nhaliday + expert   183

How many laypeople holding a popular opinion are needed to counter an expert opinion?: Thinking & Reasoning: Vol 0, No 0
Although lay opinions and expert opinions have been studied extensively in isolation, the present study examined the relationship between the two by asking how many laypeople are needed to counter an expert opinion. A Bayesian formalisation allowed the prescription of this quantity. Participants were subsequently asked to assess how many laypeople are needed in different situations. The results demonstrate that people are sensitive to the relevant factors identified for determining how many lay opinions are required to counteract a single expert opinion. People's assessments were fairly good in line with Bayesian predictions.
study  psychology  social-psych  learning  rationality  epistemic  info-foraging  info-dynamics  expert  bayesian  neurons  expert-experience  decision-making  reason 
october 2017 by nhaliday
Benedict Evans on Twitter: ""University can save you from the autodidact tendency to overrate himself. Democracy depends on people who know they don’t know everything.""
“The autodidact’s risk is that they think they know all of medieval history but have never heard of Charlemagne” - Umberto Eco

Facts are the least part of education. The structure and priorities they fit into matters far more, and learning how to learn far more again
techtariat  sv  twitter  social  discussion  rhetoric  info-foraging  learning  education  higher-ed  academia  expert  lens  aphorism  quotes  hi-order-bits  big-picture  synthesis  expert-experience 
october 2017 by nhaliday
New Theory Cracks Open the Black Box of Deep Learning | Quanta Magazine
A new idea called the “information bottleneck” is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.

sounds like he's just talking about autoencoders?
news  org:mag  org:sci  popsci  announcement  research  deep-learning  machine-learning  acm  information-theory  bits  neuro  model-class  big-surf  frontier  nibble  hmm  signal-noise  deepgoog  expert  ideas  wild-ideas  summary  talks  video  israel  roots  physics  interdisciplinary  ai  intelligence  shannon  giants  arrows  preimage  lifts-projections  composition-decomposition  characterization  markov  gradient-descent  papers  liner-notes  experiment  hi-order-bits  generalization  expert-experience  explanans  org:inst  speedometer 
september 2017 by nhaliday
Living with Ignorance in a World of Experts
Another kind of track record that we might care about is not about the expert’s performance, qua expert, but about her record of epistemic integrity. This will be important for helping provide reasonably well supported answers to (Q3) and (Q4) in particular. Anderson (2011) offers some related ideas in her discussion of “criteria for judging honesty” and “criteria for judging epistemic responsibility.” Things we might be interested include the following:
• evidence of previous expert-related dishonesty (e.g. plagiarism, faking data)
• evidence of a record of misleading statements (e.g. cherry-picking data, quotations out of context)
• evidence of a record of misrepresenting views of expert opponents
• evidence of evasion of peer-review or refusal to allow other experts to assess work
• evidence of refusal to disclose data, methodology, or detailed results
• evidence of refusal to disclose results contrary to the expert’s own views
• evidence of “dialogic irrationality”: repeating claims after they have been publicly refuted, without responding to the refutations
• evidence of a record of “over-claiming” of expertise: claiming expertise beyond the expert’s domain of expertise
• evidence of a record of “lending” one’s expertise to support other individuals or institutions that themselves lack epistemic integrity in some of the above ways
• evidence of being an “opinion for hire”—offering expert testimony for pay, perhaps particularly if that testimony conflicts with other things the expert has said
pdf  essay  study  philosophy  rationality  epistemic  info-dynamics  westminster  track-record  checklists  list  tetlock  expert  info-foraging  sleuthin  metabuch  meta:rhetoric  integrity  honor  crooked  phalanges  truth  expert-experience  reason  decision-making 
september 2017 by nhaliday
The Long-Run Weight of Communism or the Weight of LongRun History?
This study provides evidence that culture understood as values and beliefs moves very slowly. Despite massive institutional change, values and beliefs in transition countries have not changed much over the last 20 years. Evidence suggests that culture is affected by the long run historical past, in particular the participation in empires for over 100 years. Current institutional evolutions in transition countries might be more affected by their long run past than by the communist experience of the twentieth century
pdf  study  economics  growth-econ  broad-econ  cliometrics  path-dependence  wealth-of-nations  divergence  history  mostly-modern  communism  authoritarianism  political-econ  institutions  eastern-europe  russia  long-short-run  culture  cultural-dynamics  🎩  values  general-survey  nationalism-globalism  competition  individualism-collectivism  gender  labor  democracy  expert  antidemos  capitalism  microfoundations  expert-experience  roots  top-n  branches  intel  china  asia  sinosphere  orient  technocracy  europe  germanic  agriculture  heavy-industry  pre-ww2  urban-rural  EU  trust  conquest-empire  empirical  markets  usa  migration  tribalism  us-them  convergence  enlightenment-renaissance-restoration-reformation  confucian  comparison  flux-stasis  hari-seldon 
august 2017 by nhaliday
Stat 260/CS 294: Bayesian Modeling and Inference
- Priors (conjugate, noninformative, reference)
- Hierarchical models, spatial models, longitudinal models, dynamic models, survival models
- Testing
- Model choice
- Inference (importance sampling, MCMC, sequential Monte Carlo)
- Nonparametric models (Dirichlet processes, Gaussian processes, neutral-to-the-right processes, completely random measures)
- Decision theory and frequentist perspectives (complete class theorems, consistency, empirical Bayes)
- Experimental design
unit  course  berkeley  expert  michael-jordan  machine-learning  acm  bayesian  probability  stats  lecture-notes  priors-posteriors  markov  monte-carlo  frequentist  latent-variables  decision-theory  expert-experience  confidence  sampling 
july 2017 by nhaliday
Superintelligence Risk Project Update II

For example, I asked him what he thought of the idea that to we could get AGI with current techniques, primarily deep neural nets and reinforcement learning, without learning anything new about how intelligence works or how to implement it ("Prosaic AGI" [1]). He didn't think this was possible, and believes there are deep conceptual issues we still need to get a handle on. He's also less impressed with deep learning than he was before he started working in it: in his experience it's a much more brittle technology than he had been expecting. Specifically, when trying to replicate results, he's often found that they depend on a bunch of parameters being in just the right range, and without that the systems don't perform nearly as well.

The bottom line, to him, was that since we are still many breakthroughs away from getting to AGI, we can't productively work on reducing superintelligence risk now.

He told me that he worries that the AI risk community is not solving real problems: they're making deductions and inferences that are self-consistent but not being tested or verified in the world. Since we can't tell if that's progress, it probably isn't. I asked if he was referring to MIRI's work here, and he said their work was an example of the kind of approach he's skeptical about, though he wasn't trying to single them out. [2]

Earlier this week I had a conversation with an AI researcher [1] at one of the main industry labs as part of my project of assessing superintelligence risk. Here's what I got from them:

They see progress in ML as almost entirely constrained by hardware and data, to the point that if today's hardware and data had existed in the mid 1950s researchers would have gotten to approximately our current state within ten to twenty years. They gave the example of backprop: we saw how to train multi-layer neural nets decades before we had the computing power to actually train these nets to do useful things.

Similarly, people talk about AlphaGo as a big jump, where Go went from being "ten years away" to "done" within a couple years, but they said it wasn't like that. If Go work had stayed in academia, with academia-level budgets and resources, it probably would have taken nearly that long. What changed was a company seeing promising results, realizing what could be done, and putting way more engineers and hardware on the project than anyone had previously done. AlphaGo couldn't have happened earlier because the hardware wasn't there yet, and was only able to be brought forward by massive application of resources.

Summary: I'm not convinced that AI risk should be highly prioritized, but I'm also not convinced that it shouldn't. Highly qualified researchers in a position to have a good sense the field have massively different views on core questions like how capable ML systems are now, how capable they will be soon, and how we can influence their development. I do think these questions are possible to get a better handle on, but I think this would require much deeper ML knowledge than I have.
ratty  core-rats  ai  risk  ai-control  prediction  expert  machine-learning  deep-learning  speedometer  links  research  research-program  frontier  multi  interview  deepgoog  games  hardware  performance  roots  impetus  chart  big-picture  state-of-art  reinforcement  futurism  🤖  🖥  expert-experience  singularity  miri-cfar  empirical  evidence-based  speculation  volo-avolo  clever-rats  acmtariat  robust  ideas  crux  atoms  detail-architecture  software  gradient-descent 
july 2017 by nhaliday
Man's Future Birthright: Essays on Science and Humanity by H. J. Muller. - Reviewed by Theodosius Dobzhansky
Hermann J. Muller (1890-1967) was not only a great geneticist but a visionary full of messianic zeal, profoundly concerned about directing the evolutionary course of mankind toward what he believed a better future.
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july 2017 by nhaliday
Has war been declining? | OUPblog
People have always alternated between the three behavioural options of cooperation, peaceful competition, and violence to attain evolution-shaped human desires. Developments since the onset of the industrial age from 1815 onwards have radically shifted the calculus of war and peace towards the two peaceful options, sharply decreasing belligerency in the parts of the world affected by the process of modernization. Rather than war becoming more costly in terms of life and resources, as many believe to be the case (not so), the real change is that peace has become more rewarding. The Modernization Peace concept scrutinizes, contextualizes, and encompasses within a comprehensive framework the various peace theories advanced over the past few decades, and shows the more valid ones to be elements of a greater whole. By now, war has disappeared within the world’s most developed areas and survives only in its less developed, developing, and undeveloped parts.

Finally, the Modernization Peace concept has been disrupted in the past, most conspicuously during the two world wars, and challenges to it still arise. Challenges include claimants to alternative modernity—such as China and Russia, still much behind in levels of development and affluence—anti-modernists, and failed modernizers that may spawn terrorism, potentially unconventional. While the world has become more peaceful than ever before, with war unprecedentedly disappearing in its most developed parts, there is still much to worry about in terms of security and there is no place for complacency.
essay  article  expert  war  meta:war  peace-violence  martial  cooperate-defect  china  asia  russia  modernity  pinker  trends  the-bones  zeitgeist  broad-econ  org:edu  anthropology  tetlock  incentives  cost-benefit  roots  expert-experience 
june 2017 by nhaliday
- the genetic book of the dead [Dawkins]
- complementarity [Frank Wilczek]
- relative information
- effective theory [Lisa Randall]
- affordances [Dennett]
- spontaneous symmetry breaking
- relatedly, equipoise [Nicholas Christakis]
- case-based reasoning
- population reasoning (eg, common law)
- criticality [Cesar Hidalgo]
- Haldan's law of the right size (!SCALE!)
- polygenic scores
- non-ergodic
- ansatz
- state [Aaronson]: http://www.scottaaronson.com/blog/?p=3075
- transfer learning
- effect size
- satisficing
- scaling
- the breeder's equation [Greg Cochran]
- impedance matching

- reciprocal altruism
- life history [Plomin]
- intellectual honesty [Sam Harris]
- coalitional instinct (interesting claim: building coalitions around "rationality" actually makes it more difficult to update on new evidence as it makes you look like a bad person, eg, the Cathedral)
basically same: https://twitter.com/ortoiseortoise/status/903682354367143936

more: https://www.edge.org/conversation/john_tooby-coalitional-instincts

interesting timing. how woke is this dude?
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may 2017 by nhaliday
[1705.08807] When Will AI Exceed Human Performance? Evidence from AI Experts
Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans.

study  preprint  science  meta:science  technology  ai  automation  labor  ai-control  risk  futurism  poll  expert  usa  asia  trends  hmm  idk  definite-planning  frontier  ideas  prediction  innovation  china  sinosphere  multi  reddit  social  commentary  ssc  speedometer  flux-stasis  ratty  expert-experience  org:mat  singularity  optimism  pessimism 
may 2017 by nhaliday
Why isn't ethanol used more widely as an automobile fuel? - Quora
Some of these are straightforward engineering challenges, but others are fundamental and insurmountable flaws.

Simply put, corn-based ethanol is bad energy policy. We're already at the maximum blending level in the US (10%) where any further increases will likely harm consumers. There's little environmental OR energy security benefit to ethanol, because it takes about a gallon worth of fossil fuels to produce a gallon of ethanol. (Studies vary on the exact number, ranging from an EROI of 0.8 to 1.3, but the average is zero net gain.)

Basically US ethanol blending is an enormous make-work scheme for big corporate farm interests and Iowa co-ops. It is an enormous sop to the key farm lobby in early Presidential Primary states. That's the only real reason we make vast quantities of corn-based ethanol and mandate its use in the US. Nobody would want to buy the stuff if it weren't for legal blending requirements imposed on refiners and fuel distributors.

Again, some countries can do ethanol better. Climate is important to biofuels. But it is not a good fuel choice for most nations.

- Ryan Carlyle
q-n-a  qra  expert  explanation  ethanol  energy-resources  stock-flow  transportation  heavy-industry  biophysical-econ  expert-experience 
may 2017 by nhaliday
What is the likelihood we run out of fossil fuels before we can switch to renewable energy sources? - Quora
1) Can we de-carbon our primary energy production before global warming severely damages human civilization? In the short term this means switching from coal to natural gas, and in the long term replacing both coal and gas generation with carbon-neutral sources such as renewables or nuclear. The developed world cannot accomplish this alone -- it requires worldwide action, and most of the pain will be felt by large developing nations such as India and China. Ultimately this is a political and economic problem. The technology to eliminate most carbon from electricity generation exists today at fairly reasonable cost.

2) Can we develop a better transportation energy storage technology than oil, before market forces drive prices to levels that severely damage the global economy? Fossil fuels are a source of energy, but primarily we use oil in vehicles because it is an exceptional energy TRANSPORT medium. Renewables cannot meet this need because battery technology is completely uncompetitive for most fuel consumers -- prices are an order of magnitude too high and energy density is an order of magnitude too low for adoption of all-electric vehicles outside developed-world urban centers. (Heavy trucking, cargo ships, airplanes, etc will never be all-electric with chemical batteries. There are hard physical limits to the energy density of electrochemical reactions. I'm not convinced passenger vehicles will go all-electric in our lifetimes either.) There are many important technologies in existence that will gain increasing traction in the next 50 years such as natural gas automobiles and improved gas/electric hybrids, but ultimately we need a better way to store power than fossil fuels. _This is a deep technological problem that will not be solved by incremental improvements in battery chemistry or any process currently in the R&D pipeline_.

Based on these two unresolved issues, _I place the odds of us avoiding fossil-fuel-related energy issues (major climate or economic damage) at less than 10%_. The impetus for the major changes required will not be sufficiently urgent until the world is seeing severe and undeniable impacts. Civilization will certainly survive -- but there will be no small amount of human suffering during the transition to whatever comes next.

- Ryan Carlyle
q-n-a  qra  expert  energy-resources  climate-change  environment  risk  civilization  nihil  prediction  threat-modeling  world  futurism  biophysical-econ  stock-flow  transportation  technology  economics  long-short-run  no-go  speedometer  modernity  expert-experience 
may 2017 by nhaliday
Environmental Cancer? | In the Pipeline
And while I take the point that endocrine disruptors and the like need to be watched (and that we really do need to study these things more), I don’t see why the alarm bells need to be rung quite this loudly.
scitariat  org:nat  commentary  critique  expert  chemistry  endocrine  health  medicine  cancer  embodied-street-fighting  org:sci  science-anxiety  regularizer  public-health  expert-experience 
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
Futuristic Physicists? | Do the Math
interesting comment: https://westhunt.wordpress.com/2014/03/05/outliers/#comment-23087
referring to timelines? or maybe also the jetpack+flying car (doesn't seem physically impossible; at most impossible for useful trip lengths)?

Topic Mean % pessim. median disposition
1. Autopilot Cars 1.4 (125 yr) 4 likely within 50 years
15. Real Robots 2.2 (800 yr) 10 likely within 500 years
13. Fusion Power 2.4 (1300 yr) 8 likely within 500 years
10. Lunar Colony 3.2 18 likely within 5000 years
16. Cloaking Devices 3.5 32 likely within 5000 years
20. 200 Year Lifetime 3.3 16 maybe within 5000 years
11. Martian Colony 3.4 22 probably eventually (>5000 yr)
12. Terraforming 4.1 40 probably eventually (> 5000 yr)
18. Alien Dialog 4.2 42 probably eventually (> 5000 yr)
19. Alien Visit 4.3 50 on the fence
2. Jetpack 4.1 64 unlikely ever
14. Synthesized Food 4.2 52 unlikely ever
8. Roving Astrophysics 4.6 64 unlikely ever
3. Flying “Cars” 3.9 60 unlikely ever
7. Visit Black Hole 5.1 74 forget about it
9. Artificial Gravity 5.3 84 forget about it
4. Teleportation 5.3 85 forget about it
5. Warp Drive 5.5 92 forget about it
6. Wormhole Travel 5.5 96 forget about it
17. Time Travel 5.7 92 forget about it
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march 2017 by nhaliday
huge popularity of "behavioral economics"among powerful people=largely excitement at how much more control they'd exert over stupider people

Time for Behavioral Political Economy? An Analysis of Articles in Behavioral Economics: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1846184
This study analyzes leading research in behavioral economics to see whether it contains advocacy of paternalism and whether it addresses the potential cognitive limitations and biases of the policymakers who are going to implement paternalist policies. The findings reveal that 20.7% of the studied articles in behavioral economics propose paternalist policy action and that 95.5% of these do not contain any analysis of the cognitive ability of policymakers. This suggests that behavioral political economy, in which the analytical tools of behavioral economics are applied to political decision-makers as well, would offer a useful extension of the research program.

Research shows that Americans and conservatives can be less open to cues to change behavior.

It’s For Your Own Good!: http://www.nybooks.com/articles/2013/03/07/its-your-own-good/
- Cass Sunstein

Against Autonomy: Justifying Coercive Paternalism
by Sarah Conly
Cambridge University Press, 206 pp., $95.00

WHO NUDGES THE NUDGERS?: https://jacobitemag.com/2017/10/26/who-nudges-the-nudgers/
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february 2017 by nhaliday
bounds - What is the variance of the maximum of a sample? - Cross Validated
- sum of variances is always a bound
- can't do better even for iid Bernoulli
- looks like nice argument from well-known probabilist (using E[(X-Y)^2] = 2Var X), but not clear to me how he gets to sum_i instead of sum_{i,j} in the union bound?
edit: argument is that, for j = argmax_k Y_k, we have r < X_i - Y_j <= X_i - Y_i for all i, including i = argmax_k X_k
- different proof here (later pages): http://www.ism.ac.jp/editsec/aism/pdf/047_1_0185.pdf
Var(X_n:n) <= sum Var(X_k:n) + 2 sum_{i < j} Cov(X_i:n, X_j:n) = Var(sum X_k:n) = Var(sum X_k) = nσ^2
why are the covariances nonnegative? (are they?). intuitively seems true.
- for that, see https://pinboard.in/u:nhaliday/b:ed4466204bb1
- note that this proof shows more generally that sum Var(X_k:n) <= sum Var(X_k)
- apparently that holds for dependent X_k too? http://mathoverflow.net/a/96943/20644
q-n-a  overflow  stats  acm  distribution  tails  bias-variance  moments  estimate  magnitude  probability  iidness  tidbits  concentration-of-measure  multi  orders  levers  extrema  nibble  bonferroni  coarse-fine  expert  symmetry  s:*  expert-experience  proofs 
february 2017 by nhaliday
Soft analysis, hard analysis, and the finite convergence principle | What's new
It is fairly well known that the results obtained by hard and soft analysis respectively can be connected to each other by various “correspondence principles” or “compactness principles”. It is however my belief that the relationship between the two types of analysis is in fact much closer[3] than just this; in many cases, qualitative analysis can be viewed as a convenient abstraction of quantitative analysis, in which the precise dependencies between various finite quantities has been efficiently concealed from view by use of infinitary notation. Conversely, quantitative analysis can often be viewed as a more precise and detailed refinement of qualitative analysis. Furthermore, a method from hard analysis often has some analogue in soft analysis and vice versa, though the language and notation of the analogue may look completely different from that of the original. I therefore feel that it is often profitable for a practitioner of one type of analysis to learn about the other, as they both offer their own strengths, weaknesses, and intuition, and knowledge of one gives more insight[4] into the workings of the other. I wish to illustrate this point here using a simple but not terribly well known result, which I shall call the “finite convergence principle” (thanks to Ben Green for suggesting this name; Jennifer Chayes has also suggested the “metastability principle”). It is the finitary analogue of an utterly trivial infinitary result – namely, that every bounded monotone sequence converges – but sometimes, a careful analysis of a trivial result can be surprisingly revealing, as I hope to demonstrate here.
gowers  mathtariat  math  math.CA  expert  reflection  philosophy  meta:math  logic  math.CO  lens  big-picture  symmetry  limits  finiteness  nibble  org:bleg  coarse-fine  metameta  convergence  expert-experience 
january 2017 by nhaliday
Public perceptions of expert disagreement: Bias and incompetence or a complex and random world? - Sep 07, 2015
People with low education, or with low self-reported topic knowledge, were most likely to attribute disputes to expert incompetence. People with higher self-reported knowledge tended to attribute disputes to expert bias due to financial or ideological reasons. The more highly educated and cognitively able were most likely to attribute disputes to natural factors, such as the irreducible complexity and randomness of the phenomenon.

reminds me of Hanson's interpretation of political disagreement: poor data, complex phenomena with high causal density
study  psychology  social-psych  rationality  iq  expert  info-foraging  decision-making  epistemic  albion  intricacy  wonkish  biases  self-report  complex-systems  thick-thin  stylized-facts  descriptive  ideology  info-dynamics  chart  truth  expert-experience  reason 
january 2017 by nhaliday
Would Clinton have defeated Trump in an epistocracy? | We the Pleeple
Next on the chart are the various epistocratic scenarios. What if we give especially low-knowledge voters only half a vote, or only a third, or bar them completely? What if we use a graduated more-votes-for-more-knowledge system? What if we give especially high-knowledge voters an extra vote, or two, or take epistocracy literally and allow only these high-knowledge folks to vote?

Do any of these proposals improve Clinton’s popular vote margin over Trump? No. In fact, each one would have given Trump a popular vote lead, anywhere from 0.5 points (giving high-knowledge folks a single extra vote) to 4.3 points (letting only high-knowledge folks vote). In an epistocracy of the sort Brennan and others imagine, Trump’s victory over Clinton would have been even more securely won.

contrary update:
ACTUALLY, EPISTOCRACY MIGHT HAVE HELPED CLINTON DEFEAT TRUMP: http://www.pleeps.org/2017/04/11/actually-epistocracy-might-have-helped-clinton-defeat-trump/
But she probably would have been running against President Romney, and might have still lost.

Were Trump Voters Irrational?: http://quillette.com/2017/09/28/trump-voters-irrational/
In addition to being misplaced, leftists never seem to see how insulting this critique of Republican voters is. Their failure to see the insult illustrates precisely what they get wrong in evaluating the rationality of the Trump voters. Consider that these What’s the Matter with Kansas? critiques are written by highly educated left-wing pundits, professors, and advocates. Perhaps we should ask one of them whether their own vote is purely self-interested and for their own monetary benefit. They will say no, of course. And they will deny as well that their vote is irrational. Progressives will say that they often vote against their own monetary interests in order to do good for other people. Or they will say that their vote reflects their values and worldview—that they are concerned about the larger issues that are encompassed by that worldview (abortion legislation or climate change or gun restriction). Leftists seem unable to see that Republican voters—even lower income ones—may be just as attached to their own values and worldviews. The stance of the educated progressive making the What’s the Matter with Kansas? argument seems to be that: “no one else should vote against their monetary interests, but it’s not irrational for me to do so, because I am enlightened.”


Progressives tend to deny or obfuscate (just as conservatives obfuscate the research on global warming) the data indicating that single-parent households lead to more behavioral problems among children. Overwhelmingly progressive university schools of education deny the strong scientific consensus that phonics-based reading instruction facilitates most readers, especially those struggling the most. Many progressives find it hard to believe that there is no bias at all in the initial hiring of women for tenure-track university positions in STEM disciplines. Progressives tend to deny the consensus view that genetically modified organisms are safe to consume. Gender feminists routinely deny biological facts about sex differences. Largely Democratic cities and university towns are at the forefront of the anti-vaccine movement which denies a scientific consensus. In the same cities and towns, people find it hard to believe that there is a strong consensus among economists that rent control causes housing shortages and a diminution in the quality of housing. [Research citations for all the above are available from the author here.]


More formal studies have indicated that there are few differences in factual knowledge of the world between Republicans and Democrats. The Pew Research Center reported one of its News IQ surveys in 2015 (What the Public Knows, April 28, 2015) and found very few partisan differences. People in the sample answered 12 questions about current events (identifying the route of the Keystone XL pipeline; knowledge of how many Supreme Court justices are women; etc.) and the Republicans outperformed the Democrats on 7 of the 12 items. Democrats outperformed the Republicans on 5 of the items. On average, the Republicans in the sample answered 8.3 items correctly, the Democrats answered 7.9 items correctly, and the independents answered 8.0 items correctly.


Measures of so-called “knowledge” in such a domain are easily skewed in a partisan manner by selection effects. This is a version of the “party of science” problem discussed previously. Whether the Democrats or the Republicans are the “party of science” depends entirely on how the issue in question is selected. The 17-item measure used by Klein was relatively balanced (8 items biased against leftists and 9 items biased against conservatives). With all the caveats in place about the difficulty of item matching, the weak conclusion that can be drawn is that existing research provides no evidence for the view that conservatives are deficient in the domain of economic knowledge—a domain critical for rational voting behavior.
politics  polisci  2016-election  government  demographics  data  analysis  social-choice  democracy  trump  clinton  education  org:data  elections  egalitarianism-hierarchy  wonkish  antidemos  class  coalitions  postmortem  general-survey  knowledge  race  class-warfare  poll  values  distribution  multi  obama  news  org:mag  org:popup  biases  sampling-bias  survey  links  study  summary  rationality  epistemic  psychology  social-psych  expert  scitariat  identity-politics  science  social-science  westminster  truth  gender  gender-diff  labor  housing  economics  micro  markets  supply-demand  descriptive  sociology  expert-experience 
january 2017 by nhaliday
The Predictive Validity of Ideal Partner Preferences: A Review and Meta-Analysis
[A] new meta-analysis spanning the attraction and relationships literatures (k=97) revealed that physical attractiveness predicted romantic evaluations with a moderate-to-strong effect size (r = ~.40) for both sexes, and earning prospects predicted romantic evaluations with a small effect size (r = ~.10) for both sexes. Sex differences in the correlations were small (r_difference = .03) and uniformly nonsignificant.

Mating markets and bargaining hands: Mate preferences for attractiveness and resources in two national U.S. studies: http://www.sciencedirect.com.sci-hub.tw/science/article/pii/S0191886915005462

Assessing Female Mate Preferences: Answers to Ten Common Criticisms of Evolutionary Psychology: https://areomagazine.com/2017/08/09/assessing-female-mate-preferences-answers-to-ten-common-criticisms-of-evolutionary-psychology/

How Important is Physical Attractiveness in the Marriage Market: http://www.reis.cis.es/REIS/PDF/REIS_159_07_ENGLISH1499424514902.pdf
For men, the results show that being unattractive decreases the likelihood of finding a partner, of finding a partner with a university degree, and of finding a partner with a higher educational level. For women, physical attractiveness does not affect the likelihood of any of those events occurring. The study has also found out that physical attractiveness has more of an influence on people with a working class background to find a partner with higher educational attainment. These results are unexpected and pose a challenge to the theory of erotic capital.
pdf  study  meta-analysis  psychology  social-psych  evopsych  gender  values  sex  🐝  compensation  science-anxiety  gender-diff  chart  solid-study  multi  piracy  twitter  social  commentary  gnon  🐸  embodied  money  status  null-result  data  poll  properties  survey  summary  scitariat  expert  org:mag  news  sociology  anthropology  class  human-capital  :/  attaq  stylized-facts  hmm  expert-experience  theory-of-mind 
january 2017 by nhaliday
Contra NYT On Economists On Education | Slate Star Codex
Noah Smith being a right prick here.

36% of economists believe vouchers would improve education, compared to 19% who disagree. The rest are unsure or didn’t answer the question. The picture looks about the same when weighted by the economists’ confidence.

The correct way to report on this graph is “About twice as many economists believe a voucher system would improve education as believe that it wouldn’t.”

By leaving it at “only a third of economists support vouchers”, the article strongly implies that there is an economic consensus against the policy. But its own source suggests that, of economists who have an opinion, a big majority are pro-voucher.

(note also that the options are only “vouchers will improve education” and “vouchers will not improve education”, so that it’s unclear from the data if any dissenting economists agree with the reporter’s position that vouchers will make things worse. They might just think that things would stay the same.)

I think this is journalistic malpractice.
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december 2016 by nhaliday
Expert credibility in climate change
Here, we use an extensive dataset of 1,372 climate researchers and their publication and citation data to show that (i) 97–98% of the climate researchers most actively publishing in the field surveyed here support the tenets of ACC outlined by the Intergovernmental Panel on Climate Change, and (ii) the relative climate expertise and scientific prominence of the researchers unconvinced of ACC are substantially below that of the convinced researchers.
study  data  poll  expert  science  environment  meta:science  culture-war  org:nat  descriptive  epistemic  climate-change  heuristic  info-dynamics  expert-experience 
december 2016 by nhaliday
What do IQ researchers really think about the Flynn Effect? - The Unz Review
Poor countries are predicted to keep raising their game, and their intellects, richer countries less so. The USA is the only country predicted to decline in ability, presumably because of mass migration. The real experts take an even more jaundiced view, and hold out little hope for The West. These predictions will be partly testable within one generation, so pin this table to your study notice board, and test for goodness of fit in 2040.

The FLynn Effect to 2100: http://www.unz.com/akarlin/the-flynn-effect-to-2100/
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december 2016 by nhaliday
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