlds of non-cooperative game theory, long demolished by experimental economics. He also does not have recourse to non-computable general equilibrium, which never was of use for quantitative economic science, being a branch of philosophy, and a futile one at that. On both points, bravissimo.

...

Since those founding geniuses of classical economics, a market-tested betterment (a locution to be preferred to “capitalism”, with its erroneous implication that capital accumulation, not innovation, is what made us better off) has enormously enriched large parts of a humanity now seven times larger in population than in 1800, and bids fair in the next fifty years or so to enrich everyone on the planet. [Not SSA or MENA...]

...

Then economists, many on the left but some on the right, in quick succession from 1880 to the present—at the same time that market-tested betterment was driving real wages up and up and up—commenced worrying about, to name a few of the pessimisms concerning “capitalism” they discerned: greed, alienation, racial impurity, workers’ lack of bargaining strength, workers’ bad taste in consumption, immigration of lesser breeds, monopoly, unemployment, business cycles, increasing returns, externalities, under-consumption, monopolistic competition, separation of ownership from control, lack of planning, post-War stagnation, investment spillovers, unbalanced growth, dual labor markets, capital insufficiency (William Easterly calls it “capital fundamentalism”), peasant irrationality, capital-market imperfections, public … [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
The More Parents Pass on Earning Power to Offspring, the Weaker the Argument for..., Garett Jones | EconLog | Library of Economics and Liberty
...the welfare state as social insurance.

After all, if you know how your kids are going to turn out, what's there to insure against? Sure, you'd like to grab resources from other people--the raiding party has a long history--but it's only when you're not sure how your kids will turn out that you start fretting over whether insurance markets face major market failures and whether government-mandated redistribution can fix those market failures.

...

The Great Gatsby Curve has made the rounds recently, showing that in countries with higher income inequality, your parent's income does a better job predicting your income. One version from Chrystia Freeland, author of the new book Plutocrats (source: WonkBlog).

...

Coda: I'm trying to get in the habit of often using "productivity" instead of "income" or "earnings." I use the term neutrally, referring to private productivity not overall productivity, so a successful raiding party is just as productive as a McDonald's.

Subcoda: I saw Freeland speak about her book earlier this year, and she places substantial weight on productivity-side explanations for the rise of the new plutocracy.
econotariat  spearhead  garett-jones  hmm  trends  politics  polisci  wonkish  inequality  redistribution  government  policy  correlation  insurance  market-failure  economics  mobility  class  plots  org:econlib  age-generation  broad-econ  biodet  info-econ  s-factor  behavioral-gen  welfare-state  bootstraps  elite  envy  X-not-about-Y 
march 2017 by nhaliday
Deadweight loss - Wikipedia
example:
Deadweight loss created by a binding price ceiling. Producer surplus is necessarily decreased, while consumer surplus may or may not increase; however the decrease in producer surplus must be greater than the increase (if any) in consumer surplus.
economics  concept  efficiency  markets  micro  metabuch  regulation  taxes  wiki  reference  models  things  manifolds  plots  supply-demand  intersection  intersection-connectedness 
february 2017 by nhaliday
Why does 'everything look correlated on a log-log scale'? - Quora
A correlation on a log log scale is meant to suggest the data follows a power law relationship of the form yy∝x−n.∝x−n.

A low R2R2 is suppose to suggest that the data either actually follows some other distribution like yy∝e−x∝e−xor is simply random noise. The problem is that log log correlation is a necessary but not sufficient condition to prove a power law relationship. While ruling out random noise is fairly easy, ruling out an alternate functional form is much harder- you can reject a power law hypothesis by a log log plot but you cannot prove it by one. As Aaron Brown answer points out, a lot of stuff that looks like it has a power law relationship does not actually follow it in reality. In particular, an exponential or log normal relationship might give similar results over most of the range but will diverge strongly at the tail end .[1] This difference can be difficult to detect if limited data is collected at the tail ends and deviations look like noise.

An example of a log normal distribution plotted on a normal and log-log scale. [2] Note the appearance of a straight line on the right tail that diverges strongly on the left tail. Using a power law relationship in this region will cause serious errors.
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february 2017 by nhaliday
Psychological comments: Does Age make us sage or sag?
Khan on Twitter: "figure on right from @tuckerdrob lab is depressing (the knowledge plateau). do i read in vain??? https://t.co/DZzBD8onEv": https://twitter.com/razibkhan/status/809439911627493377
- reasoning rises then declines after age ~20
- knowledge plateaus by age 35-40
- different interpretation provided by study authors w/ another graph (renewal)
- study (can't find the exact graph anywhere): http://www.iapsych.com/wj3ewok/LinkedDocuments/McArdle2002.pdf

School’s out: https://westhunt.wordpress.com/2016/12/29/schools-out/
I saw a note by Razib Khan, in which he mentioned that psychometric research suggests that people plateau in their knowledge base as adults. I could believe it. But I’m not sure it’s true in my case. One might estimate total adult knowledge in terms of BS equivalents…

Age-related IQ decline is reduced markedly after adjustment for the Flynn effect: https://www.ncbi.nlm.nih.gov/m/pubmed/20349385/
Twenty-year-olds outperform 70-year-olds by as much as 2.3 standard deviations (35 IQ points) on subtests of the Wechsler Adult Intelligence Scale (WAIS). We show that most of the difference can be attributed to an intergenerational rise in IQ known as the Flynn effect.

...

For these verbal subtests, the Flynn effect masked a modest increase in ability as individuals grow older.

Predictors of ageing-related decline across multiple cognitive functions: http://www.sciencedirect.com/science/article/pii/S0160289616302707
Cognitive ageing is likely a process with few large-effect predictors

A strong link between speed of visual discrimination and cognitive ageing: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4123160/
Results showed a moderate correlation (r = 0.460) between inspection time performance and intelligence, and a strong correlation between change in inspection time and change in intelligence from 70 to 76 (r = 0.779). These results support the processing speed theory of cognitive ageing. They go beyond cross-sectional correlation to show that cognitive change is accompanied by changes in basic visual information processing as we age.
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december 2016 by nhaliday
Hidden Games | West Hunter
Since we are arguably a lot smarter than ants or bees, you might think that most adaptive personality variation in humans would be learned (a response to exterior cues) rather than heritable. Maybe some is, but much variation looks heritable. People don’t seem to learn to be aggressive or meek – they just are, and in those tendencies resemble their biological parents. I wish I (or anyone else) understood better why this is so, but there are some notions floating around that may explain it. One is that jacks of all trades are masters of none: if you play the same role all the time, you’ll be better at it than someone who keep switching personalities. It could be the case that such switching is physiologically difficult and/or expensive. And in at least some cases, being predictable has social value. Someone who is known to be implacably aggressive will win at ‘chicken’. Being known as the sort of guy who would rush into a burning building to save ugly strangers may pay off, even though actually running into that blaze does not.

...

This kind of game-theoretic genetic variation, driving distinct behavioral strategies, can have some really odd properties. For one thing, there can be more than one possible stable mix of behavioral types even in identical ecological situations. It’s a bit like dropping a marble onto a hilly landscape with many unconnected valleys – it will roll to the bottom of some valley, but initial conditions determine which valley. Small perturbations will not knock the marble out of the valley it lands in. In the same way, two human populations could fall into different states, different stable mixes of behavioral traits, for no reason at all other than chance and then stay there indefinitely. Populations are even more likely to fall into qualitatively different stable states when the ecological situations are significantly different.

...

What this means, think, is that it is entirely possible that human societies fall into fundamentally different patterns because of genetic influences on behavior that are best understood via evolutionary game theory. Sometimes one population might have a psychological type that doesn’t exist at all in another society, or the distribution could be substantially different. Sometimes these different social patterns will be predictable results of different ecological situations, sometimes the purest kind of chance. Sometimes the internal dynamics of these genetic systems will produce oscillatory (or chaotic!) changes in gene frequencies over time, which means changes in behavior and personality over time. In some cases, these internal genetic dynamics may be the fundamental reason for the rise and fall of empires. Societies in one stable distribution, in a particular psychological/behavioral/life history ESS, may simply be unable to replicate some of the institutions found in peoples in a different ESS.

Evolutionary forces themselves vary according to what ESS you’re in. Which ESS you’re in may be the most fundamental ethnic fact, and explain the most profound ethnic behavioral differences

Look, everyone is always looking for the secret principles that underlie human society and history, some algebra that takes mounds of historical and archaeological data – the stuff that happens – and explains it in some compact way, lets us understand it, just as continental drift made a comprehensible story out of geology. On second thought, ‘everyone’ mean that smallish fraction of researchers that are slaves of curiosity…

This approach isn’t going to explain everything – nothing will. But it might explain a lot, which would make it a hell of a lot more valuable than modern sociology or cultural anthropology. I would hope that an analysis of this sort might help explain fundamental long-term flavor difference between different human societies, differences in life-history strategies especially (dads versus cads, etc). If we get particularly lucky, maybe we’ll have some notions of why the Mayans got bored with civilization, why Chinese kids are passive at birth while European and African kids are feisty. We’ll see.

Of course we could be wrong. It’s going to have be tested and checked: it’s not magic. It is based on the realization that the sort of morphs and game-theoretic balances we see in some nonhuman species are if anything more likely to occur in humans, because our societies are so complex, because the effectiveness of a course of action so often depends on the psychologies of other individuals – that and the obvious fact that people are not the same everywhere.
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november 2016 by nhaliday
Overcoming Bias : Beware General Visible Prey
So, bottom line, the future great filter scenario that most concerns me is one where our solar-system-bound descendants have killed most of nature, can’t yet colonize other stars, are general predators and prey of each other, and have fallen into a short-term-predatory-focus equilibrium where predators can easily see and travel to most all prey. Yes there are about a hundred billion comets way out there circling the sun, but even that seems a small enough number for predators to careful map and track all of them.
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october 2016 by nhaliday
Democracy does not cause growth | Brookings Institution
64-page paper
Democracy & Growth: http://www.nber.org/papers/w4909
The favorable effects on growth include maintenance of the rule of law, free markets, small government consumption, and high human capital. Once these kinds of variables and the initial level of real per-capita GDP are held constant, the overall effect of democracy on growth is weakly negative. There is a suggestion of a nonlinear relationship in which democracy enhances growth at low levels of political freedom but depresses growth when a moderate level of freedom has already been attained.

The growth effect of democracy: Is it heterogenous and how can it be estimated∗: http://perseus.iies.su.se/~tpers/papers/cifar_paper_may16_07.pdf
In particular, we find an average negative effect on growth of leaving democracy on the order of −2 percentage points implying effects on income per capita as large as 45 percent over the 1960-2000 panel. Heterogenous characteristics of reforming and non-reforming countries appear to play an important role in driving these results.

Does democracy cause innovation? An empirical test of the popper hypothesis: http://www.sciencedirect.com.sci-hub.cc/science/article/pii/S0048733317300975
The results from the difference-in-differences method show that democracy itself has no direct positive effect on innovation measured with patent counts, patent citations and patent originality.

Benevolent Autocrats: https://williameasterly.files.wordpress.com/2011/09/benevolent-autocrats-easterly-draft.pdf
A large literature attributes this to the higher variance of growth rates under autocracy than under democracy. The literature offers alternative explanations for this stylized fact: (1) leaders don’t matter under democracy, but good and bad leaders under autocracy cause high and low growth, (2) leaders don’t matter under autocracy either, but good and bad autocratic systems cause greater extremes of high and low growth, or (3) democracy does better than autocracy at reducing variance from shocks from outside the political system. This paper details further the stylized facts to test these distinctions. Inconsistent with (1), the variance of growth within the terms of leaders swamps the variance across leaders, and more so under autocracy than under democracy. Country effects under autocracy are also overwhelmed by within-country variance, inconsistent with (2). Explanation (3) fits the stylized facts the best of the three alternatives.

Political Institutions, Size of Government and Redistribution: An empirical investigation: http://www.lse.ac.uk/internationalDevelopment/pdf/WP/WP89.pdf
Results show that the stronger democratic institutions are, the lower is government size and the higher the redistributional capacity of the state. Political competition exercises the strongest and most robust effect on the two variables.

https://twitter.com/GarettJones/status/899466295170801664
https://archive.is/sPFII
Fits the high-variance theory of autocracies:
More miracles, more disasters. And there's a lot of demand for miracles.

Measuring the ups and downs of governance: https://www.brookings.edu/blog/future-development/2017/09/22/measuring-the-ups-and-downs-of-governance/
Figure 2: Voice and Accountability and Government Effectiveness, 2016
https://twitter.com/whyvert/status/917444456386666497
https://archive.is/EBQlD
Georgia, Japan, Rwanda, and Serbia ↑ Gov Effectiveness; Indonesia, Tunisia, Liberia, Serbia, and Nigeria ↑ Voice and Accountability.

The logic of hereditary rule: theory and evidence: http://eprints.lse.ac.uk/69615/
Hereditary leadership has been an important feature of the political landscape throughout history. This paper argues that hereditary leadership is like a relational contract which improves policy incentives. We assemble a unique dataset on leaders between 1874 and 2004 in which we classify them as hereditary leaders based on their family history. The core empirical finding is that economic growth is higher in polities with hereditary leaders but only if executive constraints are weak. Moreover, this holds across of a range of specifications. The finding is also mirrored in policy outcomes which affect growth. In addition, we find that hereditary leadership is more likely to come to an end when the growth performance under the incumbent leader is poor.

I noted this when the paper was a working paper, but non-hereditary polities with strong contraints have higher growth rates.
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september 2016 by nhaliday

bundles : abstractsp

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