nhaliday + expectancy   31

Measures of cultural distance - Marginal REVOLUTION
A new paper with many authors — most prominently Joseph Henrich — tries to measure the cultural gaps between different countries.  I am reproducing a few of their results (see pp.36-37 for more), noting that higher numbers represent higher gaps:

...

Overall the numbers show much greater cultural distance of other nations from China than from the United States, a significant and under-discussed problem for China. For instance, the United States is about as culturally close to Hong Kong as China is.

[ed.: Japan is closer to the US than China. Interesting. I'd like to see some data based on something other than self-reported values though.]

the study:
Beyond WEIRD Psychology: Measuring and Mapping Scales of Cultural and Psychological Distance: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3259613
We present a new tool that provides a means to measure the psychological and cultural distance between two societies and create a distance scale with any population as the point of comparison. Since psychological data is dominated by samples drawn from the United States or other WEIRD nations, this tool provides a “WEIRD scale” to assist researchers in systematically extending the existing database of psychological phenomena to more diverse and globally representative samples. As the extreme WEIRDness of the literature begins to dissolve, the tool will become more useful for designing, planning, and justifying a wide range of comparative psychological projects. We have made our code available and developed an online application for creating other scales (including the “Sino scale” also presented in this paper). We discuss regional diversity within nations showing the relative homogeneity of the United States. Finally, we use these scales to predict various psychological outcomes.
econotariat  marginal-rev  henrich  commentary  study  summary  list  data  measure  metrics  similarity  culture  cultural-dynamics  sociology  things  world  usa  anglo  anglosphere  china  asia  japan  sinosphere  russia  developing-world  canada  latin-america  MENA  europe  eastern-europe  germanic  comparison  great-powers  thucydides  foreign-policy  the-great-west-whale  generalization  anthropology  within-group  homo-hetero  moments  exploratory  phalanges  the-bones  🎩  🌞  broad-econ  cocktail  n-factor  measurement  expectancy  distribution  self-report  values  expression-survival  uniqueness 
12 weeks ago by nhaliday
Altruism in a volatile world | Nature
The evolution of altruism—costly self-sacrifice in the service of others—has puzzled biologists1 since The Origin of Species. For half a century, attempts to understand altruism have developed around the concept that altruists may help relatives to have extra offspring in order to spread shared genes2. This theory—known as inclusive fitness—is founded on a simple inequality termed Hamilton’s rule2. However, explanations of altruism have typically not considered the stochasticity of natural environments, which will not necessarily favour genotypes that produce the greatest average reproductive success3,4. Moreover, empirical data across many taxa reveal associations between altruism and environmental stochasticity5,6,7,8, a pattern not predicted by standard interpretations of Hamilton’s rule. Here we derive Hamilton’s rule with explicit stochasticity, leading to new predictions about the evolution of altruism. We show that altruists can increase the long-term success of their genotype by reducing the temporal variability in the number of offspring produced by their relatives. Consequently, costly altruism can evolve even if it has a net negative effect on the average reproductive success of related recipients. The selective pressure on volatility-suppressing altruism is proportional to the coefficient of variation in population fitness, and is therefore diminished by its own success. Our results formalize the hitherto elusive link between bet-hedging and altruism4,9,10,11, and reveal missing fitness effects in the evolution of animal societies.
study  bio  evolution  altruism  kinship  stylized-facts  models  intricacy  random  signal-noise  time  order-disorder  org:nat  EGT  cooperate-defect  population-genetics  moments  expectancy  multiplicative  additive 
march 2018 by nhaliday
Centers of gravity in non-uniform fields - Wikipedia
In physics, a center of gravity of a material body is a point that may be used for a summary description of gravitational interactions. In a uniform gravitational field, the center of mass serves as the center of gravity. This is a very good approximation for smaller bodies near the surface of Earth, so there is no practical need to distinguish "center of gravity" from "center of mass" in most applications, such as engineering and medicine.

In a non-uniform field, gravitational effects such as potential energy, force, and torque can no longer be calculated using the center of mass alone. In particular, a non-uniform gravitational field can produce a torque on an object, even about an axis through the center of mass. The center of gravity seeks to explain this effect. Formally, a center of gravity is an application point of the resultant gravitational force on the body. Such a point may not exist, and if it exists, it is not unique. One can further define a unique center of gravity by approximating the field as either parallel or spherically symmetric.

The concept of a center of gravity as distinct from the center of mass is rarely used in applications, even in celestial mechanics, where non-uniform fields are important. Since the center of gravity depends on the external field, its motion is harder to determine than the motion of the center of mass. The common method to deal with gravitational torques is a field theory.
nibble  wiki  reference  physics  mechanics  intricacy  atoms  expectancy  spatial  direction  ground-up  concept  existence  uniqueness  homo-hetero  gravity  gotchas 
september 2017 by nhaliday
Atrocity statistics from the Roman Era
Christian Martyrs [make link]
Gibbon, Decline & Fall v.2 ch.XVI: < 2,000 k. under Roman persecution.
Ludwig Hertling ("Die Zahl de Märtyrer bis 313", 1944) estimated 100,000 Christians killed between 30 and 313 CE. (cited -- unfavorably -- by David Henige, Numbers From Nowhere, 1998)
Catholic Encyclopedia, "Martyr": number of Christian martyrs under the Romans unknown, unknowable. Origen says not many. Eusebius says thousands.

...

General population decline during The Fall of Rome: 7,000,000 [make link]
- Colin McEvedy, The New Penguin Atlas of Medieval History (1992)
- From 2nd Century CE to 4th Century CE: Empire's population declined from 45M to 36M [i.e. 9M]
- From 400 CE to 600 CE: Empire's population declined by 20% [i.e. 7.2M]
- Paul Bairoch, Cities and economic development: from the dawn of history to the present, p.111
- "The population of Europe except Russia, then, having apparently reached a high point of some 40-55 million people by the start of the third century [ca.200 C.E.], seems to have fallen by the year 500 to about 30-40 million, bottoming out at about 20-35 million around 600." [i.e. ca.20M]
- Francois Crouzet, A History of the European Economy, 1000-2000 (University Press of Virginia: 2001) p.1.
- "The population of Europe (west of the Urals) in c. AD 200 has been estimated at 36 million; by 600, it had fallen to 26 million; another estimate (excluding ‘Russia’) gives a more drastic fall, from 44 to 22 million." [i.e. 10M or 22M]

also:
The geometric mean of these two extremes would come to 4½ per day, which is a credible daily rate for the really bad years.

why geometric mean? can you get it as the MLE given min{X1, ..., Xn} and max{X1, ..., Xn} for {X_i} iid Poissons? some kinda limit? think it might just be a rule of thumb.

yeah, it's a rule of thumb. found it it his book (epub).
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september 2017 by nhaliday
Harmonic mean - Wikipedia
The harmonic mean is a Schur-concave function, and dominated by the minimum of its arguments, in the sense that for any positive set of arguments, {\displaystyle \min(x_{1}\ldots x_{n})\leq H(x_{1}\ldots x_{n})\leq n\min(x_{1}\ldots x_{n})} . Thus, the harmonic mean cannot be made arbitrarily large by changing some values to bigger ones (while having at least one value unchanged).

more generally, for the weighted mean w/ Pr(x_i)=t_i, H(x1,...,xn) <= x_i/t_i
nibble  math  properties  estimate  concept  definition  wiki  reference  extrema  magnitude  expectancy  metrics  ground-up 
july 2017 by nhaliday
Distribution of Word Lengths in Various Languages - Ravi Parikh's Website
Note that this visualization isn't normalized based on usage. For example the English word 'the' is used frequently, while the word 'lugubrious' is rarely used; however both words count the same in computing the histogram and average word lengths. A great idea for a follow-up would be to use language corpuses instead of word lists in order to build these histograms.
techtariat  data  visualization  project  anglo  language  foreign-lang  distribution  expectancy  measure  lexical 
june 2017 by nhaliday
[1705.03394] That is not dead which can eternal lie: the aestivation hypothesis for resolving Fermi's paradox
If a civilization wants to maximize computation it appears rational to aestivate until the far future in order to exploit the low temperature environment: this can produce a 10^30 multiplier of achievable computation. We hence suggest the "aestivation hypothesis": the reason we are not observing manifestations of alien civilizations is that they are currently (mostly) inactive, patiently waiting for future cosmic eras. This paper analyzes the assumptions going into the hypothesis and how physical law and observational evidence constrain the motivations of aliens compatible with the hypothesis.

http://aleph.se/andart2/space/the-aestivation-hypothesis-popular-outline-and-faq/

simpler explanation (just different math for Drake equation):
Dissolving the Fermi Paradox: http://www.jodrellbank.manchester.ac.uk/media/eps/jodrell-bank-centre-for-astrophysics/news-and-events/2017/uksrn-slides/Anders-Sandberg---Dissolving-Fermi-Paradox-UKSRN.pdf
http://marginalrevolution.com/marginalrevolution/2017/07/fermi-paradox-resolved.html
Overall the argument is that point estimates should not be shoved into a Drake equation and then multiplied by each, as that requires excess certainty and masks much of the ambiguity of our knowledge about the distributions. Instead, a Bayesian approach should be used, after which the fate of humanity looks much better. Here is one part of the presentation:

Life Versus Dark Energy: How An Advanced Civilization Could Resist the Accelerating Expansion of the Universe: https://arxiv.org/abs/1806.05203
The presence of dark energy in our universe is causing space to expand at an accelerating rate. As a result, over the next approximately 100 billion years, all stars residing beyond the Local Group will fall beyond the cosmic horizon and become not only unobservable, but entirely inaccessible, thus limiting how much energy could one day be extracted from them. Here, we consider the likely response of a highly advanced civilization to this situation. In particular, we argue that in order to maximize its access to useable energy, a sufficiently advanced civilization would chose to expand rapidly outward, build Dyson Spheres or similar structures around encountered stars, and use the energy that is harnessed to accelerate those stars away from the approaching horizon and toward the center of the civilization. We find that such efforts will be most effective for stars with masses in the range of M∼(0.2−1)M⊙, and could lead to the harvesting of stars within a region extending out to several tens of Mpc in radius, potentially increasing the total amount of energy that is available to a future civilization by a factor of several thousand. We also discuss the observable signatures of a civilization elsewhere in the universe that is currently in this state of stellar harvesting.
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may 2017 by nhaliday
Unlearning descriptive statistics | Hacker News
For readers who are OK with some math, I recommend John Myles White's eye-opening post about means, medians, and modes: http://www.johnmyleswhite.com/notebook/2013/03/22/modes-medians-and-means-an-unifying-perspective/. He describes these summary descriptive stats in terms of what penalty function they minimize: mean minimizes L2, median minimizes L1, mode minimizes L0.
hn  commentary  techtariat  acmtariat  data-science  explanation  multi  norms  org:bleg  nibble  scitariat  expectancy  traces 
february 2017 by nhaliday
Wald's equation - Wikipedia
important identity that simplifies the calculation of the expected value of the sum of a random number of random quantities
math  levers  probability  wiki  reference  nibble  expectancy  identity 
january 2017 by nhaliday
A Rejection of 'Broken Windows Policing' Over Race Actually Hurts Minority Neighborhoods | Manhattan Institute
https://twitter.com/BookOfTamara/status/778838226983268352
https://archive.is/ETLXf
Late-night slightly controversial criminal justice thread:

Proactive policing and crime control: https://www.nature.com/articles/s41562-017-0227-x
Evidence that curtailing proactive policing can reduce major crime: https://www.nature.com/articles/s41562-017-0211-5

Proactive Policing: Effects on Crime and Communities: http://sites.nationalacademies.org/dbasse/claj/proactive-policing/index.htm
This report from the Committee on Law and Justice finds evidence that a number of proactive policing practices are successful in reducing crime and disorder, at least in the short term, and that most of these strategies do not harm communities’ attitudes towards police.

Is Racial Profiling a Legitimate Strategy in the Fight against Violent Crime?: https://link.springer.com/epdf/10.1007/s11406-018-9945-1?author_access_token=nDM1xCesybebx7yUX2BxZ_e4RwlQNchNByi7wbcMAY6py69jTlOiEGDIgqW0Vv2HrAor6wlMLH695I2ykTiKUxf1RBnu1u_6gjXU-6vgh2gIy6CX2npHD9GR350T20x_TbCcq4MmJUPrxAqsJSe1QA%3D%3D
- Neven Sesardić

Are U.S. Cities Underpoliced?: http://marginalrevolution.com/marginalrevolution/2017/08/u-s-cities-underpoliced.html
Chalfin and McCrary acknowledge the endogeneity problem but they suggest that a more important reason why ordinary regression gives you poor results is that the number of police is poorly measured. Suppose the number of police jumps up and down in the data even when the true number stays constant. Fake variation obviously can’t influence real crime so when your regression “sees” a lot of (fake) variation in police which is not associated with variation in crime it’s naturally going to conclude that the effect of police on crime is small, i.e. attenuation bias.

By comparing two different measures of the number of police, Chalfin and McCrary show that a surprising amount of the ups and downs in the number of police is measurement error. Using their two measures, however, Chalfin and McCrary produce a third measure which is better than either alone. Using this cleaned-up estimate, they find that ordinary regression (with controls) gives you estimates of the effect of police on crime which are plausible and similar to those found using other techniques like natural experiments. Chalfin and McCrary’s estimates, however, are more precise since they use much more of the variation in the data.

Using these new estimates of the effect of police and crime along with estimates of the social cost of crime they conclude (as I have argued before) that U.S. cities are substantially under-policed.

Crime Imprisons and Kills: http://marginalrevolution.com/marginalrevolution/2018/01/crime-imprisons-kills.html
…The everyday lived experience of urban poverty has also been transformed. Analyzing rates of violent victimization over time, I found that the poorest Americans today are victimized at about the same rate as the richest Americans were at the start of the 1990s. That means that a poor, unemployed city resident walking the streets of an average city today has about the same chance of being robbed, beaten up, stabbed or shot as a well-off urbanite in 1993. Living in poverty used to mean living with the constant threat of violence. In most of the country, that is no longer true.

http://marginalrevolution.com/marginalrevolution/2015/09/what-was-gary-beckers-biggest-mistake.html
http://www.sentencingproject.org/wp-content/uploads/2016/01/Deterrence-in-Criminal-Justice.pdf
Do parole abolition and Truth-in-Sentencing deter violent crimes in Virginia?: http://link.springer.com.sci-hub.tw/article/10.1007/s00181-017-1332-4

Death penalty: https://offsettingbehaviour.blogspot.com/2011/09/death-penalty.html
And so I revise: the death penalty is wrong, and it also likely has little measurable deterrent effect. There may still be a deterrent effect; we just can't show one given available data.

The effects of DNA databases on the deterrence and detection of offenders: http://jenniferdoleac.com/wp-content/uploads/2015/03/DNA_Denmark.pdf
We exploit a large expansion of Denmark’s DNA database in 2005 to measure the effect of DNA registration on criminal behavior. Using a regression discontinuity strategy, we find that DNA registration reduces recidivism by 43%. Using rich data on the timing of subsequent charges to separate the deterrence and detection effects of DNA databases, we also find that DNA registration increases the probability that repeat offenders get caught, by 4%. We estimate an elasticity of criminal behavior with respect to the probability of detection to be -1.7. We also find suggestive evidence that DNA profiling changes non-criminal behavior: offenders added to the DNA database are more likely to get married, remain in a stable relationship, and live with their children.

Short- and long-term effects of imprisonment on future felony convictions and prison admissions: http://www.pnas.org/content/early/2017/09/26/1701544114.short
https://twitter.com/bswud/status/917354893907779585
Prison isn't criminogenic—offenders have higher rates of re-incarceration because of technical parole violations
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january 2017 by nhaliday
Bounds on the Expectation of the Maximum of Samples from a Gaussian
σ/sqrt(pi log 2) sqrt(log n) <= E[Y] <= σ sqrt(2) sqrt(log n)

upper bound pf: Jensen's inequality+mgf+union bound+choose optimal t (Chernoff bound basically)
lower bound pf: more ad-hoc (and difficult)
pdf  tidbits  math  probability  concentration-of-measure  estimate  acm  tails  distribution  calculation  iidness  orders  magnitude  extrema  tightness  outliers  expectancy  proofs  elegance 
october 2016 by nhaliday

bundles : abstractmath

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