Critiques

• moxibustion mouse study https://plus.google.com/103530621949492999968/posts/TisYM64ckLM
• criticism of teeth-removal experiment in rats http://lesswrong.com/r/discussion/lw/kfb/open_thread_30_june_2014_6_july_2014/b1u3
• criticism of small Noopept self-experiment http://www.bluelight.org/vb/threads/689936-My-Paper-quot-Noopept-amp-The-Placebo-Effect-quot?p=11910708&viewfull=1#post11910708
• why Soylent is not a good idea http://lesswrong.com/lw/hht/link_soylent_crowdfunding/90y7
• misinterpretation of fluoridation meta-analysis and ignorance of VoI http://theness.com/neurologicablog/index.php/anti-fluoride-propaganda-as-news/#comment-76400
• http://lesswrong.com/lw/1lt/case_study_melatonin/8mgf
• Fulltext: https://dl.dropboxusercontent.com/u/280585369/2014-dubal.pdf is this possible? http://nextbigfuture.com/2014/05/kl-vs-gene-makes-up-six-iq-points-of.html#comment-1376748788 http://www.reddit.com/r/Nootropics/comments/25233r/boost_your_iq_by_6_points/chddd7f
• Facebook emotion study: http://www.reddit.com/r/psychology/comments/29vg9j/no_emotions_arent_really_contagious_over_facebook/cip7ln5 https://plus.google.com/103530621949492999968/posts/1PqPdLyzXhn
• tACS causes lucid dreaming: http://www.reddit.com/r/LucidDreaming/comments/27y7n6/no_brain_stimulation_will_not_get_you_lucid/ck6isgo
• Herbalife growth patterns: http://www.reddit.com/r/business/comments/24aoo2/what_unsustainable_growth_looks_like_herbalife/ch5hwtv
• Plausible correlate of Fairtrade: http://www.reddit.com/r/Economics/comments/26jb2d/surprise_fairtrade_doesnt_benefit_the_poor/chrx9s4
• slave whippings vs cotton production http://lesswrong.com/r/discussion/lw/kwc/open_thread_sept_17_2014/bajv
• whether a study on mental illness & violence shows schizophrenics are not more likely to murder but rather be murdered: http://www.reddit.com/r/psychology/comments/2fwjs8/people_with_mental_illness_are_more_likely_to_be/ckdq50k / http://www.nationalelfservice.net/publication-types/observational-study/people-with-mental-illness-are-more-likely-to-be-victims-of-homicide-than-perpetrators-of-homicide/#comment-95507 (see also http://slatestarscratchpad.tumblr.com/post/120950150581/psycholar-giraffepoliceforce-museicetc )
• Fortune analysis of higher female CEO returns http://lesswrong.com/r/discussion/lw/l3b/contrarian_lw_views_and_their_economic/bftw
• failed attempt at estimating P(causation|correlation) https://plus.google.com/103530621949492999968/posts/UzMMmPgyyaV
• algae/IQ: http://lesswrong.com/r/discussion/lw/l9v/open_thread_nov_17_nov_23_2014/bm7o
• synaesthesia/IQ: https://www.reddit.com/r/psychology/comments/2mryte/surprising_iq_boost_12_in_average_by_a_training/cm760v8
• misinterpretation: http://slatestarcodex.com/2014/12/08/links-1214-come-ye-to-bethlinkhem/#comment-165197
• underpowered/multiple-correction jobs program: http://slatestarcodex.com/2014/12/08/links-1214-come-ye-to-bethlinkhem/#comment-165197
• vitamin D/caffeine claim based on weak in vitro claims, inconsistent with more relevant in vivo results: https://plus.google.com/u/0/103530621949492999968/posts/AUg3udezXMS [already included in Nootropics.page]
• claimed fall in digit span backwards minuscule and non-statistically-significant, no evidence of heterogeneity beyond variability due to sample size http://drjamesthompson.blogspot.com/2015/04/digit-span-bombshell.html?showComment=1428096775425#c4097303932864318518
• Claimed randomized experiment of whether sushi tastes worse after freezing is not actually a randomized experiment https://www.reddit.com/r/science/comments/324xmf/randomized_doubleblind_study_shows_the_quality_of/cq8dmsb

• aerobic vs weightlifting exercise, multiple problems but primarily p-hacking, difference-in-statistical-significance-is-not-a-significant-difference, and controlling for intermediate variable: https://plus.google.com/103530621949492999968/posts/aeZqB8JWUiQ

  • another controlling for intermediate variable: https://twitter.com/gwern/status/538763784048095232
• sexual openness result undermined by ceiling effect http://mindhacks.com/2015/04/28/when-society-isnt-judging-womens-sex-drive-rivals-mens/#comment-362749
• music study claiming WM interaction: possible ceiling effect? see FB PM
• attempt to measure effect of Nazi anti-schizophrenia eugenics program failed to use breeder’s equation to estimate possible size of effect, which is too small to detect with available data and hence attempt is foredoomed: https://www.reddit.com/r/eugenics/comments/3hqdll/between_73_and_100_of_all_individuals_with/cul2nzw
• claim high IQ types almost 100% failure rates due to inappropriate model assumption of normal distribution + narrow standard deviation: http://polymatharchives.blogspot.com/2015/01/the-inappropriately-excluded.html?showComment=1441741719623#c1407914596750199739
• implausible claims about success rate of facial recognition applied to St Petersburg population: https://news.ycombinator.com/item?id=11491264 (see also Facial recognition systems stumble when confronted with million-face database)
• human Toxoplasma gondii study is not well-powered as authors claim due to incorrect power analysis, and results are evidence for harm: http://blogs.discovermagazine.com/neuroskeptic/2016/02/20/myth-mind-altering-parasite-toxoplasma-gondii/#comment-2755778490 ; https://www.reddit.com/r/slatestarcodex/comments/5vjrmo/toxoplasma_doesnt_cause_adolescent_psychosis/de2x4kh/
• attempt at attributing Bitcoin price increases to technology improvements: https://www.reddit.com/r/Bitcoin/comments/5tbt8f/buzz_factor_or_innovation_potential_what_explains/ddlmzrz/

• analysis of designer drug/research chemical activity on Wikipedia is driven almost entirely by editing patterns of just 2 Wikipedia editors particularly interested in the topic: http://calib.ro/chemical-wiki/explorations/2016-09-12-emcdda-watchlist-and-wikipedia-timeline#comment-3277669328

Someone Should Do Something: Wishlist of Miscellaneous Project Ideas

• Erowid: data-mine the trip reports to create clusters or a state-space of drug effects/results and using the clusters & common terms, create a general inventory of descriptions; add this to the trip report form so Erowid users can provide some more structured information about their experience.

• Dark net markets: use a longitudinal crawl of DNM sellers to estimate survival curves, outstanding escrow + orders, and listed product prices / type / language to try to predict exit scams.

• Dark net markets: parse hyperlinks in all forums & DNMs and outlinked domains like Reddit’s /r/darknetmarkets in order to create a proper network topology of Tor hidden services - existing .onion crawls tend to find Tor hidden services are a very fragmented and unconnected network despite the Hidden Wikis, but this may reflect the instability of hidden services and the use of clearnet communities & indexes (like Reddit or gwern.net)

• read through the Extropy and SL4 mailing list archives: have any important ideas been forgotten or dropped? Are there any meta lessons about forecasting or thinking about existential risk to be gleaned?

• an efficient hashmap library for R, perhaps binding to a C/C++ library; existing R hashmaps are based on environments and not very fast

  • key for enabling many statistical and especially reinforcement learning algorithms like Monte Carlo tree search (MCTS)
• Markov chain/char-RNN bot for Twitter trained on just English proverbs, idioms, and expressions
• user-friendly char-RNN implementation just for classifying text, taking in CSV data of text/category
• RL agent using MCTS + GAN/PixelCNN model of environment
• hyperparameter optimization for algorithms in problems without available loss functions but human-judgeable quality, using a human for making choices in a paired or forced-choice comparison, then using a Bradley-Terry or latent variable model to infer rankings of hyperparameter settings and optimizing based on the latent scores

• deep RL for neural network design but focusing on generating a distribution of random weights for initializing a NN; better initializations have proven to be extremely important in stably training NN and simply tweaking initialization can train NNs with hundreds of layers (previously impossible, then only possible with a major architectural innovation like residual networks) eg Balduzzi et al 2017. Better initializations are hard to design by hand as they apparently work by breaking various symmetries inside the NN, so this is a problem that is well suited for brute force and trial-and-error. See further Saxe et al 2013, Krähenbühl et al 2015, Daniely et al 2016, Schoenholz et al 2016, Kumar 2017, Aghajanyan 2017.

• a VR application for viewing stereoscopic images & video and for 3D environments with extremely large parallax such as for viewing clouds with true depth perception (discussion)

• properly transcribe & annotate Douglas Hofstader’s Le Ton Beau de Marot, one of his best but also most obscure books

• provide polygenic scores as a service, a website/API where one can upload a SNP data file like the 23andMe export and get back PGSes for everything in LD Hub, and utility weights
• track down & upload missing papers in http://arthurjensen.net/?page_id=9

• expand/rewrite Wikipedia’s heritability of intelligence - grossly outdated, almost totally omitting all the GCTAs and GWASes that have definitively settled the answer in the strongly affirmative

Estimating censored test scores

An acquaintance asks the following question: he is applying for a university course which requires a certain minimum score on a test for admittance, and wonders about his chances and a possible trend of increasing minimum scores over time. (He hasn’t received his test results yet.) The university doesn’t provide a distribution of admittee scores, but it does provide the minimum scores for 2005-2013, unless all applicants were admitted because they all scored above an unknown cutoff - in which case it provides no minimum score. This leads to the dataset:

2005,NA
2006,410
2007,NA
2008,NA
2009,398
2010,407
2011,417
2012,NA
2013,NA

A quick eyeball tells us that we can’t conclude much: only 4 actual datapoints, with 5 hidden from us. We can’t hope to conclude anything about time trends, other than there doesn’t seem to be much of one: the last score, 417, is not much higher than 410, and the last two scores are low enough to be hidden. We might be able to estimate a mean, though.

We can’t simply average the 4 scores and conclude the mean minimum is 410 because of those NAs: a number of scores have been censored because they were too low, and while we don’t know what they were, we do know they were <398 (the smallest score) and so a bunch of <398s will pull down the uncensored mean of 410.

On approach is to treat it as a Tobit model and estimate using something like the censReg library (overview).

But if we try a quick call to censReg, we are confounded: a Tobit model expects you to provide the cutoff below which the observations were censored, but that is something we don’t know. All we know is that it must be below 398, we weren’t told it was exactly 395, 394, etc. Fortunately, this is a solved problem. For example: The Tobit model with a non-zero threshold, Carson & Sun 2007 tells us:

In this paper, we consider estimating the unknown censoring threshold by the minimum of the uncensored $y_i$’s. We show that the estimator $γ'$ of $γ$ is superconsistent and asymptotically exponentially distributed. Carson (1988, 1989) also suggests estimating the unknown censoring threshold by the minimum of the uncensored $y_i$’s. In a recent paper, Zuehlke (2003) rediscovers these unpublished results and demonstrates via simulations that the asymptotic distribution of the maximum likelihood estimator does not seem to be affected by the estimation of the censoring threshold.

That seems to be almost too simple and easy, but it makes sense and reminds me a little of the German tank problem: the minimum might not be that accurate a guess (it’s unlikely you just happened to draw a sample right on the censoring threshold) and it definitely can’t be wrong in the sense of being too low. (A Bayesian method might be able to do better with a prior like a exponential.)

With that settled, the analysis is straightforward: load the data, figure out the minimum score, set the NAs to 0, regress, and extract the model estimates for each year:

scores <- data.frame(Year=2005:2013,
                     MinimumScore=c(NA,410,NA,NA,398,407,417,NA,NA));
censorThreshold <- min(scores$MinimumScore, na.rm=T)
scores[is.na(scores)] <- 0

library(censReg)
# 'censorThreshold-1' because censReg seems to treat threshold as < and not <=
summary(censReg(MinimumScore ~ Year, left=censorThreshold-1, data=scores))
# Warning message:
# In censReg(MinimumScore ~ Year, left = censorThreshold - 1, data = scores) :
#   at least one value of the endogenous variable is smaller than the left limit
#
# Call:
# censReg(formula = MinimumScore ~ Year, left = censorThreshold -
#     1, data = scores)
#
# Observations:
#          Total  Left-censored     Uncensored Right-censored
#              9              5              4              0
#
# Coefficients:
#              Estimate Std. error t value Pr(> t)
# (Intercept) -139.9711        Inf       0       1
# Year           0.2666        Inf       0       1
# logSigma       2.6020        Inf       0       1
#
# Newton-Raphson maximisation, 37 iterations
# Return code 1: gradient close to zero
# Log-likelihood: -19.35 on 3 Df
-139.9711 + (0.2666 * scores$Year)
# [1] 394.6 394.8 395.1 395.4 395.6 395.9 396.2 396.4 396.7

With so little data the results aren’t very reliable, but there is one observation we can make.

The fact that half the dataset is censored tells us that the uncensored mean may be a huge overestimate (since we’re only looking at the top half of the underlying data), and indeed it is. The original mean of the uncensored scores was 410; however, the estimate including the censored data is much lower, 397 (13 less)!

This demonstrates the danger of ignoring systematic biases in your data.

So, trying to calculate a mean or time effect is not helpful. What might be better is to instead exploit the censoring directly: if the censoring happened because everyone got in, then if you showed up in a censored year, you have 100% chance of getting in; while in a non-censored year you have an unknown but <100% chance of getting in; so the probability of a censored year sets a lower bound on one’s chances, and this is easy to calculate as a simple binomial problem - 5 out of 9 years were censored years, so:

binom.test(c(5,4))
#
#   Exact binomial test
#
# data:  c(5, 4)
# number of successes = 5, number of trials = 9, p-value = 1
# alternative hypothesis: true probability of success is not equal to 0.5
# 95% confidence interval:
#  0.212 0.863
# sample estimates:
# probability of success
#                 0.5556

So we can tell him that he may have a >55% chance of getting in.

The Traveling Gerontologist problem

A quick probability exercise: Wikipedia mentions Finland has 566 centenarians as of 2010.

That’s few enough you could imagine visiting them all to research them and their longevity, in a sort of traveling salesman problem but with gerontologists instead. Except, because of the exponential increase in mortality, centenarians have high annual mortality rates; it depends on the exact age but you could call it >30% (eg Finnish 99yos in 2012 had a death toll of 326.54/1000). So you might well try to visit a centenarian and discover they’d died before you got there.

How bad a risk is this? Well, if the risk per year is 30%, then one has a 70% chance of surviving a year. To survive a year, you must survive all 365 days; by the multiplication rule, the risk is $x$ where $0.7 = x \cdot x \cdot x \cdot ... * x \text{[365.25 times]}$ or $0.7 = x^{365.25}$; solving, $x = 0.999024$.

It takes time to visit a centenarian - it wouldn’t do to be abrupt and see them for only a few minutes, you ought to listen to their stories, and you need to get to a hotel or airport, so let’s assume you visit 1 centenarian per day.

If you visit centenarian A on day 1, and you want to visit centenarian B on day 2, then you can count on a 99.9% chance B is still alive. So far so good. And if you wanted to visit 566 centenarians (let’s imagine you have a regularly-updated master list of centenarians from the Finnish population registry), then you only have to beat the odds 566 times in a row, which is not that hard: $0.999024^{566} = 0.5754023437943274$.

But that’s coldblooded of you to objectify those Finnish centenarians! Any centenarian will do, I don’t care. What if you picked the current set of 566 centenarians and wanted to visit just them, specifically - with no new centenarians introduced to the list to replace any dead ones.

That’s a little more complicated. When you visit the first centenarian, it’s the same probability: 0.999024. When you visit the second centenarian the odds change since now she (and it’s more often she than he, since remember the exponential and males having shorter mean lifetimes) has to survive 2 days, so it’s $0.999024 \cdot 0.999024$ or $0.999024^2$; for the third, it’s $0.999024^3$, and so on to #566 who has been patiently waiting and trying to survive a risk of $0.999024^566$, and then you need to multiply to get your odds of beating every single risk of death and the centenarian not leaving for a more permanent rendezvous: $0.999024 \cdot 0.999024^2 \cdot 0.999024^3 \cdot ... \cdot 0.999024^{566}$, which would be $\prod_{n=1}^{566} 0.999024^n$, or in Haskell:

product (map (\x -> 0.999024**x) [1..566])
~> 8.952743340164081e-69

(A little surprisingly, Wolfram Alpha can solve the TeX expression too.)

Given the use of floating point in that function (567 floating point exponentiations followed by as many multiplications) and the horror stories about floating point, one might worry the answer is wrong & the real probability is much larger. We can retry with an implementation of computable reals, CReal, which can be very slow but should give more precise answers:

:module + Data.Number.CReal
showCReal 100 (product (map (\x -> 0.999024**x) [1..566]))
~> 0.0000000000000000000000000000000000000000000000000000000000000000000089527433401308585720915431195262

Looks good - agrees with the floating point version up to the 11th digit:

8.9527433401 64081e-69
8.9527433401 308585720915431195262

We can also check by rewriting the product equation to avoid all the exponentiation and multiplication (which might cause issues) in favor of a single exponential:

1. $p^1 * p^2 * ... p^n$ (as before)
2. = $p^{1+2+...+n}$ (since $(x^m) * (x^n) = x^(m + n)$)
3. = $p^{\frac{n \cdot (1 + n)}{2}}$ (by arithmetic progression/Gauss’s famous classroom trick since $\sum_1^n = n \cdot \frac{a_1 + a_n}{2}$)
4. = $0.999024^{\frac{566 \cdot (1 + 566)}{2}}$ (start substituting in specific values)
5. = $0.999024^{\frac{320922}{2}}$
6. = $0.999024^{160461}$

So:

0.999024^160461
~> 8.95274334014924e-69

Or to go back to the longer version:

0.999024**((566*(1 + 566)) / 2)
~> 8.952743340164096e-69

Also close. All probabilities of success are minute.

How fast would you have to be if you wanted to at least try to accomplish the tour with, say, a 50-50 chance?

Well, that’s easy: you can consider the probability of all of them surviving one day and as we saw earlier, that’s $0.999024^{566} = 0.58$, and two days would be $(0.999024 ^ {566}) ^ 2 = 0.33$ So you can only take a little over a day before you’ve probabilistically lost & one of them has died; if you hit all 566 centenarians in 24 hours, that’s ~24 centenarians per hour or ~2 minutes to chat with each one and travel to the next. If you’re trying to collect DNA samples, better hope they’re all awake and able to give consent!

So safe to say, you will probably not be able to manage the Traveling Gerontologist’s tour.

Bayes nets

library(bnlearn)
# https://www.dropbox.com/s/4nsrszm85m47272/2015-03-22-gwern-weight.csv
weight <- read.csv("selfexperiment/weight.csv")
weight$Date <- NULL; weight$Weight.scale <- NULL
# remove missing data
weightC <- na.omit(weight)
# bnlearn can't handle integers, oddly enough
weightC <- as.data.frame(sapply(weightC, as.numeric))
summary(weightC)
#   Weight.Omron        Weight.BMI        Weight.body.fat    Weight.muscle
#  Min.   :193.0000   Min.   : 26.90000   Min.   :27.00000   Min.   :32.60000
#  1st Qu.:195.2000   1st Qu.: 27.20000   1st Qu.:28.40000   1st Qu.:34.20000
#  Median :196.4000   Median : 27.40000   Median :28.70000   Median :34.50000
#  Mean   :196.4931   Mean   : 28.95409   Mean   :28.70314   Mean   :34.47296
#  3rd Qu.:197.8000   3rd Qu.: 27.60000   3rd Qu.:29.10000   3rd Qu.:34.70000
#  Max.   :200.6000   Max.   : 28.00000   Max.   :31.70000   Max.   :35.50000
#  Weight.resting.metabolism Weight.body.age    Weight.visceral.fat
#  Min.   :1857.000          Min.   :52.00000   Min.   : 9.000000
#  1st Qu.:1877.000          1st Qu.:53.00000   1st Qu.:10.000000
#  Median :1885.000          Median :53.00000   Median :10.000000
#  Mean   :1885.138          Mean   :53.32704   Mean   : 9.949686
#  3rd Qu.:1893.000          3rd Qu.:54.00000   3rd Qu.:10.000000
#  Max.   :1914.000          Max.   :56.00000   Max.   :11.000000
cor(weightC)
#                             Weight.Omron     Weight.BMI Weight.body.fat  Weight.muscle
# Weight.Omron               1.00000000000  0.98858376919    0.1610643221 -0.06976934825
# Weight.BMI                 0.98858376919  1.00000000000    0.1521872557 -0.06231142104
# Weight.body.fat            0.16106432213  0.15218725566    1.0000000000 -0.98704369855
# Weight.muscle             -0.06976934825 -0.06231142104   -0.9870436985  1.00000000000
# Weight.resting.metabolism  0.96693236051  0.95959140245   -0.0665001241  0.15621294274
# Weight.body.age            0.82581939626  0.81286141659    0.5500409365 -0.47408608681
# Weight.visceral.fat        0.41542744168  0.43260100665    0.2798756916 -0.25076619829
#                           Weight.resting.metabolism Weight.body.age Weight.visceral.fat
# Weight.Omron                           0.9669323605    0.8258193963        0.4154274417
# Weight.BMI                             0.9595914024    0.8128614166        0.4326010067
# Weight.body.fat                       -0.0665001241    0.5500409365        0.2798756916
# Weight.muscle                          0.1562129427   -0.4740860868       -0.2507661983
# Weight.resting.metabolism              1.0000000000    0.7008354776        0.3557229425
# Weight.body.age                        0.7008354776    1.0000000000        0.4840752389
# Weight.visceral.fat                    0.3557229425    0.4840752389        1.0000000000

## create alternate dataset expressing the two percentage variables as pounds, since this might fit better
weightC2 <- weightC
weightC2$Weight.body.fat <- weightC2$Weight.Omron * (weightC2$Weight.body.fat / 100)
weightC2$Weight.muscle   <- weightC2$Weight.Omron * (weightC2$Weight.muscle / 100)

pdap <- hc(weightC)
pdapc2 <- hc(weightC2)
## bigger is better:
score(pdap, weightC)
# [1] -224.2563072
score(pdapc2, weightC2)
# [1] -439.7811072
## stick with the original, then
pdap
#   Bayesian network learned via Score-based methods
#
#   model:
#    [Weight.Omron][Weight.body.fat][Weight.BMI|Weight.Omron]
#    [Weight.resting.metabolism|Weight.Omron:Weight.body.fat]
#    [Weight.body.age|Weight.Omron:Weight.body.fat]
#    [Weight.muscle|Weight.body.fat:Weight.resting.metabolism][Weight.visceral.fat|Weight.body.age]
#   nodes:                                 7
#   arcs:                                  8
#     undirected arcs:                     0
#     directed arcs:                       8
#   average markov blanket size:           2.57
#   average neighbourhood size:            2.29
#   average branching factor:              1.14
#
#   learning algorithm:                    Hill-Climbing
#   score:                                 BIC (Gauss.)
#   penalization coefficient:              2.534452101
#   tests used in the learning procedure:  69
#   optimized:                             TRUE
plot(pdap)
## https://i.imgur.com/nipmqta.png

pdap2 <- hc(weightC, blacklist=data.frame(from=c("Weight.body.age", "Weight.resting.metabolism"), to=c("Weight.visceral.fat","Weight.muscle")))

pdap3 <- hc(weightC, blacklist=data.frame(from=c("Weight.body.age", "Weight.resting.metabolism", "Weight.BMI", "Weight.Omron"), to=c("Weight.visceral.fat","Weight.muscle", "Weight.visceral.fat", "Weight.muscle")))

pdap4 <- hc(weightC, blacklist=data.frame(from=c("Weight.body.age", "Weight.resting.metabolism", "Weight.BMI", "Weight.Omron", "Weight.Omron", "Weight.BMI"), to=c("Weight.visceral.fat","Weight.muscle", "Weight.visceral.fat", "Weight.muscle", "Weight.visceral.fat", "Weight.muscle")))

pdap5 <- hc(weightC, blacklist=data.frame(from=c("Weight.body.age", "Weight.resting.metabolism", "Weight.BMI", "Weight.Omron", "Weight.Omron", "Weight.BMI", "Weight.body.age"), to=c("Weight.visceral.fat","Weight.muscle", "Weight.visceral.fat", "Weight.muscle", "Weight.visceral.fat", "Weight.muscle", "Weight.muscle")))
#   Bayesian network learned via Score-based methods
#
#   model:
#    [Weight.body.fat][Weight.muscle|Weight.body.fat][Weight.visceral.fat|Weight.body.fat]
#    [Weight.Omron|Weight.visceral.fat][Weight.BMI|Weight.Omron]
#    [Weight.resting.metabolism|Weight.Omron:Weight.body.fat]
#    [Weight.body.age|Weight.Omron:Weight.body.fat]
#   nodes:                                 7
#   arcs:                                  8
#     undirected arcs:                     0
#     directed arcs:                       8
#   average markov blanket size:           2.57
#   average neighbourhood size:            2.29
#   average branching factor:              1.14
#
#   learning algorithm:                    Hill-Climbing
#   score:                                 BIC (Gauss.)
#   penalization coefficient:              2.534452101
#   tests used in the learning procedure:  62
#   optimized:                             TRUE
plot(pdap5)
## https://i.imgur.com/nxCfmYf.png

## implementing all the prior knowledge cost ~30:
score(pdap5, weightC)
# [1] -254.6061724

fit <- bn.fit(pdap5, weightC); fit
#   Bayesian network parameters
#
#   Parameters of node Weight.Omron (Gaussian distribution)
#
# Conditional density: Weight.Omron | Weight.visceral.fat
# Coefficients:
#         (Intercept)  Weight.visceral.fat
#       169.181651376          2.744954128
# Standard deviation of the residuals: 1.486044472
#
#   Parameters of node Weight.BMI (Gaussian distribution)
#
# Conditional density: Weight.BMI | Weight.Omron
# Coefficients:
#   (Intercept)   Weight.Omron
# -0.3115772322   0.1411044216
# Standard deviation of the residuals: 0.03513413381
#
#   Parameters of node Weight.body.fat (Gaussian distribution)
#
# Conditional density: Weight.body.fat
# Coefficients:
# (Intercept)
# 28.70314465
# Standard deviation of the residuals: 0.644590085
#
#   Parameters of node Weight.muscle (Gaussian distribution)
#
# Conditional density: Weight.muscle | Weight.body.fat
# Coefficients:
#     (Intercept)  Weight.body.fat
#   52.1003347352    -0.6141270921
# Standard deviation of the residuals: 0.06455478599
#
#   Parameters of node Weight.resting.metabolism (Gaussian distribution)
#
# Conditional density: Weight.resting.metabolism | Weight.Omron + Weight.body.fat
# Coefficients:
#     (Intercept)     Weight.Omron  Weight.body.fat
#   666.910582196      6.767607964     -3.886694779
# Standard deviation of the residuals: 1.323176507
#
#   Parameters of node Weight.body.age (Gaussian distribution)
#
# Conditional density: Weight.body.age | Weight.Omron + Weight.body.fat
# Coefficients:
#     (Intercept)     Weight.Omron  Weight.body.fat
#  -32.2651379176     0.3603672788     0.5150134225
# Standard deviation of the residuals: 0.2914301529
#
#   Parameters of node Weight.visceral.fat (Gaussian distribution)
#
# Conditional density: Weight.visceral.fat | Weight.body.fat
# Coefficients:
#     (Intercept)  Weight.body.fat
#    6.8781100009     0.1070118125
# Standard deviation of the residuals: 0.2373649058
## residuals look fairly good, except for Weight.resting.metabolism, where there are some extreme residuals in what looks a bit like a sigmoid sort of pattern, suggesting nonlinearities in the Omron scale's formula?
bn.fit.qqplot(fit)
## https://i.imgur.com/mSallOv.png

library(lavaan)
Weight.model1 <- '
    Weight.visceral.fat ~ Weight.body.fat
    Weight.Omron ~ Weight.visceral.fat
    Weight.BMI ~ Weight.Omron
    Weight.body.age ~ Weight.Omron + Weight.body.fat
    Weight.muscle ~ Weight.body.fat
    Weight.resting.metabolism ~ Weight.Omron + Weight.body.fat
                   '
Weight.fit1 <- sem(model = Weight.model1,  data = weightC)
summary(Weight.fit1)
# lavaan (0.5-16) converged normally after 139 iterations
#
#   Number of observations                           159
#
#   Estimator                                         ML
#   Minimum Function Test Statistic               71.342
#   Degrees of freedom                                 7
#   P-value (Chi-square)                           0.000
#
# Parameter estimates:
#
#   Information                                 Expected
#   Standard Errors                             Standard
#
#                    Estimate  Std.err  Z-value  P(>|z|)
# Regressions:
#   Weight.visceral.fat ~
#     Weight.bdy.ft     0.107    0.029    3.676    0.000
#   Weight.Omron ~
#     Wght.vscrl.ft     2.745    0.477    5.759    0.000
#   Weight.BMI ~
#     Weight.Omron      0.141    0.002   82.862    0.000
#   Weight.body.age ~
#     Weight.Omron      0.357    0.014   25.162    0.000
#     Weight.bdy.ft     0.516    0.036   14.387    0.000
#   Weight.muscle ~
#     Weight.bdy.ft    -0.614    0.008  -77.591    0.000
#   Weight.resting.metabolism ~
#     Weight.Omron      6.730    0.064  104.631    0.000
#     Weight.bdy.ft    -3.860    0.162  -23.837    0.000
#
# Covariances:
#   Weight.BMI ~~
#     Weight.body.g    -0.000    0.001   -0.116    0.907
#     Weight.muscle    -0.000    0.000   -0.216    0.829
#     Wght.rstng.mt     0.005    0.004    1.453    0.146
#   Weight.body.age ~~
#     Weight.muscle     0.001    0.001    0.403    0.687
#     Wght.rstng.mt    -0.021    0.030   -0.700    0.484
#   Weight.muscle ~~
#     Wght.rstng.mt     0.007    0.007    1.003    0.316
#
# Variances:
#     Wght.vscrl.ft     0.056    0.006
#     Weight.Omron      2.181    0.245
#     Weight.BMI        0.001    0.000
#     Weight.body.g     0.083    0.009
#     Weight.muscle     0.004    0.000
#     Wght.rstng.mt     1.721    0.193

zeo <- read.csv("~/wiki/docs/zeo/gwern-zeodata.csv")
zeo$Sleep.Date <- as.Date(zeo$Sleep.Date, format="%m/%d/%Y")

## convert "05/12/2014 06:45" to "06:45"
zeo$Start.of.Night <- sapply(strsplit(as.character(zeo$Start.of.Night), " "), function(x) { x[2] })
## convert "06:45" to 24300
interval <- function(x) { if (!is.na(x)) { if (grepl(" s",x)) as.integer(sub(" s","",x))
                                           else { y <- unlist(strsplit(x, ":")); as.integer(y[[1]])*60 + as.integer(y[[2]]); }
                                         }
                          else NA
                        }
zeo$Start.of.Night <- sapply(zeo$Start.of.Night, interval)
## correct for the switch to new unencrypted firmware in March 2013;
## I don't know why the new firmware subtracts 15 hours
zeo[(zeo$Sleep.Date >= as.Date("2013-03-11")),]$Start.of.Night <- (zeo[(zeo$Sleep.Date >= as.Date("2013-03-11")),]$Start.of.Night + 900) %% (24*60)

## after midnight (24*60=1440), Start.of.Night wraps around to 0, which obscures any trends,
## so we'll map anything before 7AM to time+1440
zeo[zeo$Start.of.Night<420 & !is.na(zeo$Start.of.Night),]$Start.of.Night <- (zeo[zeo$Start.of.Night<420 & !is.na(zeo$Start.of.Night),]$Start.of.Night + (24*60))

zeoSmall <- subset(zeo, select=c(ZQ,Total.Z,Time.to.Z,Time.in.Wake,Time.in.REM,Time.in.Light,Time.in.Deep,Awakenings,Start.of.Night,Morning.Feel))
zeoClean <- na.omit(zeoSmall)
# bnlearn doesn't like the 'integer' class that most of the data-frame is in
zeoClean <- as.data.frame(sapply(zeoClean, as.numeric))

library(bnlearn)
## after a bunch of iteration, blacklisting arrows which violate the prior knowledge
bl <- data.frame(from=c("Morning.Feel", "ZQ", "ZQ", "ZQ", "ZQ", "ZQ", "ZQ", "Time.in.REM", "Time.in.Light", "Time.in.Deep", "Morning.Feel", "Awakenings", "Time.in.Light", "Morning.Feel", "Morning.Feel","Total.Z", "Time.in.Wake", "Time.to.Z", "Total.Z", "Total.Z", "Total.Z"),
                 to=c("Start.of.Night", "Total.Z", "Time.in.Wake", "Time.in.REM", "Time.in.Deep", "Morning.Feel","Start.of.Night", "Start.of.Night","Start.of.Night","Start.of.Night", "Time.to.Z", "Time.to.Z", "Time.to.Z", "Total.Z", "Time.in.Wake","Time.to.Z","Time.to.Z", "Start.of.Night", "Time.in.Deep", "Time.in.REM", "Time.in.Light"))

zeo.hc <- hc(zeoClean, blacklist=bl)
zeo.iamb         <- iamb(zeoClean, blacklist=bl)
## problem: undirected arc: Time.in.Deep/Time.in.REM; since hc inferred [Time.in.Deep|Time.in.REM], I'll copy that for iamb:
zeo.iamb <- set.arc(zeo.iamb, from = "Time.in.REM", to = "Time.in.Deep")
zeo.gs <- gs(zeoClean, blacklist=bl)
## same undirected arc:
zeo.gs <- set.arc(zeo.gs, from = "Time.in.REM", to = "Time.in.Deep")

## Bigger is better:
score(zeo.iamb, data=zeoClean)
# [1] -44776.79185
score(zeo.gs, data=zeoClean)
# [1] -44776.79185
score(zeo.hc, data=zeoClean)
# [1] -44557.6952
## hc scores best, so let's look at it:
zeo.hc
#   Bayesian network learned via Score-based methods
#
#   model:
#    [Start.of.Night][Time.to.Z|Start.of.Night][Time.in.Light|Time.to.Z:Start.of.Night]
#    [Time.in.REM|Time.in.Light:Start.of.Night][Time.in.Deep|Time.in.REM:Time.in.Light:Start.of.Night]
#    [Total.Z|Time.in.REM:Time.in.Light:Time.in.Deep][Time.in.Wake|Total.Z:Time.to.Z]
#    [Awakenings|Time.to.Z:Time.in.Wake:Time.in.REM:Time.in.Light:Start.of.Night]
#    [Morning.Feel|Total.Z:Time.to.Z:Time.in.Wake:Time.in.Light:Start.of.Night]
#    [ZQ|Total.Z:Time.in.Wake:Time.in.REM:Time.in.Deep:Awakenings]
#   nodes:                                 10
#   arcs:                                  28
#     undirected arcs:                     0
#     directed arcs:                       28
#   average markov blanket size:           7.40
#   average neighbourhood size:            5.60
#   average branching factor:              2.80
#
#   learning algorithm:                    Hill-Climbing
#   score:                                 BIC (Gauss.)
#   penalization coefficient:              3.614556939
#   tests used in the learning procedure:  281
#   optimized:                             TRUE

plot(zeo.hc)
## https://i.imgur.com/nD3LXND.png

fit <- bn.fit(zeo.hc, zeoClean); fit
#
#   Bayesian network parameters
#
#   Parameters of node ZQ (Gaussian distribution)
#
# Conditional density: ZQ | Total.Z + Time.in.Wake + Time.in.REM + Time.in.Deep + Awakenings
# Coefficients:
#    (Intercept)         Total.Z    Time.in.Wake     Time.in.REM    Time.in.Deep      Awakenings
# -0.12468522173   0.14197043518  -0.07103211437   0.07053271816   0.21121000076  -0.56476256303
# Standard deviation of the residuals: 0.3000223604
#
#   Parameters of node Total.Z (Gaussian distribution)
#
# Conditional density: Total.Z | Time.in.Wake + Start.of.Night
# Coefficients:
#    (Intercept)    Time.in.Wake  Start.of.Night
# 907.6406157850   -0.4479377278   -0.2680771514
# Standard deviation of the residuals: 68.90853885
#
#   Parameters of node Time.to.Z (Gaussian distribution)
#
# Conditional density: Time.to.Z | Start.of.Night
# Coefficients:
#    (Intercept)  Start.of.Night
# -1.02898431407   0.01568450832
# Standard deviation of the residuals: 13.51606719
#
#   Parameters of node Time.in.Wake (Gaussian distribution)
#
# Conditional density: Time.in.Wake | Time.to.Z
# Coefficients:
#   (Intercept)      Time.to.Z
# 14.7433880499   0.3289378711
# Standard deviation of the residuals: 19.0906685
#
#   Parameters of node Time.in.REM (Gaussian distribution)
#
# Conditional density: Time.in.REM | Total.Z + Start.of.Night
# Coefficients:
#      (Intercept)           Total.Z    Start.of.Night
# -120.62442964234     0.37864195651     0.06275760841
# Standard deviation of the residuals: 19.32560757
#
#   Parameters of node Time.in.Light (Gaussian distribution)
#
# Conditional density: Time.in.Light | Total.Z + Time.in.REM + Time.in.Deep
# Coefficients:
#   (Intercept)        Total.Z    Time.in.REM   Time.in.Deep
#  0.6424267863   0.9997862624  -1.0000587988  -1.0001805537
# Standard deviation of the residuals: 0.5002896274
#
#   Parameters of node Time.in.Deep (Gaussian distribution)
#
# Conditional density: Time.in.Deep | Total.Z + Time.in.REM
# Coefficients:
#   (Intercept)        Total.Z    Time.in.REM
# 15.4961459056   0.1283622577  -0.1187382535
# Standard deviation of the residuals: 11.90756843
#
#   Parameters of node Awakenings (Gaussian distribution)
#
# Conditional density: Awakenings | Time.to.Z + Time.in.Wake + Time.in.REM + Time.in.Light + Start.of.Night
# Coefficients:
#     (Intercept)        Time.to.Z     Time.in.Wake      Time.in.REM    Time.in.Light
# -18.41014329148    0.02605164827    0.05736596152    0.02291139969    0.01060661963
#  Start.of.Night
#   0.01129521977
# Standard deviation of the residuals: 2.427868657
#
#   Parameters of node Start.of.Night (Gaussian distribution)
#
# Conditional density: Start.of.Night
# Coefficients:
# (Intercept)
# 1413.382886
# Standard deviation of the residuals: 64.43144125
#
#   Parameters of node Morning.Feel (Gaussian distribution)
#
# Conditional density: Morning.Feel | Total.Z + Time.to.Z + Time.in.Wake + Time.in.Light + Start.of.Night
# Coefficients:
#     (Intercept)          Total.Z        Time.to.Z     Time.in.Wake    Time.in.Light
# -0.924662971061   0.004808652252  -0.010127269154  -0.008636841492  -0.002766602019
#  Start.of.Night
#  0.001672816480
# Standard deviation of the residuals: 0.7104115719

## some issues with big residuals at the extremes in the variables Time.in.Light, Time.in.Wake, and Time.to.Z;
## not sure how to fix those
bn.fit.qqplot(fit)
# https://i.imgur.com/fmP1ca0.png

library(lavaan)
Zeo.model1 <- '
    Time.to.Z ~ Start.of.Night
    Time.in.Wake ~ Total.Z + Time.to.Z
    Awakenings ~ Time.to.Z + Time.in.Wake + Time.in.REM + Time.in.Light + Start.of.Night
    Time.in.Light ~ Time.to.Z + Start.of.Night
    Time.in.REM ~ Time.in.Light + Start.of.Night
    Time.in.Deep ~ Time.in.REM + Time.in.Light + Start.of.Night
    Total.Z ~ Time.in.REM + Time.in.Light + Time.in.Deep
    ZQ ~ Total.Z + Time.in.Wake + Time.in.REM + Time.in.Deep + Awakenings
    Morning.Feel ~ Total.Z + Time.to.Z + Time.in.Wake + Time.in.Light + Start.of.Night
                   '
Zeo.fit1 <- sem(model = Zeo.model1,  data = zeoClean)
summary(Zeo.fit1)
# lavaan (0.5-16) converged normally after 183 iterations
#
#   Number of observations                          1379
#
#   Estimator                                         ML
#   Minimum Function Test Statistic               22.737
#   Degrees of freedom                                16
#   P-value (Chi-square)                           0.121
#
# Parameter estimates:
#
#   Information                                 Expected
#   Standard Errors                             Standard
#
#                    Estimate  Std.err  Z-value  P(>|z|)
# Regressions:
#   Time.to.Z ~
#     Start.of.Nght     0.016    0.006    2.778    0.005
#   Time.in.Wake ~
#     Total.Z          -0.026    0.007   -3.592    0.000
#     Time.to.Z         0.314    0.038    8.277    0.000
#   Awakenings ~
#     Time.to.Z         0.026    0.005    5.233    0.000
#     Time.in.Wake      0.057    0.003   16.700    0.000
#     Time.in.REM       0.023    0.002   10.107    0.000
#     Time.in.Light     0.011    0.002    6.088    0.000
#     Start.of.Nght     0.011    0.001   10.635    0.000
#   Time.in.Light ~
#     Time.to.Z        -0.348    0.085   -4.121    0.000
#     Start.of.Nght    -0.195    0.018  -10.988    0.000
#   Time.in.REM ~
#     Time.in.Light     0.358    0.018   19.695    0.000
#     Start.of.Nght     0.034    0.013    2.725    0.006
#   Time.in.Deep ~
#     Time.in.REM       0.081    0.012    6.657    0.000
#     Time.in.Light     0.034    0.009    3.713    0.000
#     Start.of.Nght    -0.017    0.006   -3.014    0.003
#   Total.Z ~
#     Time.in.REM       1.000    0.000 2115.859    0.000
#     Time.in.Light     1.000    0.000 2902.045    0.000
#     Time.in.Deep      1.000    0.001  967.322    0.000
#   ZQ ~
#     Total.Z           0.142    0.000  683.980    0.000
#     Time.in.Wake     -0.071    0.000 -155.121    0.000
#     Time.in.REM       0.071    0.000  167.090    0.000
#     Time.in.Deep      0.211    0.001  311.454    0.000
#     Awakenings       -0.565    0.003 -178.407    0.000
#   Morning.Feel ~
#     Total.Z           0.005    0.001    8.488    0.000
#     Time.to.Z        -0.010    0.001   -6.948    0.000
#     Time.in.Wake     -0.009    0.001   -8.592    0.000
#     Time.in.Light    -0.003    0.001   -2.996    0.003
#     Start.of.Nght     0.002    0.000    5.414    0.000

Genome sequencing costs

# http://www.genome.gov/sequencingcosts/
# http://www.genome.gov/pages/der/sequencing_costs_apr2014.xls
# converted to CSV & deleted cost per base (less precision); CSV looks like:
# https://dl.dropboxusercontent.com/u/182368464/sequencing_costs_apr2014.csv
## Date, Cost per Genome
## Sep-01,"$95,263,072"
## ...
sequencing <- read.csv("sequencing_costs_apr2014.csv")
sequencing$Cost.per.Genome <- as.integer(gsub(",", "", sub("\\$", "", as.character(sequencing$Cost.per.Genome))))
# interpret month-years as first of month:
sequencing$Date <- as.Date(paste0("01-", as.character(sequencing$Date)), format="%d-%b-%y")
head(sequencing)
##         Date Cost.per.Genome
## 1 2001-09-01        95263072
## 2 2002-03-01        70175437
## 3 2002-09-01        61448422
## 4 2003-03-01        53751684
## 5 2003-10-01        40157554
## 6 2004-01-01        28780376

l <- lm(log(Cost.per.Genome) ~ Date, data=sequencing); summary(l)
##
## Coefficients:
##                 Estimate   Std. Error  t value   Pr(>|t|)
## (Intercept) 50.969823683  1.433567932  35.5545 < 2.22e-16
## Date        -0.002689621  0.000101692 -26.4486 < 2.22e-16
##
## Residual standard error: 0.889707 on 45 degrees of freedom
## Multiple R-squared:  0.939559,   Adjusted R-squared:  0.938216
## F-statistic: 699.528 on 1 and 45 DF,  p-value: < 2.22e-16
plot(log(Cost.per.Genome) ~ Date, data=sequencing)
## https://i.imgur.com/3XK8i0h.png
# as expected: linear in log (Moore's law) 2002-2008, sudden drop, return to Moore's law-ish ~December 2011?
# but on the other hand, maybe the post-December 2011 behavior is a continuation of the curve
library(segmented)
# 2 break-points / 3 segments:
piecewise <- segmented(l, seg.Z=~Date, psi=list(Date=c(13970, 16071)))
summary(piecewise)
## Estimated Break-Point(s):
##             Est. St.Err
## psi1.Date 12680 1067.0
## psi2.Date 13200  279.8
##
## t value for the gap-variable(s) V:  0 0 2
##
## Meaningful coefficients of the linear terms:
##                 Estimate   Std. Error  t value   Pr(>|t|)
## (Intercept) 35.841699121  8.975628264  3.99322 0.00026387
## Date        -0.001504431  0.000738358 -2.03754 0.04808491
## U1.Date      0.000679538  0.002057940  0.33020         NA
## U2.Date     -0.002366688  0.001926528 -1.22847         NA
##
## Residual standard error: 0.733558 on 41 degrees of freedom
## Multiple R-Squared: 0.962565,  Adjusted R-squared:   0.958
with(sequencing, plot(Date, log(Cost.per.Genome), pch=16)); plot(piecewise, add=T)
## https://i.imgur.com/HSRqkJO.png
# The first two segments look fine, but the residuals are clearly bad for the third line-segment:
# it undershoots (damaging the second segment's fit), overshoots, then undershoots again. Let's try again with more breakpoints:

lots <- segmented(l, seg.Z=~Date, psi=list(Date=NA), control=seg.control(stop.if.error=FALSE, n.boot=0))
summary(segmented(l, seg.Z=~Date, psi=list(Date=as.Date(c(12310, 12500, 13600, 13750,  14140,  14680,  15010, 15220), origin = "1970-01-01", tz = "EST"))))
# delete every breakpoint below t-value of ~|2.3|, for 3 breakpoints / 4 segments:
piecewise2 <- segmented(l, seg.Z=~Date, psi=list(Date=as.Date(c("2007-08-25","2008-09-18","2010-03-12"))))
with(sequencing, plot(Date, log(Cost.per.Genome), pch=16)); plot(piecewise2, add=T)

# the additional break-point is used up on a better fit in the curve. It looks like an exponential decay/asymptote,
# so let's work on fitting that part of the graph, the post-2007 curve:
sequencingRecent <- sequencing[sequencing$Date>as.Date("2007-10-01"),]
lR <- lm(log(Cost.per.Genome) ~ Date, data=sequencingRecent); summary(lR)
piecewiseRecent <- segmented(lR, seg.Z=~Date, psi=list(Date=c(14061, 16071))); summary(piecewiseRecent)
## Estimated Break-Point(s):
##             Est. St.Err
## psi1.Date 14290  36.31
## psi2.Date 15290  48.35
##
## t value for the gap-variable(s) V:  0 0
##
## Meaningful coefficients of the linear terms:
##                 Estimate   Std. Error   t value   Pr(>|t|)
## (Intercept)  1.13831e+02  6.65609e+00  17.10182 2.0951e-13
## Date        -7.13247e-03  4.73332e-04 -15.06865 2.2121e-12
## U1.Date      4.11492e-03  4.94486e-04   8.32161         NA
## U2.Date      2.48613e-03  2.18528e-04  11.37668         NA
##
## Residual standard error: 0.136958 on 20 degrees of freedom
## Multiple R-Squared: 0.995976,  Adjusted R-squared: 0.994971

with(sequencingRecent, plot(Date, log(Cost.per.Genome), pch=16)); plot(piecewiseRecent, add=T)

lastPiece <- lm(log(Cost.per.Genome) ~ Date, data=sequencingRecent[as.Date(15290, origin = "1970-01-01", tz = "EST")<sequencingRecent$Date,]); summary(lastPiece)
## Coefficients:
##                 Estimate   Std. Error  t value   Pr(>|t|)
## (Intercept) 17.012409648  1.875482507  9.07095 1.7491e-05
## Date        -0.000531621  0.000119056 -4.46528  0.0020963
##
## Residual standard error: 0.0987207 on 8 degrees of freedom
## Multiple R-squared:  0.71366,    Adjusted R-squared:  0.677867
with(sequencingRecent[as.Date(15290, origin = "1970-01-01", tz = "EST")<sequencingRecent$Date,], plot(Date, log(Cost.per.Genome), pch=16)); abline(lastPiece)

predictDays <- seq(from=sequencing$Date[1], to=as.Date("2030-12-01"), by="month")
lastPiecePredict <- data.frame(Date = predictDays, Cost.per.Genome=c(sequencing$Cost.per.Genome, rep(NA, 305)), Cost.per.Genome.predicted = exp(predict(lastPiece, newdata = data.frame(Date = predictDays))))

nlmR <- nls(log(Cost.per.Genome) ~ SSasymp(as.integer(Date), Asym, r0, lrc), data=sequencingRecent); summary(nlmR)
##
## Parameters:
##          Estimate   Std. Error    t value Pr(>|t|)
## Asym  7.88908e+00  1.19616e-01   65.95328   <2e-16
## r0    1.27644e+08  1.07082e+08    1.19203   0.2454
## lrc  -6.72151e+00  5.05221e-02 -133.04110   <2e-16
##
## Residual standard error: 0.150547 on 23 degrees of freedom
with(sequencingRecent, plot(Date, log(Cost.per.Genome))); lines(sequencingRecent$Date, predict(nlmR), col=2)

# side by side:
with(sequencingRecent, plot(Date, log(Cost.per.Genome), pch=16))
plot(piecewiseRecent, add=TRUE, col=2)
lines(sequencingRecent$Date, predict(nlmR), col=3)
# as we can see, the 3-piece linear fit and the exponential decay fit identically;
# but exponential decay is more parsimonious, IMO, so I prefer that.

predictDays <- seq(from=sequencingRecent$Date[1], to=as.Date("2020-12-01"), by="month")
data.frame(Date = predictDays, Cost.per.Genome.predicted = exp(predict(nlmR, newdata = data.frame(Date = predictDays))))

http://www.unz.com/gnxp/the-intel-of-sequencing/#comment-677904 https://biomickwatson.wordpress.com/2015/03/25/the-cost-of-sequencing-is-still-going-down/

Proposal: hand-counting mobile app for more fluid group discussions

Groups use voting for decision-making, but existing vote systems are cumbersome. Hand-raising is faster, but does not scale because hand-counting hands is slow. Advances in machine vision may make it possible for AI to count hands in photos accurately. Combined with a smartphone’s camera, this could yield an app for fast voting in even large groups.

Medium-large (>10 people) groups face a problem in reaching consensus: ballot or pen-and-paper voting is sufficiently slow and clunky that it is too costly to use for anything but the most important discussions. A group is forced to adopt other discussion norms and save a formal vote for only the final decision, and even then the long delay kills a lot of enthusiasm and interest. Voting could be used for many more decisions if it could be faster, and of course all existing group votes would benefit from increased speed. (I am reminded of anime conventions and film festivals where, particularly for short films such as AMVs, one seems to spend more time filling out a ballot & passing them along aisles & the staff painfully counting through each ballot by hand than one actually spends watching the media in question!)

It would be better if voting could be as fluent and easy as simply raising your hand like in a small group such as a classroom - a mechanism which makes it so easy to vote that votes can be held as fast as the alternatives can be spoken aloud and a glance suffices to count (an alert group could vote on 2 or 3 topics in the time it takes to read this sentence). But hand-raising, as great as it is, suffers from the flaw that it does not scale due to the counting problem: a group of 500 people can raise their hands as easily as a group of 50 or 5, but it takes far too long to count ~250 hands: the person counting will quickly tire of the tedium, they will make mistakes counting, and this puts a serious lag on each vote, a lag which increases linearly with the number of voters. (Hands can be easy to approximate if almost everyone votes for or against something, but if consensus is so overwhelming, one doesn’t need to vote in the first place! The hard case of almost-balanced votes is the most important case.)

One might suggest using an entirely different strategy: a website with HTML polls or little clicker gizmos like used in some college lectures to administer quick quizzes. This have the downsides that they require potentially expensive equipment (I used a clicker in one class and I think it cost at least $20, so if a convention wanted to use that in an audience of hundreds, that’s a major upfront cost & my experience was that clickers were unintuitive, did not always work, and slowed things down if anything; a website would only work if you assume everyone has smartphones and is willing to pull them out to use at an instance’s notice and of course that there’s working WiFi in the room, which cannot be taken for granted) and considerable overhead in explaining to everyone how it works and getting them on the same page and making sure every person who wanders in also gets the message. (If anyone is going to be burdened with understanding or using a new system, it should be the handful of conference/festival/group organizers, not the entire audience!) It’s hard to imagine a film festival running using either system, and difficult to see either system improving on pen-and-paper ballots which at least are cheap, relatively straightforward, and well-known.

Hand-counting really does seem like the best solution, if only the counting could be fixed. Counting is something computers do fast, so that is the germ of an idea. What if a smartphone could count the votes? You don’t want a smartphone app on the entire audiences’ phones, of course, since that’s even worse than having everyone go to a website to vote; but machine vision has made enormous strides in the 2000s-2010s, reaching human-equivalent performance on challenging image recognition contests like ImageNet. (Machine vision is complicated, but the important thing is that it’s the kind of complicated which can be outsourced to someone else and turned into a dead-easy-to-use app, and the burden does not fall on the primary users - the audience.) What if the organizer had an app which took a photo of the entire audience with lifted arms and counted hands & faces and returned a vote count in a second?

Such an app would be ideal for any cultural, political, or organizational meeting. Now the flow for, eg, a film festival could go: [no explanation given to audience, one just starts] OK, how many people liked the first short, Vampire Deli by Ms Houston? [everyone raises hand, smartphone flashes, 1s passes] OK, 140 votes. How many liked the second short, Cthulicious by Mr Iouston? [raises hands, smartphone flashes, 1s passes] OK… 130 people. Congratulations Ms Houston! And so on.

Such an app might be considered an infeasible machine vision task, but I believe it could be feasible: facial localization is an old and well-studied image recognition task (and effective algorithms are built into every consumer camera), hands/fingers have very distinct shapes, and both tasks seem easier than the subtle discriminations between, say, various dog breeds demanded of ImageNet contestants.

Specifically, one could implement the machine vision core as follows:

1. multilayer neural networks trained for one task can be repurposed to similar tasks by removing the highest layer and retraining on the new task, potentially reaping great performance gains as the hybrid network has already learned much of what it needs for the second task (transfer learning). So one could take a publicly available NN trained for ImageNet (such as AlexNet, available in caffe), remove the top two layers, and retrain on a dataset of audiences; this will perform better since the original NN has already learned how to detect edges, recognize faces, etc

   The simpler task of counting crowds has already shown itself susceptible to deep learning: eg Cross-scene Crowd Counting via Deep Convolutional Neural Networks.
2. raid Flickr and Google Images for CC-licensed photos of audiences raising their arms; then one can manually count how many arms are raised (or outsource to Amazon Mechanical Turk). With the boost from a transferred convolutional deep network, one might get good performance with just a few thousand photos to train with. If each photo takes a minute to obtain and count, then one can create a useful corpus in a week or two of work.

3. train the NN, applying the usual data augmentation tricks to increase one’s meager corpus, trying out random hyperparameters, tweaking the architecture, etc

   (Note that while NNs are very slow and computationally intensive to train, they are typically quite fast to run; the smartphone app would not be training a NN, which is indeed completely infeasible from a CPU and battery life standpoint - it is merely running the NN created by the original developer.)

4. with an accurate NN, one can wrap it in a mobile app framework. The UI, at the simplest, is simply a big button to press to take a photo, feed it into the NN, and display the count. Some additional features come to mind:

   • headcount mode: one may not be interested in a vote, but in how many people are in an audience (to estimate how popular a guest is, whether an event needs to move to a new bigger space, etc). If the NN can count faces and hands to estimate a vote count, it can simply report the count of faces instead.
   • the app should save every photo & count, both as an audit trail and also to support post-vote recounts in case of disputes or a desire for a more definitive count

   • the reported count should come with an indication of the NN’s uncertainty/error-rate, so users are not misled by their little handheld oracle and so they can redo a vote if the choice is borderline; Bayesian methods, in which previous votes are drawn upon, might be relevant here.

     • if the original photo could be annotated with graphical notes for each recognized/counted hand & face, this would let the user see what the NN is thinking and would help build confidence a great deal
   • it should support manually entering in a vote-count; if the manual count differs, then this indicates the NN made an error and the photo & count should be uploaded to the original developer so it can be added to the corpus and the NN’s performance fixed in future releases of the app
   • smartphone cameras may not be high-resolution or have a sufficiently wide field-of-view to capture the entire audience at once; some sort of montage mode should exist so the user can swing the phone across the audience, bursts of shots taken, and the overlapping photos stitched together into a single audience photo which can be then fed into the NN as usual
   • a burst of photos might be superior to a single photo due to smartphone & hand movement blur; I don’t know if it’s best to try to combine the photos, run the NN multiple times and take the median, or feed multiple photos into the NN (perhaps by moving to a RNN architecture?)

   • the full-strength NN might still be too slow and energy-hungry to run pleasantly on a smartphone; there are model compression techniques for simplifying deep NNs to reduce the number of nodes or have fewer layers without losing much performance, which might be useful in this context (and indeed, were originally motivated by wanting to make speech-recognition run better on smartphones)

Given this breakdown, one might estimate building such an app as requiring, assuming one is already reasonably familiar with deep networks & writing mobile apps:

1. 1 week to find an ImageNet NN, learn how to modify it, and set it up to train on a fresh corpus
2. 3 weeks to create a corpus of <5000 photos with manually-labeled hand counts
3. 5 weeks to train the NN (NNs as large as ImageNet NNs take weeks to train; depending on the GPU hardware one has access to and how many tweaks and hyperparameters one tries, 5 weeks could be drastically optimistic; but on the plus side, it’s mostly waiting as the GPUs suck electricity like crazy)
4. 5 weeks to make an intuitive simple app, submitting to an app store, etc
5. These estimates are loose and probably too optimistic (although I would be surprised if it took a good developer more than 6 months to develop this app), but that would suggest >14 weeks or 784 hours of work for a developer, start to finish. (Even at minimum wage, this represents a substantial development cost of >$6k; at more plausible developer salaries, easily >$60k of salary.)

How large is the market for such an app? Groups such as anime conventions or anything on a college campus are cheapskates and would balk at a price higher than $4.99 (even if only 5 or 10 staffers need to buy it and it makes the experience much smoother). There are probably several hundred anime or video game conventions which might use this to vote, so that might be 1000 sales there. There’s easily 13,000 business conventions or conferences in the USA, which might not need voting so much, but would be attracted by a headcount mode to help optimize their event. This suggests perhaps $70k in sales with much less profit after the app store cut & taxes, much of which sales would probably be one-offs as the user reuses it for each conference. So even a wild success, in which most events adopt use of such voting software, would barely recoup the development costs; as a product, it seems this is just too much of a niche unless one could develop it much faster (such as by finding an existing corpus of hands/photos, or be certain of banging out the mobile app in much less than I estimated), find a larger market (theaters for audience participation?), or increase price substantially (10x the price and aim at only businesses?).

Air conditioner replacement

Is my old air conditioner inefficient enough to replace? After calculating electricity consumption for it and a new air conditioner, with discounting, and with uncertainty in parameters evaluated by a Monte Carlo method, I conclude that the savings are too small by an order of magnitude to pay for a new replacement air conditioner.

I have an old Whirlpool air conditioner (AC) in my apartment, and as part of insulating and cooling my apartment, I’ve wondered if the AC should be replaced on energy efficiency grounds. Would a new AC save more than it costs upfront? What is the optimal decision here?

Initially I was balked in analysis because I couldn’t figure out what model it was, and thus anything about it like its energy efficiency. (No model number or name appears anywhere visible on it, and I’m not going to rip it out of the wall just to look at hidden parts.)

set.seed(2015-07-26)
simulate <- function() {
    BTUs <- rnorm(1, 10000, 100)
    EER_old <- 10.7 - abs(rnorm(1, 0, 0.5)) # half-normals because efficiencies only get worse, not better
    EER_new <- 11.2 - abs(rnorm(1, 0, 0.5))
    kWh <- rnorm(1, 0.09, 0.01)
    dailyUsage <- rnorm(1, 8, 2)
    months <- sample (4:6, 1)
    minimumSavings <- rnorm(1, 47, 4)

    annualNetSavings <- ((((BTUs / EER_old ) - (BTUs / EER_new)) / 1000) * kWh * dailyUsage * 30 * months) - minimumSavings
    return(annualNetSavings)
}
sims <- replicate(100000, simulate())
summary(sims)
##        Min.     1st Qu.      Median        Mean     3rd Qu.        Max.
## -70.3666500 -46.2051500 -42.3764100 -42.1133700 -38.3134600  -0.7334517
quantile(sims, p=c(0.025, 0.975))
##        2.5%        97.5%
## -53.59989114 -29.13999204

Some ways of dealing with measurement error

Prompted by a question on LessWrong, some examples of how to analyze noisy measurements in R:

## Create a simulated dataset with known parameters, and then run a ML multilevel model, a ML SEM,
## and a Bayesian multilevel model; with the last, calculate Expected Value of Sample Information (EVSI):

## SIMULATE
set.seed(2015-08-11)
## "There is a variable X, x belongs to [0, 100]."
toplevel <- rnorm(n=1, 50, 25)
## "There are n ways of measuring it, among them A and B are widely used."
## "For any given measurer, the difference between x(A) and x(B) can be up to 20 points."
A <- toplevel + runif(1, min=-10, max=10)
B <- toplevel + runif(1, min=-10, max=10)
c(toplevel, A, B)
# [1] 63.85938385 55.43608379 59.42333264
### the true level of X we wish to recover is '63.85'

## "Between two any measurers, x(A)1 and x(A)2 can differ on average 10 points, likewise with B."
### let's imagine 10 hypothetical points are sample using method A and method B
### assume 'differ on average 10 points' here means something like 'the standard deviation is 10'
A_1 <- rnorm(n=10, mean=A, sd=10)
B_1 <- rnorm(n=10, mean=B, sd=10)

data <- rbind(data.frame(Measurement="A", Y=A_1), data.frame(Measurement="B", Y=B_1)); data
#    Measurement           Y
# 1            A 56.33870025
# 2            A 69.07267213
# 3            A 40.36889573
# 4            A 48.67289213
# 5            A 79.92622603
# 6            A 62.86919410
# 7            A 53.12953462
# 8            A 66.58894990
# 9            A 47.86296948
# 10           A 60.72416003
# 11           B 68.60203507
# 12           B 58.24702007
# 13           B 45.47895879
# 14           B 63.45308935
# 15           B 52.27724328
# 16           B 56.89783535
# 17           B 55.93598486
# 18           B 59.28162022
# 19           B 70.92341777
# 20           B 49.51360373

## MLM

## multi-level model approach:
library(lme4)
mlm <- lmer(Y ~ (1|Measurement), data=data); summary(mlm)
# Random effects:
#  Groups      Name        Variance Std.Dev.
#  Measurement (Intercept)  0.0000  0.000000
#  Residual                95.3333  9.763877
# Number of obs: 20, groups:  Measurement, 2
#
# Fixed effects:
#              Estimate Std. Error  t value
# (Intercept) 58.308250   2.183269 26.70685
confint(mlm)
#                    2.5 %       97.5 %
# .sig01       0.000000000  7.446867736
# .sigma       7.185811525 13.444112087
# (Intercept) 53.402531768 63.213970887

## So we estimate X at 58.3 but it's not inside our confidence interval with such little data. Bad luck?

## SEM

library(lavaan)
X.model <- '        X =~ B + A
                    A =~ a
                    B =~ b'
X.fit <- sem(model = X.model, meanstructure = TRUE, data = data2)
summary(X.fit)
# ...                   Estimate  Std.err  Z-value  P(>|z|)
# Latent variables:
#   X =~
#     B                 1.000
#     A              7619.504
#   A =~
#     a                 1.000
#   B =~
#     b                 1.000
#
# Intercepts:
#     a                58.555
#     b                58.061
#     X                 0.000
#     A                 0.000
#     B                 0.000
## Well, that didn't work well - explodes, unfortunately. Probably still not enough data.

## MLM (Bayesian)

library(R2jags)
## rough attempt at writing down an explicit multilevel model which
## respects the mentioned priors about errors being reasonably small:
model <- function() {
  grand.mean ~ dunif(0,100)

  delta.between.group ~ dunif(0, 10)

  sigma.between.group ~ dunif(0, 100)
  tau.between.group <- pow(sigma.between.group, -2)

  for(j in 1:K){
   # let's say the group-level differences are also normally-distributed:
   group.delta[j] ~ dnorm(delta.between.group, tau.between.group)
   # and each group also has its own standard-deviation, potentially different from the others':
   group.within.sigma[j] ~ dunif(0, 20)
   group.within.tau[j] <- pow(group.within.sigma[j], -2)

   # save the net combo for convenience & interpretability:
   group.mean[j] <- grand.mean + group.delta[j]
  }

  for (i in 1:N) {
   # each individual observation is from the grand-mean + group-offset, then normally distributed:
   Y[i] ~ dnorm(grand.mean + group.delta[Group[i]], group.within.tau[Group[i]])
  }

  }
jagsData <- list(N=nrow(data), Y=data$Y, K=length(levels(data$Measurement)),
             Group=data$Measurement)
params <- c("grand.mean","delta.between.group", "sigma.between.group", "group.delta", "group.mean",
            "group.within.sigma")
k1 <- jags(data=jagsData, parameters.to.save=params, inits=NULL, model.file=model); k1
# ...                      mu.vect sd.vect    2.5%     25%     50%     75%   97.5%  Rhat n.eff
# delta.between.group     4.971   2.945   0.221   2.353   4.967   7.594   9.791 1.008   260
# grand.mean             52.477  11.321  23.453  47.914  53.280  58.246  74.080 1.220    20
# group.delta[1]          6.017  11.391 -16.095   0.448   5.316  10.059  34.792 1.152    21
# group.delta[2]          5.662  11.318 -15.836   0.054   5.009  10.107  33.548 1.139    21
# group.mean[1]          58.494   3.765  50.973  56.188  58.459  60.838  66.072 1.001  3000
# group.mean[2]          58.139   2.857  52.687  56.366  58.098  59.851  63.999 1.003   920
# group.within.sigma[1]  12.801   2.766   8.241  10.700  12.446  14.641  18.707 1.002  1100
# group.within.sigma[2]   9.274   2.500   5.688   7.475   8.834  10.539  15.700 1.002  1600
# sigma.between.group    18.031  21.159   0.553   3.793   9.359  23.972  82.604 1.006  1700
# deviance              149.684   2.877 145.953 147.527 149.081 151.213 156.933 1.001  3000

## VOI

posteriorXs <- k1$BUGSoutput$sims.list[["grand.mean"]]
MSE <- function(x1, x2) { (x2 - x1)^2 }
lossFunction <- function(x, predictions) { mean(sapply(predictions, function(x2) { MSE(x, x2)}))}
## our hypothetical mean-squared loss if we predicted, say, X=60:
lossFunction(60, posteriorXs)
# [1] 184.7087612
## of the possible values for X, 1-100, what value of X minimizes our squared error loss?
losses <- sapply(c(1:100), function (n) { lossFunction(n, posteriorXs);})
which.min(losses)
# [1] 52
## 52 also equals the mean estimate of X, which is good since it's well known that the mean is what minimizes
## the loss when the loss is squared-error so it suggests that I have not screwed up the definitions
losses[52]
[1] 128.3478462

## to calculate EVSI, we repeatedly simulate a few hundred times the existence of a hypothetical 'C' measurement
## and draw n samples from it;
## then we add the C data to our existing A & B data; run our Bayesian multilevel model again on the bigger dataset;,
## calculate what the new loss is, and compare it to the old loss to see how much the new data
## reduced the loss/mean-squared-error.
## Done for each possible n (here, 1-30) and averaged out, this tells us how much 1 additional datapoint is worth,
## 2 additional datapoints, 3 additional datapoints, etc.
sampleValues <- NULL
for (i in seq(from=1, to=30)) {

    evsis <- replicate(500, {
        n <- i

        C <- toplevel + runif(1, min=-10, max=10)
        C_1 <- rnorm(n=n, mean=C, sd=10)
        ## all as before, more or less:
        newData <- rbind(data, data.frame(Measurement="C", Y=C_1))

        jagsData <- list(N=nrow(newData), Y=newData$Y, K=length(levels(newData$Measurement)),
                         Group=newData$Measurement)
        params <- c("grand.mean","delta.between.group", "sigma.between.group", "group.delta", "group.mean",
                    "group.within.sigma")
        jEVSI <- jags(data=jagsData, parameters.to.save=params, inits=NULL, model.file=model)

        posteriorTimesEVSI <- jEVSI$BUGSoutput$sims.list[["grand.mean"]]
        lossesEVSI <- sapply(c(1:100), function (n) { lossFunction(n, posteriorTimesEVSI);})

        oldOptimum <- 128.3478462 # losses[52]
        newOptimum <- losses[which.min(lossesEVSI)]
        EVSI <- newOptimum - oldOptimum
        return(EVSI)
        }
        )

    print(i)

    print(mean(evsis))
    sampleValues[i] <- mean(evsis)
}
sampleValues
#  [1] 13.86568780 11.07101087 14.15645538 13.05296681 11.98902668 13.86866619 13.65059093 14.05991443
#  [9] 14.80018511 16.36944874 15.47624541 15.64710237 15.74060632 14.79901214 13.36776390 15.35179426
# [17] 14.31603459 13.70914727 17.20433606 15.89925289 16.35350861 15.09886204 16.30680175 16.27032067
# [25] 16.30418553 18.84776433 17.86881713 16.65973397 17.04451609 19.17173439

## As expected, the gain in reducing MSE continues increasing as data comes in but with diminishing returns;
## this is probably because in a multilevel model like this, you aren't using the _n_ datapoints to estimate X
## directly so much as you are using them to estimate a much smaller number of latent variables, which are then
## the _n_ used to estimate X. So instead of getting hyperprecise estimates of A/B/C, you need to sample from additional
## groups D/E/F/... Trying to improve your estimate of X by measuring A/B/C many times is like trying to estimate
## IQ precisely by administering a WM test a hundred times.

## If we wanted to compare with alternatives like instead sampling n data points from C and a D, it's easy to modify
## the EVSI loop to do so: generate `D <- toplevel + runif(1, min=-10, max=10); D_1 <- rnorm(n=n, mean=D, sd=10)`
## and now `rbind` D_1 in as well. At a guess, after 5-10 samples from the current group, estimates of X will be improved more
## by then sampling from a new group.

## Or the loss function could be made more realistic. It's unlikely one is paid by MSE, and if one adds in how much
## money each sample costs, with a realistic loss function, one could decide exactly how much data is optimal to collect.

## To very precisely estimate X, when our measurements are needed to measure at least 3 latent variables,
## requires much more data than usual.

## In general, we can see the drawbacks and benefits of each approach. A canned MLM
## is very fast to write but doesn't let us include prior information or easily run
## additional analyses like how much additional samples are worth. SEM works poorly
## on small samples but is still easy to write in if we have more complicated
## models of measurement error. A full-blown modeling language like JAGS is quite
## difficult to write in and MCMC is slower than other approaches but handles small
## samples without any errors or problems and offers maximal flexibility in using
## the known prior information and then doing decision-theoretic stuff. Overall for
## this problem, I think JAGS worked out best, but possibly I wasn't using LAVAAN
## right and that's why SEM didn't seem to work well.

Value of Information: clinical prediction instruments for suicide

http://slatestarcodex.com/2015/08/31/magic-markers/#comment-232970

I agree. When criticizing the study for claiming the blood levels added predictive power and it’s not clear they did, this is solely a statistical claim and can be done in a vacuum. But when one then goes on to pan the predictive power of the underlying clinical prediction instruments as useless in all circumstances, based on just the prediction stats:

So when people say We have a blood test to diagnose suicidality with 92% accuracy!, even if it’s true, what they mean is that they have a blood test which, if it comes back positive, there’s still less than 50-50 odds the person involved is suicidal. Okay. Say you’re a psychiatrist. There’s a 48% chance your patient is going to be suicidal in the next year. What are you going to do? Commit her to the hospital? I sure hope not. Ask her some questions, make sure she’s doing okay, watch her kind of closely? You’re a psychiatrist and she’s your depressed patient, you would have been doing that anyway. This blood test is not really actionable. And then remember that this isn’t the blood test we have. We have some clinical prediction instruments that do this…But having a blood test for suicide won’t be very useful, even if it works.

One is implicitly making some strong cost-benefit claims here and stepping from statistics (what are the probabilities?) to decision theory (given these probabilities, how should I act?). They are not identical: no AUC graph will ever tell you if a model’s predictions are useful or not, and there is no universal threshold where 92% specificity/sensitivity is totally useless but 95% would make a difference - these clinical prediction instruments might be useless indeed, but that will depend on costs, base rates, and available actions. (I tried to make this point to Coyne on Twitter earlier but I don’t think he understood what I was getting at & he blew me off.)

Discontinuities come from our actions; our inferences are incremental. There are some contexts where a tiny 1% improvement in AUC might be worth a lot (Wall Street) and there are some contexts where sensitivity or specificity of 99% is still useless because it won’t change your actions at all (I’m currently comparing my riding lawn mower to a robotic lawn mower, and thus far, it doesn’t matter how precise my parameters are, the robotic lawn mowers are, to my disappointment, just too expensive right now). I think p-values have shown us how well arbitrary thresholds work out in practice (and remember where they came from in the first place! decision rules set per problem - Gosset, in optimizing a brewery, did not have the pathologies we have with p<0.05 fetishism.) I also don’t believe your choices are really that restricted: you mean if you were absolutely convinced that your patient was about to commit suicide, there is absolutely nothing you could do besides treat them like any other depressive? That seems unlikely. But whatever, even if commitment is the only alternative, there is still a value to the information provided by a clinical prediction instrument, and we can calculate it, and you should if you want to rule it out as having any value, in the same way that in criticizing a study as weak, it’s better to ignore the p-values and just work out the right posterior and demonstrate directly how little evidence it contains.

Let’s try this as an example, it’s not hard or terribly complex (just tedious). So we have a ward of 100 depressive patients where we are interested in preventing suicide; our prior probability is that 7.5% or ~7 of them will commit suicide. The value of a life has been given a lot of different valuations, but $10 million is a good starting point.

Action 1:

What are our costs or losses? We could say that we expect a loss of 7.5*$10m or -$75m, and if we stand by and do no treatment or intervention whatsoever, we spend no more money and so the total loss is

0 + 0.075 * 100 * 10,000,000 = -$75,000,000

Action 2:

Let’s say they all stay by default for one week and this costs a net $1000 a day; let’s say further that, since commitment is the mentioned alternative, while committed a suicide attempt will fail. And since we know that suicides are so often spontaneous and major depression comes and goes, a frustrated suicide attempt doesn’t simply mean that they will immediately kill themselves as soon as they get out. This 7% comes from a followup period of a year, so the probability any will attempt suicide in the next week might be 0.075/52 or 0.001442307692. So this gives us our default setup: we have 100 patients staying for 7 days at a net cost of $1000 a day or $700,000 total, and by having them stay, we stop an expected average of 0.14 suicides and thus we prevent an expected loss of 0.14 * $10m = $1,440,000, for a total loss of treatment-cost minus treatment-gain plus remaining-loss:

$700,000 - (0.14 * $10m) - $10m * 100 * (0.075-(0.075/52)) = -$74,257,692.

Note that this loss is smaller than in the scenario in which we don’t do any commitment at all; since one week of suicide-watch reduced the suicide loss more than it cost, this is not surprising.

Specifically, the benefit is:

action1 - action2 = gain to switching 75000000 - 74257692 = $742,308

Not fantastic, but it’s in the right order of magnitude (you can’t expect more from a low base-rate event and a treatment with such a low probability of making a difference, after all) so it looks plausible, and it’s still more than zero. We can reject the action of not committing them at all as being inferior to committing them for one week.

Action 3:

What if we were instead choosing between one week and committing them for a full year - thus catching the full 7.5% of suicides during the 1-year followup? Does that work? First, the loss from this course of action:

((100*365.2*1000) - (0 * 10000000) - (10000000 * 100 * (0.075-(0.075/1)))) = -$36,520,000

Since there are no suicides, we avoid the default loss of -$75m, but we still have to spend $36,520,000 to pay for the long-term commitment. However, the benefit to the patients has increased dramatically since we stop so many more suicides:

action 2 - action 3 = $35,637,692.31

(We go from a loss of -$74m to a loss of -$36m.) So we see action 3 is even better than action 2 for the patients. Of course, we can’t extrapolate out any further than 1 year, because that’s what our followup number is, and we don’t know how the suicide risk falls after the 1 year point - if it drops to ~0, then further commitment is a terrible idea. So I’m not going to calculate out any further. (Since this is all linear stuff, the predicted benefit will increase smoothly over the year and so there’s no point in calculating out alternatives like 1 month, 3 months, 6 months, 9 months, etc.) What’s that, action 3 is totally infeasible and no one would ever agree to this - the patients would scream their heads off and the health insurance companies would never go for it - even if we could show that long commitments do reduce the suicide rate enough to justify the costs? And, among other things, I’ve oversimplified in assuming the 7% risk is evenly distributed over the year rather than a more plausible distribution like exponentially decreasing from Day 1, so likely commitment stops being a good idea more like month 3 or something? Yeah, you’re probably right, so let’s go back to using action 2’s loss as our current best alternative.

Now, having set out some of the choices available, we can find out how much better information is worth. First, let’s ask what the Expected Value of Perfect Information is: if we were able to take our 100 patients and exactly predict which 7 were depressive and would commit suicide this year in the absence of any intervention, where our choice is between committing them for one week or not at all. Given such information we can eject the 93 who we now know were never a suicide risk, and we hold onto the 7 endangered patients, and we have a new loss of the commitment cost of 7 people for a week vs the prevented loss of the chance they will try to commit suicide that week of this year:

((771000) - (0.14 * 10000000) - (10000000 * 7 * (1-(1/52)))) = -$70,004,846

How much did we gain from our perfect information? About $4m:

74257692 - 70004846 = $4,252,846

(This passes our sanity checks: additional information should never hurt us, so the amount should be >=$0, but we are limited by the intervention to doing very little, so the ceiling should be a low amount compared to the total loss, which this is.)

So as long as the perfect information did not cost us more than $4m or so, we would have net gained from it: we would have been able to focus commitment on the patients at maximal risk. So suppose we had a perfect test which cost $1000 a patient to run, and we wanted to know if the gained information was valuable enough to bother with using this expensive test; the answer in this case is definitely yes: with 100 patients, it’ll cost $100,000 to run the test but it’ll save $4.25m for a net profit of $4.15m. In fact, we would be willing to pay per-patient costs up to $42k, at which point we hit break-even (4252846 / 100).

OK, so that’s perfect information. What about imperfect information? Well, imperfect is a lot like perfect information, just, y’know - less so. Let’s consider this test: with the same prior, a negative on it means the patient now has P=0.007 to commit suicide that year, and a positive means P=0.48, and the sensitivity/specificity at 92%. (Just copying that from OP & ButYouDisagree, since those sound plausible.) So when we run the test on our patients, we find of the 4 possible outcomes:

• 85.1 patients are non-suicidal and the test will not flag them
• 7.4 are non-suicidal but the test will flag them
• 6.9 are suicidal and the test will flag them
• 0.6 are suicidal but the test will not flag them

So if we decide whether to commit or not commit solely based on this test, we will send home 85.1 + 0.6 = 85.7 patients (and indeed 0.6/85.7=0.007), and we will retain the remaining 7.4 + 6.9 = 14.3 patients (and indeed, 6.9/14.3=0.48). So our loss is the wrongly ejected patient of 0.6 suicides plus the cost of committing 14.3 patients (both safe and at-risk) for a week in exchange for the gain of a small chance of stopping the suicide of the 6.9 actually at risk:

(10000000*85.7*0.007) + (14.3*7*1000) + (10000000 * (0.4814.3) (1-(1/52))) = -$73,419,100

How much did we gain from our imperfect information? About $0.8m:

74257692 - 73419100 = $838,592

or $8,385.92 per patient. (This passes our sanity check: greater than $0, but much less than the perfect information. The exact amount may seem lame, but as a fraction of the value of perfect information, it’s not too bad: the test gets us 20% - 838592 / 4252846 - of the way to perfection.)

And that’s our answer: the test is not worth $0 - it’s worth $8k. And once you know what the cost of administering the test is, you simply subtract it and now you have the Net Expected Value of Information for this test. (I can’t imagine it costs $8k to administer what this sounds like, so at least in this model, the value is highly likely >$0.)

By taking the posterior of the test and integrating all the estimated costs and benefits into a single framework, we can nail down exactly how much value these clinical instruments could deliver if used to guide decision-making. And if you object to some particular parameter or assumption, just build another decision-theory model and estimate the new cost. For example, maybe commitment actually costs, once you take into account all the disruption to lives and other such side-effects, not $1000 but net of $5000 per day, what then? Then the gain halves to $438,192, etc. And if it costs $10000 then the test is worth nothing because you won’t commit anyone ever because it’s just way too expensive, and now you know it’s worth $0; or if commitment is so cheap that it’s more like $100 a day, then the test is also worth $0 because you would just commit everyone (since breakeven is then a suicide probability way below 7%, all the way at ~0.4% which is still below the 0.7% which the test can deliver, so the test result doesn’t matter for deciding whether to commit, so it’s worth $0), or if you adopt a more reasonable value of life like $20m, the value of perfect information shoots up (obviously, since the avoided loss doubles) but the value of imperfect information drops like a stone (since now that one suicidal patient sent home blows away your savings from less committing) and the test becomes worthless; and playing with the formulas, you can figure out the various ranges of assumptions in which the test has positive value and estimate how much it has under particular parameters, and of course if parameters are uncertain, you can cope with that uncertainty by embedding this in a Bayesian model to get posterior distributions of particular parameters incorporating all the uncertainty.

So to sum up: there are no hard thresholds in decision-making and imposing them can cost us better decision-making, so to claim additional information is worthless, more analysis needed, and this analysis must be done with respect to the available actions & their consequences, which even under the somewhat extreme conditions here of very weak interventions & low base-rates, suggests that the value of this information is positive.

Bayesian Model Averaging

## original: "Bayesian model choice via Markov chain Monte Carlo methods" Carlin & Chib 1995 http://stats.ma.ic.ac.uk/~das01/MyWeb/SCBI/Papers/CarlinChib.pdf
## Kobe example & data from: "A tutorial on Bayes factor estimation with the product space method", Lodewyckx et al 2011 http://ejwagenmakers.com/2011/LodewyckxEtAl2011.pdf
## Lodewyckx code can be downloaded after registration & email from http://ppw.kuleuven.be/okp/software/scripts_tut_bfepsm/

## "Table 2: Observed field goals (y) and attempts (n) by Kobe Bryant during the NBA seasons of 1999 to 2006."
kobe <- read.csv(stdin(),header=TRUE)
Year, y,   n,    y.n
1999, 554, 1183, 0.47
2000, 701, 1510, 0.46
2001, 749, 1597, 0.47
2002, 868, 1924, 0.45
2003, 516, 1178, 0.44
2004, 573, 1324, 0.43
2005, 978, 2173, 0.45
2006, 399,  845, 0.47

library(runjags)
model1<-"model{
      # 1) MODEL INDEX
      # Model index is 1 or 2.
      # Prior probabilities based on argument prior1.
      # Posterior probabilities obtained by averaging
      # over postr1 and postr2.
      M ~ dcat(p[])
      p[1] <- prior1
      p[2] <- 1-prior1
      postr1 <- 2-M
      postr2 <- 1-postr1

      # 2) MODEL LIKELIHOOD
      # For each year, successes are Binomially distributed.
      # In M1, the success rate is fixed over years.
      # In M2, the success rate is year-specific.
      for (i in 1:n.years){
       successes[i] ~ dbin(pi[M,i], attempts[i])

       pi[1,i] <- pi.fixed
       pi[2,i] <- pi.free[i]
      }

      # 3) MODEL 1 (one single rate)
      # The fixed success rate is given a Beta prior and pseudoprior.
      # Whether it is a prior or pseudoprior depends on the Model index.
      pi.fixed ~ dbeta(alpha.fixed[M],beta.fixed[M])
      alpha.fixed[1] <- alpha1.prior
      beta.fixed[1] <- beta1.prior
      alpha.fixed[2] <- alpha1.pseudo
      beta.fixed[2] <- beta1.pseudo

      # 4) MODEL 2 (multiple independent rates)
      # The year-specific success rate is given a Beta prior and pseudoprior.
      # Whether it is a prior or pseudoprior depends on the Model index.
      for (i in 1:n.years){
       pi.free[i] ~ dbeta(alpha.free[M,i],beta.free[M,i])
       alpha.free[2,i] <- alpha2.prior
       beta.free[2,i] <- beta2.prior
       alpha.free[1,i] <- alpha2.pseudo[i]
       beta.free[1,i] <- beta2.pseudo[i]
      }
      # predictive interval for hypothetical 2007 data in which Kobe makes 1000 shots:
      successes.new.1 ~ dbin(pi.fixed, 1000)
      successes.new.2 ~ dbin(pi.free[n.years], 1000)

#      success.new.weighted ~ dcat(M
  }"
# 'prior1' value from paper
data <- list("prior1"=0.000000007451, "n.years"= length(kobe$Year), "successes"=kobe$y, "attempts"=kobe$n,
             "alpha1.prior"=1, "beta1.prior"=1, "alpha2.prior"=1, "beta2.prior"=1,
             "alpha1.pseudo"=1, "beta1.pseudo"=1, "alpha2.pseudo"=rep(1,8), "beta2.pseudo"=rep(1,8) )
# inits <- function() { list(mu=rnorm(1),sd=30,t=as.vector(apply(mailSim,1,mean))) }
params <- c("pi.free", "pi.fixed", "postr1", "postr2", "M", "successes.new.1", "successes.new.2")
j1 <- run.jags(model=model1, monitor=params, data=data, n.chains=getOption("mc.cores"), method="rjparallel", sample=500000); j1
# JAGS model summary statistics from 4000000 samples (chains = 8; adapt+burnin = 5000):
#
#                  Lower95  Median Upper95    Mean      SD Mode      MCerr MC%ofSD SSeff
# pi.free[1]        0.3145 0.46864 0.98709 0.47383 0.11553   -- 0.00041958     0.4 75810
# pi.free[2]       0.10099 0.46447 0.77535 0.47005  0.1154   -- 0.00042169     0.4 74887
# pi.free[3]       0.19415  0.4692 0.86566  0.4741 0.11457   -- 0.00040171     0.4 81342
# pi.free[4]      0.020377 0.45146 0.69697 0.45867 0.11616   -- 0.00042696     0.4 74023
# pi.free[5]      0.024472 0.43846  0.7036 0.44749 0.11757   -- 0.00043352     0.4 73548
# pi.free[6]      0.076795 0.43325 0.74944 0.44318 0.11684   -- 0.00043892     0.4 70863
# pi.free[7]       0.06405 0.45033 0.73614 0.45748 0.11541   -- 0.00041715     0.4 76543
# pi.free[8]       0.30293 0.47267 0.97338 0.47708 0.11506   -- 0.00040938     0.4 79000
# pi.fixed        0.039931 0.45756 0.97903 0.49256 0.26498   -- 0.00099537     0.4 70868
# postr1                 0       0       1 0.15601 0.36287    0    0.15113    41.6     6
# postr2                 0       1       1 0.84399 0.36287    1    0.15113    41.6     6
# M                      1       2       2   1.844 0.36287    2    0.15113    41.6     6
# successes.new.1        0     463     940  492.57  265.28  454    0.99543     0.4 71019
# successes.new.2      300     473     971  477.05  116.03  473     0.4152     0.4 78094
getLogBF <- function(prior0, postr0) { log((postr0/(1-postr0)) / (prior0/(1-prior0))) }
getLogBF(0.000000007451, 0.15601)
# [1] 17.02669704
## analytic BF: 18.79; paper's MCMC estimate: 18.80; not sure where I lost 1.8 of the BF.

Dealing with all-or-nothing unreliability of data

Given two disagreeing polls, one small & imprecise but taken at face-value, and the other large & precise but with a high chance of being totally mistaken, what is the right Bayesian model to update on these two datapoints? I give ABC and MCMC implementations of Bayesian inference on this problem and find that the posterior is bimodal with a mean estimate close to the large unreliable poll’s estimate but with wide credible intervals to cover the mode based on the small reliable poll’s estimate.

A question was asked of me: what should one infer if one is given what would be definitive data if one could take it at face value - but one suspects this data might be totally incorrect? An example would be if one wanted to know what fraction of people would answer yes to a particular question, and one had a very small poll (n=10) suggesting 90% say yes, but then one was also given the results from a much larger poll (n=1000) saying 75% responded yes - but this poll was run by untrustworthy people, people that, for whatever reason, you believe might make something up half the time. You should be able to learn something from this unreliable poll, but you can’t learn everything from it because you would be burned half the time.

If not for this issue of unreliability, this would be an easy binomial problem: specify a uniform or Jeffreys prior on what percentage of people will say yes, add in the binomial data of 9/10, and look at the posterior. But what do we do with the unreliability joker?

## install.packages("devtools")
## devtools::install_github("rasmusab/bayesian_first_aid")
library(BayesianFirstAid)
b <- bayes.binom.test(oldData$Yes, oldData$N); b
# ...number of successes = 9, number of trials = 10
# Estimated relative frequency of success:
#   0.85
# 95% credible interval:
#   0.63 0.99
# The relative frequency of success is more than 0.5 by a probability of 0.994
# and less than 0.5 by a probability of 0.006

library(runjags)
model_string <- "model {
  x ~ dbinom(theta, n)
  theta ~ dbeta(1, 1) }"
model <- autorun.jags(model_string, monitor="theta", data=list(x=oldData$Yes, n=oldData$N)); model
# JAGS model summary statistics from 20000 samples (chains = 2; adapt+burnin = 5000):
#
#       Lower95  Median Upper95    Mean      SD Mode     MCerr MC%ofSD SSeff    AC.10   psrf
# theta 0.63669 0.85254  0.9944 0.83357 0.10329   -- 0.0007304     0.7 20000 0.011014 1.0004

oldData <- data.frame(Yes=9, N=10)
simulatePoll <- function(n, pr)  { rbinom(1, size=n, p=pr); }
poll_abc <- replicate(100000, {
    # draw from our uniform prior
    p <- runif(1,min=0,max=1)
    # simulate a hypothetical poll dataset the same size as our original
    newData <- data.frame(Yes=simulatePoll(oldData$N, p), N=oldData$N)
    # were they equal? if so, save sample as part of posterior
    if (all(oldData == newData)) { return(p) }
   }
  )
resultsABC <- unlist(Filter(function(x) {!is.null(x)}, poll_abc))
summary(resultsABC)
#      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.
# 0.3260816 0.7750520 0.8508855 0.8336383 0.9117471 0.9991691
hist(resultsABC)
# https://i.imgur.com/fn3XYQW.png

oldData2 <- data.frame(Yes=c(9,750), N=c(10,1000)); oldData2
#   Yes    N
# 1   9   10
# 2 750 1000
simulateHonestPoll <- function(n, pr)  { rbinom(1, size=n, p=pr); }
simulateFauxPoll <- function(n, pr, switchp) { if(sample(c(TRUE, FALSE), 1, prob=c(switchp, 1-switchp))) { rbinom(1, size=n, p=pr); } else { round(runif(1, min=0, max=n)); }}
poll_abc <- replicate(1000000, {
 priorp <- runif(1,min=0,max=1)
 switch <- 0.5
 n1 <- 10
 n2 <- 1000
 data1 <- data.frame(Yes=simulateHonestPoll(n1, priorp), N=n1)
 data2 <- data.frame(Yes=simulateFauxPoll(n2, priorp, switch), N=n2)
 newData <- rbind(data1, data2)
 if (all(oldData2 == newData)) { return(priorp) }
 }
)
resultsABC <- unlist(Filter(function(x) {!is.null(x)}, poll_abc))
summary(resultsABC)
#      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.
# 0.5256471 0.7427098 0.7584650 0.7860109 0.8133581 0.9765648
hist(resultsABC)
# https://i.imgur.com/atMz0jg.png

library(runjags)
model1 <- "model {
  for (i in 1:N) {
   y[i] ~ dbinom(theta[i], n[i])
   theta[i] <- thetaOfClust[ clust[i] ]
   clust[i] ~ dcat(pi[])
  }
  pi[1]  <- switch[1]
  pi[2]  <- switch[2]
  thetaOfClust[1] ~ dbeta(1,1)
  thetaOfClust[2] ~ dunif(0,1)
 }"
j1 <- autorun.jags(model1, monitor=c("theta"), data = list(N=nrow(oldData2), y=oldData2$Yes, n=oldData2$N, switch=c(0.5, 0.5), clust=c(1,NA))); j1
# ...      Lower95  Median Upper95    Mean       SD Mode      MCerr MC%ofSD SSeff    AC.10   psrf
# theta[1] 0.70582 0.75651 0.97263 0.77926  0.07178   --   0.001442       2  2478  0.12978 1.0011
# theta[2] 0.72446 0.75078 0.77814 0.75054 0.013646   -- 0.00009649     0.7 20000 0.009458      1
plot(j1)
# https://i.imgur.com/EaqR0dD.png

switch <- 0.5
oldData3 <- data.frame(Yes=c(9,(750*switch)), N=c(10,(1000*switch)))
b2 <- bayes.binom.test(sum(oldData3$Yes), sum(oldData3$N)); b2
#
#   Bayesian First Aid binomial test
#
# data: sum(oldData3$Yes) and sum(oldData3$N)
# number of successes = 384, number of trials = 510
# Estimated relative frequency of success:
#   0.75
# 95% credible interval:
#   0.71 0.79
# The relative frequency of success is more than 0.5 by a probability of >0.999
# and less than 0.5 by a probability of <0.001

Dysgenics power analysis

Current dysgenic estimates predict that genotypic IQ in the West are falling at a substantial rate, amounting to around half a standard deviation or more over the past century, by 1. reducing the frequency at which intelligence-increasing genetic variants occur (through natural selection against such variants) and 2. by increasing the number of new and potentially harmful genetic mutations (increasing mutation load). Estimates are produced indirectly by surveying reproductive rates or by trying to show decreases in phenotypic traits associated with intelligence; it would obviously be preferable to examine dysgenic effects directly, by observing decreases in frequencies or increases in mutation load in a large sample of Western genetic information such as SNP arrays or whole-genomes (respectively). Such direct testing of dysgenics hypotheses are becoming increasingly feasible due to the exponential decrease in SNP & whole-genome sequencing costs creating large datasets (some publicly available) and the recent identification of some intelligence genes. It remains unclear how large these datasets must be to overcome sampling error and yield informative estimates of changes in frequencies or mutation load, however; datasets like PGP or SSGAC may still be too small to investigate dysgenics. I considered the effect size estimates and under some simple models derive power calculations & power simulations of how large a dataset would be required to have an 80% chance of detecting a dysgenic effect: to detect the decrease in intelligence SNPs using SNP data, n≥30,000; to detect the increase in mutation load in whole genomes, n≥160 I then compare to available datasets: the effect on SNPs can be detected by a large number of existing proprietary databases, but there are no public databases which will be large enough in the foreseeable future; the effect on mutation load, on the other hand, can be detected using solely the currently publicly available dataset from PGP. So I conclude that while only the proprietary databases can directly test dysgenic theories of selection for the foreseeable future, there is an opportunity to analyze PGP genomes to directly test the dysgenic theory of mutation load.

The dysgenics hypothesis argues that due to observed reproductive patterns where the highly educated or intelligent tend to have fewer offspring, genotypic IQ (the upper bound on phenotypic IQs set by genes and the sort of thing measured by a polygenic score).

If dysgenics is true, then it is an extremely important phenomenon, as important as many things that get far more attention like lead remediation; but to paraphrase Richard Hamming¹, just because a problem is important does not mean it is worth working on or researching or discussing if there is no chance of making progress - if the data is hopelessly compromised by many systematic biases which would cause false positives or if the data is too scanty to overcome random error or analyses so flexible that they could deliver any answer the partisan wishes. Phenotypic data will, in all probability, never allow for a clear & decisive answer to the question of whether dysgenics exists or matters, as long-term comparisons are roughly as credible as noting that global piracy rates have declined while global warming increases, or paracetamol consumption rates have increased in tandem with Alzheimer’s rates; only direct examination of genetics will deliver the decisive answer. It would be nice to have an idea of how much genetic data we would need to overcome random error (and hence, whether it’s possible to make progress in the near future), which we can answer by doing some statistical power analyses.

Changes over time in genetics could be due to changes within a particular race or population (for example, in all white Englishmen), or could be due to population movements like one group replacing or migrating or merging into another (population genetics has revealed innumerable complex examples historically). The latter is possible thanks to the increasing availability of ancient DNA, often made public for researchers; so one could observe very long-term trends with cumulatively large effects (implying that small samples may suffice), but this approach has serious issues in interpretation and questions about how comparable intelligence variants may be across groups or throughout human evolution. With the former, there is less concern about interpretation due to greater temporal and ethnic homogeneity - if a GWAS on white northern Europeans in 2013 turns up intelligence variants and produces a useful polygenic score, it will almost certainly work on samples of white northern Europeans in 1900 too - but because the time-scale is so short the effect will be subtler and harder to detect. Nevertheless, a result within a modern population would be much more credible, so we’ll focus on that.

How subtle and hard to detect an effect are we talking about here? Woodley 2012 summarizes a number of estimates:

Early in the 20th century, negative correlations were observed between intelligence and fertility, which were taken to indicate a dysgenic fertility trend (e.g. Cattell, 1936; Lentz, 1927; Maller, 1933; Sutherland, 1929). Early predictions of the rate of dysgenesis were as high as between 1 and 1.5 IQ points per decade (Cattell, 1937, 1936)…In their study of the relationship between intelligence and both completed and partially completed fertility, van Court and Bean (1985) reported that the relationships were predominantly negative in cohorts born between the years 1912 and 1982…Vining (1982) was the first to have attempted an estimation of the rate of genotypic IQ decline due to dysgenesis with reference to a large national probability cohort of US women aged between 24 and 34 years in 1978. He identified significant negative correlations between fertility and IQ ranging from −.104 to −.221 across categories of sex, age and race, with an estimated genotypic IQ decline of one point a generation. In a 10year follow-up study using the same cohort, Vining (1995) re-examined the relationship between IQ and fertility, now that fertility was complete, finding evidence for a genotypic IQ decline of .5 points per generation. Retherford and Sewell (1988) examined the association between fertility and IQ amongst a sample of 9000 Wisconsin high-school graduates (graduated 1957). They found a selection differential that would have reduced the phenotypic IQ by .81 points per generation under the assumption of equal IQs for parents and children. With an estimate of .4 for the additive heritability of IQ, they calculated a more modest genotypic decline of approximately .33 points. The study of Ree and Earles (1991), which employed the NLSY suggests that once the differential fertility of immigrant groups is taken into consideration, the phenotypic IQ loss amongst the American population may be greater than .8 of a point per generation. Similarly, in summarizing various studies, Herrnstein & Murray (1994) suggest that it would be nearly impossible to make the total [phenotypic IQ decline] come out to less than one point per generation. It might be twice that. (p. 364). Loehlin (1997) found a negative relationship between the fertility of American women aged 35-44 in 1992 and their educational level. By assigning IQ scores to each of six educational levels, Loehlin estimated a dysgenesis rate of .8 points in one generation. Significant contributions to the study of dysgenesis have been made by Lynn, 1996 (see also: 2011) whose book Dysgenics: Genetic deterioration in modern populations provided the first estimates of the magnitude of dysgenesis in Britain over a 90 year period, putting the phenotypic loss at .069 points per year (about 1.7 points a generation assuming a generational length of 25 years). In the same study, Lynn estimated that the genotypic IQ loss was 1.64 points per generation between 1920 and 1940, which reduced to .66 points between 1950 and the present. Subsequent work by Lynn has investigated dysgenesis in other populations. For example Lynn (1999) found evidence for dysgenic fertility amongst those surveyed in the 1994 National Opinion Research Center survey, which encompassed a representative sample of American adults, in the form of negative correlations between the intelligence of adults aged 40+ and the number of children and siblings. Lynn estimates the rate of dysgenesis amongst this cohort at .48 points per generation. In a more recent study, Lynn and van Court (2004) estimated that amongst the most recent US cohort for which fertility can be considered complete (i.e. those born in the years 1940-1949), IQ has declined by .9 points per generation. At the country level, Lynn and Harvey (2008) have found evidence of a global dysgenesis of around .86 points between 1950 and 2000, which is projected to increase to 1.28 points in the period from 2000 to 2050. This projection includes the assumption that 35% of the variance in cross-country IQ differences is due to the influence of genetic factors. A subsequent study by Meisenberg (2009), found that the fertility differential between developed and developing nations has the potential to reduce the phenotypic world population IQ mean by 1.34 points per decade (amounting to a genotypic decline of .47 points per decade assuming Lynn & Harvey’s 35% estimate). This assumes present rates of fertility and pre-reproductive mortality within countries. Meisenberg (2010) and Meisenberg and Kaul (2010) have examined the factors through which intelligence influences reproductive outcomes. They found that amongst the NLSY79 cohort in the United States, the negative correlation between intelligence and fertility is primarily associated with g and is mediated in part by education and income, and to a lesser extent by more liberal gender attitudes. From this Meisenberg has suggested that in the absence of migration and with a constant environment, selection has the potential to reduce the average genotypic IQ of the US population by between .4, .8 and 1.2 points per generation.

All of these estimates are genetic selection estimates: indirect estimates inferred from IQ being a heritable trait and then treating it as a natural selection/breeding process, where a trait is selected against based on phenotype and how fast the trait decreases in each succeeding generation depends on how genetic the trait is and how harsh the selection is. So variation in these estimates (quoted estimates per generation range from .3 to 3+) is due to sampling error, differences in populations or time periods, expressing the effect by year or generation, the estimate used for heritability, reliability of IQ estimates, and whether additional genetic effects are taken into account - for example, Woodley et al 2015 finds -.262 points per decade from selection, but in another paper argues that paternal mutation load must be affecting intelligence by ~-0.84 in the general population, giving a total of -1 per decade.

Dysgenics effects should be observable by looking at genomes & SNP data with known ages/birth-years and looking for increases in total mutations or decreases in intelligence-causing SNPs, respectively.

Selection on SNPs

Without formally meta-analyzing all dysgenics studies, a good starting point on the selection effect seems like a genetic selection of 1 point per decade or 0.1 points per year or 0.007 standard deviations per year (or 0.7 standard deviations per century).

The most common available genetic data is SNP data, which sequence only the variants most common in the general population; SNP data can look at the effects of genetic selection but will not look at new mutations (since a new mutation would not be common enough to be worth putting onto a SNP chip).

Given a large sample of SNP data, a birth year (or age), and a set of binary SNP variables which cause intelligence (coded as 1 for the good variant, 0 for the others), we could formulate this as a multivariate regression: glm(cbind(SNP1, SNP2, ... SNP_N) ~ Year, family=binomial) and see if the year variable has a negative sign (increasing passage of time predicts lower levels of the good genes); if it does, this is evidence for dysgenics. Better yet, given information about the effect size of the SNPs, we could for each person’s SNP sum the net effects and then regress on a single variable, giving more precision rather than looking for independent effects on each SNP: lm(Polygenic_score ~ Year). Again a negative sign on the year variable is evidence for dysgenics.

Directional predictions are weak, and in this case we have quantitative predictions of how big the effects should be. Most of the public genomes I looked at seem to have the earliest birthdates in the 1950s or so; genomes can come from any age person (parents can give permission, and sequencing has been done prenatally) so the maximum effect is the difference between 1950 and 2015, which is 65*0.007=0.455 standard deviations (but most genomes will come from intermediate birth-dates, which are less informative about the temporal trend - in the optimal experimental design for measuring a linear trend, half the samples would be from 1950 and the other half from 2015). If the genetic total is going down by 0.455SDs, how much do the frequencies of all the good genes go down?

One simple model of genotypic IQ would be to treat it as a large number of alleles of equal binary effect: a binomial sum of n=10,000 1/0 variables with P=50% (population frequency) is reasonable. (For example, GIANT has found a large number of variants for height, and the GCTAs indicate that SNPs explain much more of variance than the top Rietveld hits currently account for; this specific model is loosely inspired by Hsu 2014.) In such a model, the average value of the sum is of course n*p=5000, and the SD is sqrt(n*p*(1-p)) or sqrt(10000*0.5*0.5) or 50. Applying our estimate of dysgenic effect, we would expect the sum to fall by 0.455*50=22.75, so we would be comparing two populations, one with a mean of 5000 and a dysgenic mean of 4977.25. If we were given access to all alleles from a sample of 1950 and 2015 genomes and so we could construct the sum, how hard would it be able to tell the difference? In this case, the sum is normally distributed as there are more than enough alleles to create normality, so we can just treat this as a two-sample normally-distributed comparison of means (a t-test), and we already have a directional effect size in mind, -0.445SDs, so:

power.t.test(delta=0.455, power=0.8, alternative="one.sided")
#      Two-sample t test power calculation
#
#               n = 60.4155602
# ...

A total n=120 is doable, but it is unlikely that we will know all intelligence genes anytime soon; instead, we know a few. A new mean of 4977 implies that since total number of alleles is the same but the mean has fallen, the frequencies must also fall and the average frequency falls from 0.5 to 4977.25/10000=0.497725. To go to the other extreme, if we know only a single gene and we want to test a fall from a frequency of 0.50 to 0.4977, we need infeasibly more samples:

power.prop.test(p1=0.5, p2=0.497725, power=0.8, alternative="one.sided")
#      Two-sample comparison of proportions power calculation
#
#               n = 597,272.2524
# ...

1.2m datapoints would be difficult to get, and so a single gene test would be unhelpful; further, a single gene could change frequencies solely through genetic drift without the change being due to dysgenic pressures.

We know a number of genes, though: Rietveld gives 4 good hits, so we can look at a polygenic score from that. They are all of similar effect size and frequency, so we’ll continue under the same assumptions of 1/0 and P=50%. The non-dysgenic average score is 4*0.5=2, sd=sqrt(4*0.5*0.5)=1. (Naturally, the SD is much larger than before because with so few random variables…) The predicted shift is from frequencies of 0.5 to 0.497, so the dysgenic scores should be 4*0.497=1.988, sd=sqrt(4*0.497*0.503)=0.999. The difference of 0.012 on the reduced polygenic score is d=((2-1.988) / 0.999)=0.012, giving a necessary power of:

power.t.test(delta=0.012006003, power=0.8)
#      Two-sample t test power calculation
#
#               n = 108904.194
# ...

So the 4 hits do reduce the necessary sample size, but it’s still not feasible to require 218k SNP datasets (unless you are 23andMe or SSGAC or an entity like that).

In the current GWAS literature, there are ~9 hits we could use, but the upcoming SSGAC paper promises: We identified 86 independent SNPs associated with EA (p<5E-8).. So how much would 86 improve over 4?

• mean old: 86*0.5=43
• sd old: sqrt(86*0.5*0.5)=4.6368
• mean new: 86*0.497=42.742
• sd new: sqrt(86*0.497*(1-0.497))=4.6367
• so d=(43-42.742)/4.63675=0.0556

power.t.test(delta=((43-42.742)/4.63675), power=0.8)
#      Two-sample t test power calculation
#
#               n = 5071.166739
# ...

So with 75, it drops from 200k to 10.1k.

To work backwards: we know with 1 hit, we need a million SNP datasets (infeasible for any but the largest proprietary databases, who have no interest in studying this hypothesis), and with all hits we need more like 200 genomes (entirely doable with just publicly available datasets like PGP), but how many hits do we need to work with an in-between amount of data like the ~2k genomes with ages I guess may be publicly available now or in the near future?

power.t.test(n=1000, power=0.8)
#     Two-sample t test power calculation
#
#              n = 1000
#          delta = 0.1253508704
hits=437;
mean1=hits*0.5; sd1=sqrt(hits*0.5*0.5);
mean2=hits*0.497; sd2=sqrt(hits*0.497*(1-0.497));
d=(mean1-mean2)/mean(c(sd1,sd2)); d
# [1] 0.1254283986

With a polygenic score drawing on 437 hits, then a sample of 2k suffices to detect the maximum decrease.

This is pessimistic because the 10k alleles are not all the same effect size and GWAS studies inherently will tend to find the largest effects first. So the first 4 (or 86) hits are worth the most. The distribution of effects is probably something like an inverse exponential distribution: many small near-zero effects and a few large ones. Rietveld 2013 released the betas for all SNPs, and the beta estimates can be plotted; each estimate is imprecise and there are artifacts in the beta sizes (SSGAC confirms that they were rounded to 3 decimals), but the distribution looks like a radioactive half-life graph, an inverse exponential distribution. With a mean of 1, we can simulate creating a set of 10k effect sizes which are exponentially distributed and have mean 5000 and SD close to (but larger than) 50 and mimics closely the binomial model:

effects <- sort(rexp(10000)/1, decreasing=TRUE)
genomeOld <- function() { ifelse(sample(c(FALSE,TRUE), prob=c(0.5, 0.5), 10000, replace = TRUE), 0, effects) }
mean(replicate(10000, sum(genomeOld())))
# [1] 5000.270218
sd(replicate(10000, sum(genomeOld())))
# [1] 69.82652816
genomeNew <- function() { ifelse(sample(c(FALSE,TRUE), prob=c(0.497, 1-0.497), 10000, replace = TRUE), 0, effects) }

With a dysgenic effect of -0.445SDs, that’s a fall of the sum of random exponentials of ~31, which agrees closely with the difference in polygenic genome scores:

mean(replicate(10000, sum(genomeOld() - genomeNew())))
# [1] 29.75354558

For each draw from the old and new populations, we can take the first 4 alleles, which were the ones assigned the largest effects, and build a weak polygenic score and compare means. For example:

polyNew <- replicate(1000, sum(genomeNew()[1:4]))
polyOld <- replicate(1000, sum(genomeOld()[1:4]))
t.test(polyOld, polyNew, alternative="greater")
#   Welch Two Sample t-test
#
# data:  polyOld and polyNew
# t = 0.12808985, df = 1995.8371, p-value = 0.8980908
# alternative hypothesis: true difference in means is not equal to 0
# 95 percent confidence interval:
#  -0.7044731204  0.8029267301
# sample estimates:
#   mean of x   mean of y
# 17.72741040 17.67818359

Or to mimic 86 hits:

t.test(replicate(1000, sum(genomeOld()[1:86])), replicate(1000, sum(genomeNew()[1:86])))
#
#     Welch Two Sample t-test
#
# t = 1.2268929, df = 1997.6307, p-value = 0.2200074
# alternative hypothesis: true difference in means is not equal to 0
# 95% confidence interval:
#  -0.8642674547  3.7525210076
# sample estimates:
#   mean of x   mean of y
# 244.5471658 243.1030390

Using the exponential simulation, we can do a parallelized power analysis: simulate draws (i=300) & tests for a variety of sample sizes to get an idea of what sample size we need to get decent power with 86 hits.

library(ggplot2)
library(parallel) # warning, Windows users
library(plyr)

genomeOld <- function(efft) { ifelse(sample(c(FALSE,TRUE), prob=c(0.5, 0.5), length(efft), replace = TRUE), 0, efft) }
genomeNew <- function(efft) { ifelse(sample(c(FALSE,TRUE), prob=c(0.497, 1-0.497), length(efft), replace = TRUE), 0, efft) }

simulateStudy <- function(n, hits) {
        effects <- sort(rexp(10000)/1, decreasing=TRUE)[1:hits]
        polyOld <- replicate(n, sum(genomeOld(effects)))
        polyNew <- replicate(n, sum(genomeNew(effects)))
        t <- t.test(polyOld, polyNew, alternative="greater")
        return(data.frame(N=n, P=t$p.value, PO.mean=mean(polyOld), PO.sd=sd(polyOld), PN.mean=mean(polyNew), PN.sd=sd(polyNew))) }

hits <- 86
parallelStudies <- function(n, itr) { ldply(mclapply(1:itr, function(x) { simulateStudy(n, hits); })); }

sampleSizes <- seq(500, 5000, by=100)
iters <- 300
powerExponential <- ldply(lapply(sampleSizes, function(n) { parallelStudies(n, iters) })); summary(powerExponential)
#        N              P                  PO.mean             PO.sd             PN.mean
#  Min.   : 500   Min.   :0.000000000   Min.   :222.5525   Min.   :23.84966   Min.   :221.2894
#  1st Qu.:1600   1st Qu.:0.002991554   1st Qu.:242.8170   1st Qu.:26.46606   1st Qu.:241.3242
#  Median :2750   Median :0.023639517   Median :247.2059   Median :27.04467   Median :245.7044
#  Mean   :2750   Mean   :0.093184735   Mean   :247.3352   Mean   :27.06300   Mean   :245.8298
#  3rd Qu.:3900   3rd Qu.:0.107997575   3rd Qu.:251.7787   3rd Qu.:27.64103   3rd Qu.:250.2157
#  Max.   :5000   Max.   :0.997322161   Max.   :276.2614   Max.   :30.67000   Max.   :275.7741
#      PN.sd
#  Min.   :23.04527
#  1st Qu.:26.45508
#  Median :27.04299
#  Mean   :27.05750
#  3rd Qu.:27.63241
#  Max.   :30.85065
powerExponential$Power <- powerExponential$P<0.05
powers <- aggregate(Power ~ N, mean, data=powerExponential); powers
# 1   500 0.2133333333
# 2   600 0.2833333333
# 3   700 0.2833333333
# 4   800 0.3133333333
# 5   900 0.3033333333
# 6  1000 0.3400000000
# 7  1100 0.4066666667
# 8  1200 0.3833333333
# 9  1300 0.4133333333
# 10 1400 0.4166666667
# 11 1500 0.4700000000
# 12 1600 0.4600000000
# 13 1700 0.4666666667
# 14 1800 0.4733333333
# 15 1900 0.5233333333
# 16 2000 0.5366666667
# 17 2100 0.6000000000
# 18 2200 0.5900000000
# 19 2300 0.5600000000
# 20 2400 0.6066666667
# 21 2500 0.6066666667
# 22 2600 0.6700000000
# 23 2700 0.6566666667
# 24 2800 0.7133333333
# 25 2900 0.7200000000
# 26 3000 0.7300000000
# 27 3100 0.7300000000
# 28 3200 0.7066666667
# 29 3300 0.7433333333
# 30 3400 0.7133333333
# 31 3500 0.7233333333
# 32 3600 0.7200000000
# 33 3700 0.7766666667
# 34 3800 0.7933333333
# 35 3900 0.7700000000
# 36 4000 0.8100000000
# 37 4100 0.7766666667
# 38 4200 0.8000000000
# 39 4300 0.8333333333
# 40 4400 0.8466666667
# 41 4500 0.8700000000
# 42 4600 0.8633333333
# 43 4700 0.8166666667
# 44 4800 0.8366666667
# 45 4900 0.8666666667
# 46 5000 0.8800000000
qplot(N, Power, data=powers)  + stat_smooth()

So for a well-powered two-group comparison of 1950 & 2015 SNP datasets using 86 SNPs, we would want ~4000 in each group for a total n=8000; we do have nontrivial power even at a total n=1000 (500 in each group means 21% power) but a non-statistically-significant result will be difficult to interpret and if one wanted to do that, reporting a Bayes factor from a Bayesian hypothesis test would make much more sense to express clearly whether the (non-definitive) data is evidence for or against dysgenics.

This is still too optimistic since we assumed the optimal scenario of only very old and very new genomes, while available genomes are more likely to be distributed fairly uniformly between 1950 and 2015. Per Optimal design in psychological research, McClelland 1997, we expect a penalty of ~2x in sample size efficiency in going from the optimal two-group extreme endpoints design to samples being uniformly distributed (due to much of our sample size being wasted on estimating small effects) and so we would expect our sample size requirement to at least double to around n=16000, but we can do a power simulation here as well. To get the effect size for each year, we simply split the frequency decrease over each year and generate hypothetical genomes with less of a frequency decrease uniformly distributed 1950-2015, and do a linear regression to get a p-value for the year predictor:

hits <- 86
sampleSizes <- seq(8000, 30000, by=1000)
iters <- 100
genome <- function(effects) {
  t <- sample(c(1:(2015-1950)), 1)
  decreasedFrequency <- 0.5 - (((0.5-0.497)/(2015-1950)) * t)
  geneFlips <- sample(c(FALSE,TRUE), prob=c(decreasedFrequency, 1-decreasedFrequency), replace = TRUE, length(effects))
  geneValues <- ifelse(geneFlips, effects, 0)
  return(data.frame(Year=1950+t,
                    PolygenicScore=sum(geneValues)))
  }
simulateStudy <- function(n, hits) {
        effects <- sort(rexp(10000)/1, decreasing=TRUE)[1:hits]
        d <- ldply(replicate(n, genome(effects), simplify=FALSE))
        l <- lm(PolygenicScore ~ Year, data=d)
        p <- anova(l)$`Pr(>F)`[1]
        return(data.frame(N=n, P=p, PO.mean=predict(l, newdata=data.frame(Year=1950)),
                                           PN.mean=predict(l, newdata=data.frame(Year=2015)))) }
parallelStudies <- function(n, itr) { ldply(mclapply(1:itr, function(x) { simulateStudy(n, hits); })); }
powerExponentialDistributed <- ldply(lapply(sampleSizes, function(n) { parallelStudies(n, iters) })); summary(powerExponential)
powerExponentialDistributed$Power <- powerExponentialDistributed$P<0.05
powers <- aggregate(Power ~ N, mean, data=powerExponentialDistributed); powers
#        N Power
# 1   8000  0.27
# 2   9000  0.32
# 3  10000  0.35
# 4  11000  0.33
# 5  12000  0.41
# 6  13000  0.34
# 7  14000  0.41
# 8  15000  0.48
# 9  16000  0.55
# 10 17000  0.62
# 11 18000  0.55
# 12 19000  0.60
# 13 20000  0.69
# 14 21000  0.61
# 15 22000  0.65
# 16 23000  0.63
# 17 24000  0.71
# 18 25000  0.67
# 19 26000  0.71
# 20 27000  0.74
# 21 28000  0.70
# 22 29000  0.79
# 23 30000  0.83
qplot(N, Power, data=powers)  + stat_smooth()

In this case, the power simulation suggestions the need for triple rather than double the data, and so a total of n=30,000 to be well-powered.

power.t.test(d=(130/206), power=0.8)
#      Two-sample t test power calculation
#
#               n = 40.40035398
# ...

Power analysis for racial admixture studies of continuous variables

I consider power analysis of a genomic racial admixture study for detecting genetic group differences affecting a continuous trait such as IQ in US African-Americans, where ancestry is directly measured by genome sequencing and the comparisons are all within-family to eliminate confounding by population structure or racism/colorism/discrimination. The necessary sample size for well-powered studies is closely related to the average size of differences in ancestry percentage between siblings, as the upper bound on IQ effect per percentage is small, requiring large differences in ancestry to detect easily. A within-family comparison of siblings, due to the relatively small differences in ancestry between siblings estimated from IBD measurements of siblings, might require n>50,000 pairs of siblings to detect possible effects on IQ, an infeasible sample size. An alternative design focuses on increasing the available ancestry differences within a family unit by comparing adoptees with siblings; the larger within-population standard deviation of ancestry creates larger & more easily-detected IQ differences. A random-effects meta-analysis of past admixture & ancestry studies suggests the SD in heterogeneous samples may range from 2% to 20% with a mean of 11% (95% predictive interval), yielding sample sizes of n>20,000, n=1100, and n=500. Hence, an adoption study is probably in the feasible range, with required sample sizes comparable to annual adoption rates among US African-Americans.

Admixture studies examine racial phenotypic differences in traits such as blood pressure by comparing people with ancestry from multiple groups, and correlating differences in ancestry percentage with differences in the phenotype. So, for example, African-Americans have higher blood-pressure than white Americans, and most African-Americans have an average white ancestry of something like 20-25% (see later); if having 26% white ancestry predicts slightly lower blood pressure while 24% predicts higher, that suggests the difference is (as is currently believed) genetic; and this logic can be used to narrow down to specific chromosome regions, and has contributed to study of racial differences in disease.

One application would be to thorny questions like potential group differences in non-medical traits like intelligence. The standard admixture design, requiring a few thousand subjects spanning the full range, might not necessarily work here here because of the claimed environmental effects. A proposed resolution to the question is to do an admixture study comparing African-American siblings. Siblings are highly genetically related on average (50%) but in a randomized fashion due to recombination; so two siblings, including fraternal twins, born to the same parents in the same family in the same neighborhood going to the same schools, will nevertheless have many different variants, and will differ in how related they are - the average is 50% but it could be as low as 45% or high as 55%. So given two siblings, they will differ slightly in their white ancestry, and if indeed white ancestry brings with it more intelligence variants, then the sibling with a higher whiter percentage ought to be slightly more intelligent on average, and this effect will have to be causal, as the inheritance is randomized and all other factors are equal by design. (A result using ancestry percentages measured in the general population, outside families, would be able to make far more powerful comparisons by comparing people with ~0% white ancestry to those with anywhere up to 100%, and require small sample sizes, and such analyses have been done with the expected result, but are ambiguous & totally unconvincing, as the correlation of greater whiteness with intelligence could easily be due to greater SES or greater blackness could be a marker for recent immigration or any of a number of confounds that exist.) This has historically been difficult or impossible since how does one measure the actual ancestry in siblings? But with the rise of cheap genotyping, precise measure of actual (rather than average) ancestry can be done for <$100, so that is no longer an obstacle.

588 * 0.5*(1-0.5)
# [1] 147

sqrt((588 * 0.5*(1-0.5)))
# [1] 12.12435565
12/147
# [1] 0.08163265306

L=35; sqrt(1/(16*L) - (1/(3*L^2)))
# [1] 0.03890508247

n <- 10000
siblings <- data.frame(
sibling1AncestryPercentage = rnorm(n, mean=25, sd=1),
sibling1NonancestryIQ = rnorm(n, mean=0, sd=12),

sibling2AncestryPercentage = rnorm(n, mean=25, sd=1),
sibling2NonancestryIQ = rnorm(n, mean=0, sd=12))

siblings$sibling1TotalIQ <- with(siblings, sibling1NonancestryIQ + sibling1AncestryPercentage*(0.008*15))
siblings$sibling2TotalIQ <- with(siblings, sibling2NonancestryIQ + sibling2AncestryPercentage*(0.008*15))
siblings$siblingAncestryDifference <- with(siblings, sibling1AncestryPercentage - sibling2AncestryPercentage)
siblings$siblingIQDifference <- with(siblings, sibling1TotalIQ - sibling2TotalIQ )

summary(siblings)
# ...
# siblingAncestryDifference siblingIQDifference
# Min.   :-5.370128122      Min.   :-68.2971343
# 1st Qu.:-0.932086950      1st Qu.:-11.7903864
# Median : 0.002384529      Median : -0.2501536
# Mean   : 0.007831583      Mean   : -0.4166863
# 3rd Qu.: 0.938513265      3rd Qu.: 11.0720667
# Max.   : 5.271052675      Max.   : 67.5569825
summary(lm(siblingIQDifference ~ siblingAncestryDifference, data=siblings))
# ...Coefficients:
#                             Estimate Std. Error  t value  Pr(>|t|)
# (Intercept)               -0.4192761  0.1705125 -2.45892 0.0139525
# siblingAncestryDifference  0.3306871  0.1220813  2.70874 0.0067653
#
# Residual standard error: 17.05098 on 9998 degrees of freedom
# Multiple R-squared:  0.000733338,    Adjusted R-squared:  0.0006333913
# F-statistic: 7.337294 on 1 and 9998 DF,  p-value: 0.006765343
confint(lm(siblingIQDifference ~ siblingAncestryDifference, data=siblings))
#                                    2.5 %         97.5 %
# (Intercept)               -0.75351500523 -0.08503724643
# siblingAncestryDifference  0.09138308561  0.56999105507

admixtureTest <- function(n, alpha=0.05, ancestryEffect=0.008) {
 siblings <- data.frame(
     sibling1AncestryPercentage =pmax(0, rnorm(n, mean=25, sd=1)),
     sibling1NonancestryIQ = rnorm(n, mean=0, sd=12),


     sibling2AncestryPercentage = pmax(0,rnorm(n, mean=25, sd=1)),
     sibling2NonancestryIQ = rnorm(n, mean=0, sd=12))

 siblings$sibling1TotalIQ <- with(siblings, sibling1NonancestryIQ + sibling1AncestryPercentage*(ancestryEffect*15))
 siblings$sibling2TotalIQ <- with(siblings, sibling2NonancestryIQ + sibling2AncestryPercentage*(ancestryEffect*15))
 siblings$siblingAncestryDifference <- with(siblings, sibling1AncestryPercentage - sibling2AncestryPercentage)
 siblings$siblingIQDifference <- with(siblings, sibling1TotalIQ - sibling2TotalIQ )

 p <- summary(lm(siblingIQDifference ~ siblingAncestryDifference, data=siblings))$coefficients[8]
 return(p<alpha)
}

power <- function(n, iters=10000, n.parallel=8) {
    library(parallel)
    library(plyr)
    mean(unlist(mclapply(1:n.parallel, function(i) {
        replicate(iters/n.parallel, admixtureTest(n)) }))) }

# powers <- sapply(seq(100, 10000, by=10), power)
power(100)
# [1] 0.0502
power(500)
# [1] 0.0535
power(1500)
# [1] 0.0642
power(15000)
# [1] 0.2251
power(50000)
# [1] 0.6077

Adoption power analysis

One possibility is to drop the idea of using only biological siblings. Is there any way to have ancestry differences as large as in the general population, but within a family? Half-siblings come to mind but those more typically tend to join the household as older kids or teenagers, so aren’t so good. One possibility is adoptees: there are a substantial number of African-American children adopted into other African-American households (white parents adopting black children is controversial and rarer, described as decreasing dramatically, but still substantial in total numbers, at least 20,000), and even a noticeable number of African children adopted abroad (14,800 from Ethiopia just 1999-2014, with more adoption from Nigeria & the Congo). The same logic of the within-family study should apply but to unrelated siblings who will have far greater differences in ancestry now (possibly anywhere up to 50% if an African child is adopted into an African-American family with considerable white ancestry & some luck). This would increase power dramatically, perhaps enough to bring the study within the realm of near-future feasibility.

Examining adoptions of African children would not be a convincing way of establishing group differences, particularly for IQ, as there are many known environmental harms (eg pre-natal lack of iodine is known to cause large impairments in cognitive which cannot be repaired later in life, and poor iodization is frequent in Africa), so while examining African adoptees would doubtless require a very small sample size, the results would be uninterpretable. So the more interesting case is instead examining AA adoptees/siblings, all of whom are raised in a wealthy (and iodine-sufficient) industrialized country.

In this case, we’re considering a pair of an AA sibling with the same IQ & ancestry distributions, as compared with adoptees who are either African (100% African ancestry) or likewise have the same IQ/ancestry distributions. Since the sibling & adoptee are unrelated, they effectively vary & differ as much as two random people from the general population would in IQ & African ancestry, except for shared-environment effects on IQ; shared-environment for adult IQ is relatively low, maybe 10% of variance. So instead of an SD of 15, they would vary moderately less, like 14.23 points (sqrt(15^2 * 0.9)).

One assumption here is a shared mean: one would actually expect, given the association of lighter skin with higher wealth/SES and darker with lower wealth/SES, that the adopting parents (and hence their biological children) would be relatively high on European ancestry, and conversely, the mothers giving up children for adoption would be relatively low, so the expected difference in ancestry is higher than simulated. Assuming equal means, however, is a conservative assumption since if such a correlation holds, the differences will be larger, hence the ancestry effect sizes larger, hence smaller sample sizes required. In the extreme version of this, the adoptive family is white and so the ancestry difference is maximal (~99% vs ~20%), requiring even smaller sample sizes, but at the cost of introducing complications like whether there are interactions with the white adoptive family not present in an AA adoptive family; in any case, such trans-racial adoption is apparently unpopular now, so it may not come up much.

Mean population European ancestry & population standard deviation

Ancestry effects remain as before; the mean ancestry is not too important as long as it’s not near 0, but since adoptees are drawn from the general population, the ancestry SD must be adjusted but it’s unclear what the right SD here is - cited studies range from 4% up to 11%, and this is a key parameter for power (with 4%, then sibling and adoptee will tend to be quite similar on ancestry percentage & much more data will be required, but with 11% they will differ a good deal and make results stronger).

Reported figures from the genetics literature for European ancestry in US African-American range from 14% to 24%, reflecting both sampling error and various biases & self-selection & geographic/regional effects in the datasets:

• Halder et al 2008, A Panel of Ancestry Informative Markers for Estimating Individual Biogeographical Ancestry and Admixture From Four Continents: Utility and Applications: 14.3%, SD 13.3% (n=136)
• Ducci et al 2009, Association of Substance Use Disorders With Childhood Trauma but not African Genetic Heritage in an African American Cohort: 7%, SD 9% (n=864); this is a little odd since they allocate the 21% non-African ancestry (African ancestry: 079%, SD 14%) to Europe, Middle East, Central Asia, Native American, East Asia, and Oceania, but it’s hard to imagine that really corresponds to the ancestry of New Jersey African-Americans; if we ignore that attempt at splitting the non-African ancestry, 21% & SD 14% might be more consistent.
• Gravlee et al 2009, Genetic ancestry, social classification, and racial inequalities in blood pressure in Southeastern Puerto Rico: European ancestry isn’t reported and Puerto Ricans have too much other ancestry to simply subtract the reported African ancestries
• Signorello et al 2010, Blood vitamin D levels in relation to genetic estimation of African ancestry, Table 1: mean African ancestry of 92.9% implies European ancestry ~7.1%, ~IQR of African ancestry implies SD ~8%, (n=379)
• Bryc et al 2010, Genome-wide patterns of population structure and admixture in West Africans and African Americans: median 18.5%, 25th–75th percentiles: 11.6–27.7%; no mean or SD reported in the paper or supplemental materials I could find, but the median/percentiles suggest an SD of ~4.65% (qnorm(0.25) * (0.116 - 0.185); n=365)
• Nassir et al 2012, Relationship between Adiposity and Admixture in African American and Hispanic American Women: 22.5%, SD 14.7% (n=11712)

• Henry Louis Gates Jr summarizes results available to him in February 2013: 29%, 22%, 19%, 19% (unspecified sample sizes & population SDs)

  • According to Ancestry.com, the average African American is 65% sub-Saharan African, 29% European and 2% Native American.
  • According to 23andme.com, the average African American is 75% sub-Saharan African, 22% European and only 0.6% Native American.
  • According to Family Tree DNA.com, the average African American is 72.95% sub-Saharan African, 22.83% European and 1.7% Native American.
  • According to National Geographic’s Genographic Project, the average African American is 80% sub-Saharan African, 19% European and 1% Native American.
  • According to AfricanDNA, in which I am a partner with Family Tree DNA, the average African American is 79% sub-Saharan African, 19% European and 2% Native American.

• Bryc et al 2014, The Genetic Ancestry of African Americans, Latinos, and European Americans across the United States: mean 24%, SD not reported, and Bryc declined to provide any information about the study & population SD when contacted but pixel-counting Figure S1/Figure S18 yields the mean of 24% and an SD of ~17% (n=5269)

• Baharian et al 2016, The Great Migration and African-American Genomic Diversity: reports 3 cohorts with European ancestry at:

  • SCCS: 14(13.65-14.43)% (n=2128)
  • HRS: 16.7(16.16-17.27)% (n=1501)
  • ASW: 21.3(19.50-23.20)% (n=97)

  but no population SDs, with statistically-significant differences likely due to recruiting:

  The overall proportion of African ancestry is substantially higher in the SCCS and HRS than in the ASW and the recently published 23andMe cohort [12] (Table 1). The HRS cohort can be thought of as representative of the entire African-American population, while the SCCS focuses primarily on individuals attending community health centers in rural, underserved locations in the South. By contrast, the sampling for the ASW and 23andMe did not aim for specific representativeness, and the ascertainment in the 23andMe cohort might have enriched for individuals with elevated European ancestry (see Materials and Methods and discussion in [12]). In the HRS, average African ancestry proportion is 83% in the South and lower in the North (80%, bootstrap p= 6 × 10−6) and West (79%, p= 10−4) (Fig 1). Within the SCCS, African ancestry proportion is highest in Florida (89%) and South Carolina (88%) and lowest in Louisiana (75%) with all three significantly different from the mean (Florida p=0.006, South Carolina p=4 × 10−4, and Louisiana p< 10−5; bootstrap). The elevated African ancestry proportion in Florida and South Carolina is also observed in the HRS and in the 23andMe study [12], but Louisiana is more variable across cohorts (Fig 1E). As expected, European ancestry proportions largely complement those of African ancestry across the US.

• Kirkegaard et al 2016, Biogeographic ancestry, cognitive ability, and socioeconomic outcomes’s supplementary information: 17%, SD 11% (n=140)

Considering just studies with usable ancestry percentages, population SD, and n, and using inferred SDs from Signorello:

admixture <- read.csv(stdin(), header=TRUE, colClasses=c("factor", "numeric", "numeric", "integer"))
Study,Mean,SD,N
"Halder et al 2008",0.143,0.133,136
"Ducci et al 2009",0.07,0.09,864
"Signorello et al 2010",0.071,0.08,379
"Bryc et al 2010",0.185,0.0465,365
"Nassir et al 2012",0.225,0.147,11712
"Bryc et al 2014",0.24,0.17,5269
"Kirkegaard et al 2016",0.17,0.11,140

# what is the standard error/precision of a population SD? http://davidmlane.com/hyperstat/A19196.html
admixture$SD.SE <- (0.71*admixture$SD) / sqrt(admixture$N)
summary(admixture)
#                    Study        Mean                 SD                  N               SD.SE
#  Bryc et al 2010      :1   Min.   :0.0700000   Min.   :0.0465000   Min.   :  136.0   Min.   :0.0009644066
#  Bryc et al 2014      :1   1st Qu.:0.1070000   1st Qu.:0.0850000   1st Qu.:  252.5   1st Qu.:0.0016954481
#  Ducci et al 2009     :1   Median :0.1700000   Median :0.1100000   Median :  379.0   Median :0.0021739221
#  Halder et al 2008    :1   Mean   :0.1577143   Mean   :0.1109286   Mean   : 2695.0   Mean   :0.0034492579
#  Kirkegaard et al 2016:1   3rd Qu.:0.2050000   3rd Qu.:0.1400000   3rd Qu.: 3066.5   3rd Qu.:0.0047591374
#  Nassir et al 2012    :1   Max.   :0.2400000   Max.   :0.1700000   Max.   :11712.0   Max.   :0.0080973057
#  Signorello et al 2010:1

library(metafor)
r.mean <- rma(yi=Mean, sei=SD/sqrt(N), measure="SMD", ni=N, data=admixture); r.mean
# Random-Effects Model (k = 7; tau^2 estimator: REML)
#
# tau^2 (estimated amount of total heterogeneity): 0.0046 (SE = 0.0027)
# tau (square root of estimated tau^2 value):      0.0680
# I^2 (total heterogeneity / total variability):   99.82%
# H^2 (total variability / sampling variability):  566.51
#
# Test for Heterogeneity:
# Q(df = 6) = 3477.2614, p-val < .0001
#
# Model Results:
#
# estimate       se     zval     pval    ci.lb    ci.ub
#   0.1578   0.0258   6.1187   <.0001   0.1072   0.2083
predict(r.mean)
#   pred     se  ci.lb  ci.ub  cr.lb  cr.ub
# 0.1578 0.0258 0.1072 0.2083 0.0153 0.3003

r.sd <- rma(yi=SD, sei=SD.SE, measure="SMD", ni=N, data=admixture); r.sd
# Random-Effects Model (k = 7; tau^2 estimator: REML)
#
# tau^2 (estimated amount of total heterogeneity): 0.0018 (SE = 0.0011)
# tau (square root of estimated tau^2 value):      0.0425
# I^2 (total heterogeneity / total variability):   99.77%
# H^2 (total variability / sampling variability):  440.67
#
# Test for Heterogeneity:
# Q(df = 6) = 3819.2793, p-val < .0001
#
# Model Results:
#
# estimate       se     zval     pval    ci.lb    ci.ub
#   0.1108   0.0162   6.8587   <.0001   0.0792   0.1425
predict(r.sd)
#    pred     se  ci.lb  ci.ub  cr.lb  cr.ub
#  0.1108 0.0162 0.0792 0.1425 0.0216 0.2001

par(mfrow=c(2,1))
forest(r.mean, slab=admixture$Study)
forest(r.sd, slab=admixture$Study)

There is high heterogeneity, as expected, and the meta-analytic summary are consistent with simply taking the mean, so meta-analysis was not really necessary.

The issue of heterogeneity depends on how one wants to interpret these numbers: as the true latent African-American population mean/SD of European ancestry, or as a way to estimate the possible spread of sampling? In the former, the heterogeneity is a serious issue because it suggests the estimate may be badly biased or at least is highly imprecise; in the latter, it is both a curse and a benefit, since it implies that it is possible to recruit for genetics studies samples with a wide range of ancestry (thereby greatly increasing statistical power) but also that one might get unlucky & wind up with a very ancestry-homogeneous sample (if the sample turns out to have an SD as high as 20%, excellent; if it’s as low as 7.9%, one is in trouble).

So for power analysis one might check the meta-analytic mean case, as well as the prediction interval (a 95% CI around the SD/mean does not mean that 95% of the true effects, including the inherent heterogeneity, will fall in that interval): SDs of 2%, 11%, and 20%. (For any cost-benefit analysis or trying to optimize expenditures, one would want to work with the posterior distributions to average over everything, but for just general informative purposes, those 3 are good parameters to check.)

Power simulation

Code:

adopteeTest <- function(n, alpha=0.05, ancestryEffect=0.008, populationAncestryMean=0.1440, populationAncestrySD=0.1008, c=0.1) {
 unrelatedSiblingSD <- sqrt(15^2 * (1-c)) # subtract 10% for same shared-environment
 siblings <- data.frame(
     sibling1AncestryPercentage = pmax(0, rnorm(n, mean=populationAncestryMean*100, sd=populationAncestrySD*100)),
     sibling1NonancestryIQ = rnorm(n, mean=0, sd=unrelatedSiblingSD),

     adopteeAncestryPercentage = pmax(0, rnorm(n, mean=populationAncestryMean*100, sd=populationAncestrySD*100)),
     adopteeNonancestryIQ = rnorm(n, mean=0, sd=unrelatedSiblingSD))

 siblings$sibling1TotalIQ <- with(siblings, sibling1NonancestryIQ + sibling1AncestryPercentage*(ancestryEffect*15))
 siblings$adopteeTotalIQ  <- with(siblings, adopteeNonancestryIQ + adopteeAncestryPercentage*(ancestryEffect*15))
 siblings$siblingAncestryDifference <- with(siblings, sibling1AncestryPercentage - adopteeAncestryPercentage)
 siblings$siblingIQDifference       <- with(siblings, sibling1TotalIQ - adopteeTotalIQ )

 p <- summary(lm(siblingIQDifference ~ siblingAncestryDifference, data=siblings))$coefficients[8]
 return(p<alpha)
}

power <- function(n, sd, iters=10000, n.parallel=8) {
    library(parallel)
    library(plyr)
    mean(unlist(mclapply(1:n.parallel, function(i) {
    replicate(iters/n.parallel, adopteeTest(n, populationAncestrySD=sd)) }))) }

ns <- seq(100, 10000, by=100)
powerLow  <- sapply(ns, function(n) { power(n, sd=0.0216)})
powerMean <- sapply(ns, function(n) { power(n, sd=0.1108)})
powerHigh <- sapply(ns, function(n) { power(n, sd=0.2001)})

library(ggplot2); library(gridExtra)
pl <- qplot(ns, powerLow)  + coord_cartesian(ylim = c(0,1))
pm <- qplot(ns, powerMean) + coord_cartesian(ylim = c(0,1))
ph <- qplot(ns, powerHigh) + coord_cartesian(ylim = c(0,1))
grid.arrange(pl, pm, ph, ncol=1)

So for the worst-case SD, sample size is unclear but n>20,000 pairs; mean SD, n=1100 pairs; high SD, n=500 pairs. The latter two are feasible amounts for population registries or adoption-focused cohort studies. Thus genome adoption studies, combined with the much less powerful but more common within-sibling comparisons, are capable of delivering precise answers to long-standing questions about the origins of group differences with moderate sample sizes.

Operating on an aneurysm

In the excellent neurosurgery memoir Do No Harm: Stories of Life, Death, and Brain Surgery (Henry Marsh 2014), chapter 2 Aneurysm, there is a passage on weighing the costs of action and inaction:

A thirty-two-year-old woman, he said tersely. For surgery today. Had some headaches and had a brain scan. As he talked a brain scan flashed up on the wall.

…It’s an unruptured aneurysm, seven millimetres in size, Fiona - the most experienced of the registrars - said. So there’s a point zero five per cent risk of rupture per year according to the international study published in 1998. And if it ruptures? Fifteen per cent of people die immediately and another thirty per cent die within the next few weeks, usually from a further bleed and then there’s a compound interest rate of four per cent per year.

…If we did nothing the patient might eventually suffer a haemorrhage which would probably cause a catastrophic stroke or kill her. But then she might die years away from something else without the aneurysm ever having burst. She was perfectly well at the moment, the headaches for which she had had the scan were irrelevant and had got better. The aneurysm had been discovered by chance. If I operated I could cause a stroke and wreck her - the risk of that would probably be about four or five per cent. So the acute risk of operating was roughly similar to the life-time risk of doing nothing. Yet if we did nothing she would have to live with the knowledge that the aneurysm was sitting there in her brain and might kill her any moment.

Reading this, I was a little surprised by Marsh’s evaluation given those specific numbers. Intuitively, it did not seem to me that a single risk of 5% was anywhere near as bad as a lifelong risk of 0.5%, for a 32 year old woman who would probably live another 50 years - the one number is 10x bigger than the other, but the other number is 50x bigger, and a quick heuristic for the total probability of many independent small probabilities is to just sum them up, suggesting that the risk of the untreated aneurysm was much worse (50*0.005=0.25, and 0.25>0.05). So I thought after I finished reading the book, I would work it out a little more accurately.

The Power of Twins: Revisiting Student’s Scottish Milk Experiment Example

Randomized experiments require more subjects the more variable each datapoint is to overcome the noise which obscures any effects of the intervention. Reducing noise enables better inferences with the same data, or less data to be collected, which can be done by balancing observed characteristics between control and experimental datapoints. A particularly dramatic example of this approach is running experiments on identical twins rather than regular people, because twins vary far less from each other than random people do. In 1931, the great statistician Student noted problems with an extremely large (n=20,000) Scottish experiment in feeding children milk (to see if they grew more in height or weight), and claimed that the experiment could have been done far more cost-effectively with an extraordinary reduction of >95% fewer children if it had been conducted using twins. He, however, did not provide any calculations or data demonstrating this. I revisit the issue and run a power calculation on height indicating that Student’s claims were correct and that the experiment would have required ~97% fewer children if run with twins. This reduction is not unique to the Scottish experiment and in general, one can expect a reduction of 89% using twins rather than regular people.

generalD <- 0.083/1.560322176
twinD <- 0.083/0.25
generalP <- power.t.test(d=generalD, power=0.8); generalP
#      Two-sample t test power calculation
#
#               n = 5548.623219
#           delta = 0.05319414239
#              sd = 1
#       sig.level = 0.05
#           power = 0.8
#     alternative = two.sided
#
# NOTE: n is number in *each* group
twinP <- power.t.test(d=twinD, power=0.8); twinP
#      Two-sample t test power calculation
#
#               n = 143.3836014
#           delta = 0.332
#              sd = 1
#       sig.level = 0.05
#           power = 0.8
#     alternative = two.sided
#
# NOTE: n is number in *each* group
twinP$n / generalP$n
# [1] 0.02584129355

twinAll <- 0.083/(0.338*1.560322176)
generalAll <- 0.083/1.560322176
generalAll <- power.t.test(d=generalD, power=0.8)
twinAll <- power.t.test(d=twinAll, power=0.8)
twinAll$n / generalAll$n
# [1] 0.1143975625

RNN metadata for mimicking individual author style

Char-RNNs are unsupervised generative models which learn to mimic text sequences. I suggest extending char-RNNs with inline metadata such as genre or author prefixed to each line of input, allowing for better & more efficient metadata, and more controllable sampling of generated output by feeding in desired metadata. An experiment using torch-rnn on a set of ~30 Project Gutenberg e-books (1 per author) to train a large char-RNN shows that a char-RNN can learn to remember metadata such as authors, learn associated prose styles, and often generate text visibly similar to that of a specified author.

Due to length, this has been split out to another page.

MCTS

An implementation in R of a simple Monte Carlo tree search algorithm (using Thompson sampling rather than a UCT) implemented with data.tree. This MCTS assumes binary win/loss (1/0) terminal rewards with no intermediate rewards/costs so it cannot be used to solve general MDPs, and does not expand leaf nodes in the move tree passed to it. (I also suspect parts of it are implemented wrong though it reaches the right answer in a simple Blockworld problem and seems OK in a Tic-Tac-Toe problem.)

library(data.tree)
## MCTS helper functions:
playOutMoves <- function(move, state, actions) {
  for (i in 1:length(actions)) {
     state <- move(state, actions[i])$State
    }
    return(state)
    }
playOutRandom <- function(move, state, actions, timeout=1000, verbose=FALSE) {
 action <- sample(actions, 1)
 turn <- move(state, action)
 if(verbose) { print(turn); };
 if (turn$End || timeout==0) { return(turn$Reward) } else {
                               playOutRandom(move, turn$State, actions, timeout=timeout-1, verbose) }
 }

createTree <- function(plys, move, moves, initialState, tree=NULL) {
 if (is.null(tree)) { tree <- Node$new("MCTS", win=0, loss=0) }
 if (plys != 0) {
  for(i in 1:length(moves)) {
    x <- tree$AddChild(moves[i], win=0, loss=0)
    createTree(plys-1, move, moves, initialState, tree=x)
  }
 }
 # cache the state at each leaf node so we don't have to recompute each move as we later walk the tree to do a rollout
 tree$Do(function(node) { p <- node$path; node$state <- playOutMoves(move, initialState, p[2:length(p)]); }, filterFun = isLeaf)
 return(tree)
}

mcts <- function (tree, randomSimulation, rollouts=1000) {
 replicate(rollouts, {
   # Update posterior sample for each node based on current statistics and use Thompson sampling.
   # With a beta uniform prior (Beta(1,1)), update on binomial (win/loss) is conjugate with simple closed form posterior: Beta(1+win, 1+n-win).
   # So we sample directly from that posterior distribution for Thompson sampling
   tree$Do(function(node) { node$Thompson <- rbeta(1, 1+node$win, 1+(node$win+node$loss)-node$win) })
   # find & run 1 sample:
   node <- treeWalk(tree)
   rollout <- randomSimulation(node$state)
   if(rollout==1) { node$win <- node$win+1; } else { node$loss <- node$loss+1; }

   # propagate the new leaf results back up tree towards root:
   tree$Do(function(x) { x$win  <- Aggregate(x, "win",  sum); x$loss <- Aggregate(x, "loss", sum) }, traversal = "post-order")
  })
}

## walk the game tree by picking the branch with highest Thompson sample down to the leaves
## and return the leaf for a rollout
treeWalk <- function(node) {
    if(length(node$children)==0) { return(node); } else {
        children <- node$children
         best <- which.max(sapply(children, function(n) { n$Thompson; } ))
        treeWalk(children[[best]]) } }

mctsDisplayTree <- function(tree) {
    tree$Do(function(node) { node$P <- node$win / (node$win + node$loss) } )
    tree$Sort("P", decreasing=TRUE)
    print(tree, "win", "loss", "P", "Thompson")
    }

## Blockworld simulation
## 0=empty space, 1=agent, 2=block, 3=goal point
blockActions <- c("up", "down", "left", "right")
blockInitialState <- matrix(ncol=5, nrow=5, byrow=TRUE,
                       data=c(0,0,0,0,1,
                              0,2,0,0,2,
                              0,0,0,2,0,
                              0,2,0,0,0,
                              0,0,0,0,3))
blockMove <- function(state, direction) {
   if(state[5,5] == 2) { return(list(State=state, Reward=1, End=TRUE)) }
   position <- which(state == 1, arr.ind=TRUE)
   row <- position[1]; col <- position[2]
   rowNew <- 0; colNew <- 0
   switch(direction,
     # if we are at an edge, no change
     up    = if(row == 1) { rowNew<-row; colNew<-col; } else { rowNew <- row-1; colNew <- col; },
     down  = if(row == 5) { rowNew<-row; colNew<-col; } else { rowNew <- row+1; colNew <- col; },
     left  = if(col == 1) { rowNew<-row; colNew<-col; } else { rowNew <- row;   colNew <- col-1; },
     right = if(col == 5) { rowNew<-row; colNew<-col; } else { rowNew <- row;   colNew <- col+1; }
   )
   # if there is not a block at the new position, make the move
   if (state[rowNew,colNew] != 2) {
      state[row,col] <- 0
      state[rowNew,colNew] <- 1
      return(list(State=state, Reward=0, End=FALSE))
       } else {
               state[rowNew,colNew] <- 1
               state[row,col] <- 0
               switch(direction,
                # if the block is at the edge it can't move
                up    = if(rowNew == 1) { } else { state[rowNew-1,colNew] <- 2 },
                down  = if(rowNew == 5) { } else { state[rowNew+1,colNew] <- 2 },
                left  = if(colNew == 1) { } else { state[rowNew,colNew-1] <- 2 },
                right = if(colNew == 5) { } else { state[rowNew,colNew+1] <- 2 } )
                # a block on the magic 5,5 point means a reward and reset of the playing field
                if(state[5,5] == 2) { return(list(State=state, Reward=1, End=TRUE)) } else { return(list(State=state, Reward=0, End=FALSE)) }
                }
}

## Blockworld examples:
# blockMove(blockInitialState, "left")
# blockMove(blockInitialState, "down")
# blockMove(blockInitialState, "right")$State
# blockMove(blockMove(blockInitialState, "right")$State, "down")
# blockMove(blockMove(blockMove(blockInitialState, "down")$State, "down")$State, "down")
# playOutMoves(blockMove, blockInitialState, c("down", "down", "down"))
# playOutRandom(blockMove, blockInitialState, blockActions)

tree <- createTree(2, blockMove, blockActions, blockInitialState)
mcts(tree, function(state) { playOutRandom(blockMove, state, blockActions) })
mctsDisplayTree(tree)

tree2 <- createTree(3, blockMove, blockActions, blockInitialState)
mcts(tree2, function(state) { playOutRandom(blockMove, state, blockActions) })
mctsDisplayTree(tree2)

## Tic-Tac-Toe
tttActions <- 1:9
tttInitialState <- matrix(ncol=3, nrow=3, byrow=TRUE, data=0)
tttMove <- function(state, move) {
   move <- as.integer(move)
   # whose move is this? Player 1 moves first, so if the number of pieces are equal, it must be 1's turn:
   player <- 0;  if(sum(state == 1) == sum(state == 2)) { player <- 1 } else { player <- 2}

   # check move is valid:
   if(state[move] == 0) { state[move] <- player }

   ## enumerate all possible end-states (rows, columns, diagonals): victory, or the board is full and it's a tie
   victory <- any(c(
       all(state[,1] == player),
       all(state[1,] == player),
       all(state[,2] == player),
       all(state[2,] == player),
       all(state[,3] == player),
       all(state[3,] == player),
       all(as.logical(c(state[1,1], state[2,2], state[3,3]) == player)),
       all(as.logical(c(state[1,3], state[2,3], state[3,1]) == player))
   ))
   tie <- all(state != 0)

   # if someone has won and the winner is player 1, then a reward of 1
   if(victory) { return(list(State=state, Reward=as.integer(player==1), End=TRUE)) } else {
    if(tie) { return(list(State=state, Reward=0, End=TRUE)) } else {
      return(list(State=state, Reward=0, End=FALSE)) }
     }
}

## Tic-Tac-Toe examples:
# tttMove(tttMove(tttMove(tttInitialState, 5)$State, 9)$State, 2)
# playOutMoves(tttMove, tttInitialState, c(5, 9, 2))
# playOutRandom(tttMove, tttInitialState, tttActions, verbose=TRUE)

treeTTT <- createTree(2, tttMove, tttActions, tttInitialState)
mcts(treeTTT, function(state) { playOutRandom(tttMove, state, tttActions) })
mctsDisplayTree(treeTTT)
## hypothetical: if opponent plays center (5), what should be the reply?
treeTTT2 <- createTree(2, tttMove, tttActions, tttMove(tttInitialState, 5)$State)
mcts(treeTTT2, function(state) { playOutRandom(tttMove, state, tttActions) })
mctsDisplayTree(treeTTT2)

Candy Japan A/B test

Due to length, has been split out to Candy Japan’s new box A/B test.

DeFries-Fulker power analysis

DeFries-Fulker (DF) extremes analysis

• DeFries & Fulker 1985
• DeFries et al 1987, Evidence for a genetic aetiology in reading disability of twins /docs/genetics/1987-defries.pdf
• DeFries & Fulker 1988, Multiple regression analysis of twin data: Etiology of deviant scores versus individual differences
• A Model-Fitting Implementation of the DeFries-Fulker Model for Selected Twin Data Purcell & Sham 2003 /docs/genetics/2003-purcell.pdf
• LaBuda et al 1986
• DeFries et al 1991, Colorado Reading Project: An update
• Gillespie & Neale 2006 A Finite Mixture Model for Genotype and Environment Interactions: Detecting Latent Population Heterogeneity http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.534.6298&rep=rep1&type=pdf
• Purcell et al 2001 Comorbidity between verbal and non-verbal cognitive delays in 2-year-olds: A bivariate twin analysis https://www.researchgate.net/profile/Emily_Simonoff/publication/227715188_Comorbidity_between_verbal_and_nonverbal_cognitive_delays_in_2yearolds_a_bivariate_twin_analysis/links/0fcfd50b74917d5933000000.pdf
• http://www.sciencedirect.com/science/article/pii/S0160289614001676 Thinking positively: The genetics of high intelligence, Shakeshaft et al 2015

mixture model

generateSiblingPair <- function(ID=TRUE) {
   ## Population mean 100, SD 15; let's make family means distributed normally too;
   ## heritability 0.8, shared environment 0.1, siblings share half of genes on average + shared environment
   ## so a pair of siblings has 1 - (0.8*0.5+0.1) = 0.5 of the variance of the general population.
   parental <- mean(rnorm(1,mean=100,sd=15*0.8), rnorm(1,mean=100,sd=15*0.8))
   siblings <- rnorm(2, mean=parental, sd=15*(1 - (0.8*0.5+0.1)))
   ## Siblings will tend to vary this much, unless they are, lamentably, one of the, say,
   ## 5% struck by mutational lightning and reduced to an IQ of, let's say, 80
   if(ID) { siblings <- ifelse(rbinom(2,1,prob=0.05), siblings,rnorm(2, mean=80, sd=15)) }
   return(c(max(siblings), min(siblings)))
}
generateSiblingPairs <- function(n,ID=TRUE) { as.data.frame(t(replicate(n, generateSiblingPair(ID=ID)))) }
## dataset with lightning:
df <- round(rescale(generateSiblingPairs(1000000, ID=TRUE), mean=5, sd=2))
## floor/ceiling at 0/9 for everyone:
df[df$V1>9,]$V1 <- 9
df[df$V1<1,]$V1 <- 1
df[df$V2>9,]$V2 <- 9
df[df$V2<1,]$V2 <- 1

## dataset without:
df2 <- round(rescale(generateSiblingPairs(1000000, ID=FALSE), mean=5, sd=2))
df2[df2$V1>9,]$V1 <- 9
df2[df2$V1<1,]$V1 <- 1
df2[df2$V2>9,]$V2 <- 9
df2[df2$V2<1,]$V2 <- 1

par(mfrow=c(2,1))
hist(df$V1 - df$V2)
hist(df2$V1 - df2$V2)

## mixture modeling:
library(flexmix)
## check k=1 vs k=2 on df1, where k=2 is ground truth:
g1.1 <- flexmix(I(V1-V2) ~ 1, k=1, data=df)
g1.2 <- flexmix(I(V1-V2) ~ 1, k=2, data=df)
summary(g1.1); summary(g1.2)

## check k=1 vs k=2 on df2, where k=1 is ground truth:
g2.1 <- flexmix(I(V1-V2) ~ 1, k=1, data=df2)
g2.2 <- flexmix(I(V1-V2) ~ 1, k=2, data=df2)
summary(g2.1); summary(g2.2)

Inferring mean IQs from SMPY/TIP elite samples

Samples taken from the extremes of mixtures of distributions can have very different properties than random samples, such as wildly disproportionate representation of one distribution. This can be used to infer differing means. I demonstrate working backwards from the racial composition of TIP/SMPY samples of extremely (1-in-10,000) gifted youth to estimate the overall racial means, which is consistent with the known racial means and hence an unbiased selection process, using ABC to infer Bayesian credible intervals on the estimated means.

The general properties of statistical distributions can be very different from the properties of specific subsets in counterintuitive ways. One common example is that a small difference in means for many distributions can lead to large differences in extreme subsets.

For example, male and female average heights differ by a relatively small amount, inches at most. So in a random sample, plenty of women will be taller than men, and vice versa. However, if instead ask the sex of the tallest person in the sample, it will often be male, and the larger the sample, the more certain we can be that it will be male, and that the top X% by height will be male. Likewise, if we wanted to start a basketball league and recruited the tallest 100 people in the country, this small mean difference will show up as our entire basketball league turning out to be male. (And since height is highly heritable, we may find out that many of them are related!) What seemed like a small difference become a large one; we could have worked it out in advance if we had thought about it.

Reasoning from the general to the particular turned out to be tricky in this case because we were dealing with extreme values rather than random samples - 1 basketball player chosen by height from thousands of people. Many things of great interest turn out to be like that: we are interested in the extremes much more than the expectation. Running a 2-hour marathon is an extreme on athleticism; winning the Nobel is an extreme on scientific accomplishment; being enlisted in the NBA is an extreme on height; being admitted to MIT/Stanford/Harvard is an extreme on intelligence; murdering someone is an extreme on violence; winning an Academy Award is an extreme on acting success. When we ask questions like, why does the world record in this sport keep being shattered or why are so many NBA players related or how good can we expect the best chess player to be in 10 years or does this racial composition prove bias or how much more important are the best authors in literature than obscurer figures or why do so few women win the Field Medal, we’re asking extreme value questions whose answers may be counterintuitive - and the answer may be as simple as the shape of distributions, and a slightly lower mean here or a slightly higher standard deviation there. (Working backwards from a sample selected for passing a threshold to a mean can be called the method of limits or the method of thresholds.)

The study When Lightning Strikes Twice: Profoundly Gifted, Profoundly Accomplished, Makel et al 2016 describes the accomplishments of the Duke TIP sample, 259 children selected for their intelligence by taking the highest-scorers out of 425,000 adolescents taking the SAT (usually <13yo) starting in 1981, representing the top 0.01% of the test-takers. The TIP sample parallels the better-known SMPY sample, which also selected extremely intelligent adolescents, who were included in a longitudinal sample. It’s frequently suggested, based on anecdotal evidence or some biased convenience samples, that more intelligence may not be better; extremely intelligent people may be unhealthy, neurotic, insane, isolated, lonely, discriminated against by society and their peers, and doomed to failure; or if things are not quite that dire, as all studies show things improving up to 130, then at around that point greater intelligence may stop making any difference, and there be little difference between someone with an IQ of 130 and 160. This is difficult to study cross-sectionally, because once you start talking about as extreme as 0.01%, it is difficult to recruit any subjects at all, and your sample will be biased in unknown ways; if you only look at successful people, you are missing the hypothetical homeless bum living out of a trash can who is a troubled and misunderstood genius. To solve these problems, you want to filter through hundreds of thousands of people so you can select the very brightest possible, and you want to find them as early as possible in life, before they have had any chance to fail or succeed, and track them longitudinally as they grow up. This is what the SMPY & TIP studies do, and the results are that the subjects are spectacularly successful in life; great intelligence is not harmful and the returns to greater intelligence are not zero even as high as 1 in 10,000.

Makel et al 2016 also reports the ethnic breakdown of the TIP and SMPY samples: 72% white, 22% Asian, 6% not reported or other. This distribution might seem remarkable given that subjects taking the SAT in 1981 were born ~1970, when the USA was ~77% white, ~11% black, and ~0.7% Asian, so white are slightly under-represented, blacks are very under-represented (even if we assume all 6% are black, then that’s still half), and Asians are 31x (!) overrepresented.

## TIP/SMPY sample size & ethnic percentages: https://pbs.twimg.com/media/Cj9DXwxWEAEaQYk.jpg
tip <- 259; smpy <- 320 ## total: 579
white <- ((0.65*tip) + (0.78*smpy)) / (tip+smpy)
asian <- ((0.24*tip) + (0.20*smpy)) / (tip+smpy)
white; asian
# [1] 0.7218480138
# [1] 0.2178929188

# http://drjamesthompson.blogspot.com/2016/06/some-characteristics-of-eminent-persons.html
# > The data on ethnicity are rather sparse, but we can do a little bit of work on them by looking at US Census
# > figures for the 1970s when most of these children were born: White 178,119,221...Asia 1,526,401...So, in the
# > absence of more detailed particulars about the Other category, Asians win the race by a country mile. If we
# > simplify things by considering only Whites, Blacks and Asians the US in 1970 then the country at that time was
# > 88% White, 11% Black, and less than 1% Asian. The actual results of eminent students are 77% White, 0% Black,
# > 22% Asian. No need for a Chi square.
#
# Asian is 0.7%: R> 1526401  / (178119221 / 0.80)
whiteRR <- white / 0.77; asianRR <- asian / 0.007
whiteRR; asianRR
# [1] 0.937464953
# [1] 31.12755983

Of course, races in the USA have long differed by mean intelligence, with the rule of thumb being Asians ~105 IQ, whites ~100, and blacks ~90. So the order is expected - but still, 31x! Are the results being driven by some sort of pro-Asian bias or otherwise bizarre?

But this is an extreme sample. 1-in-10,000 is far out on the tails: 3.71SDs.

-qnorm(1/10000)
# [1] 3.719016485

Maybe this is normal. Can we work backwards from the overrepresentations to what differences would have generated them?

Yes, we can, even with this small sample which is so extreme and unrepresentative of the general population. This is because it is an order statistics problem: we know the order represented by the sample and so can work back to parameters of the distribution the order statistics are being generated by. Since IQ is a normal distribution, we know the overrepresentation RR, and the exact cutoff/limit used in the sample, we can convert the limit to a standard deviations, and then find the normal distribution $\mathcal{N}(100+x,15)$ which is RR (31) times the normal distribution $\mathcal{N}(100,15)$ at a standard deviations.

We can compare using two pnorms and shifting the second by a SDs. So for example, shifting by 15 IQ points or 1 SD would lead to 84x overrepresentation

pnorm(qnorm(1/10000)) / pnorm(qnorm(1/10000) - (15/15))
# [1] 84.39259519

We would like to solve for the shift which leads to an exact overrepresentation like 31.127; an optimization routine like R’s optim function can do that, but it requires an error to minimize, so minimizing pnorm()/pnorm(x) doesn’t work since it just leads to negative infinity, nor will RR == pnorm()/pnorm(x) work, because it evaluates to 0 for all values of x except the exact right one . Instead, we minimize the squared error between the ratio predicted by a particular x and our observed RR. This works:

## An optimization routine which automatically finds for us the IQ increase which most closely matches the RR:
solver <- function(RR, cutoff=10000) {
    optim(1,
        function(IQ_gain) { (RR - (pnorm(qnorm(1/cutoff)) / pnorm(qnorm(1/cutoff)-(IQ_gain/15))))^2 },
        )$par }

100 + solver(whiteRR)
# [1] 99.75488281
100 + solver(asianRR)
# [1] 111.8929688

So our inferred white & Asian populations means are: 99.8 and 111.9. These are relatively close to the expected values.

This approach can be used to infer other things as well. For example, the TIP/SMPY papers have not, as far as I’ve seen, mentioned what fraction of the white subjects were ethnic Jewish; since they are so over-represented in areas like Nobel prizes, we would expect many of the TIP/SMPY white students to have been Jewish. Using an estimate of the Jewish population in 1970 and estimates of their mean IQ, we can work forward to what fraction of TIP/SMPY subjects might be Jewish. The 1970-1971 National Jewish Population Study estimated 5,800,000 persons (of whom 5,370,000 were Jews) living in Jewish households out of a total US population of 205 million, or 2.8% of the total population or ~3.6% of the white population. So of the ~418 white subjects, ~15 would be expected to be Jewish under the null hypothesis of no difference. The majority of American Jews are of Ashkenazi descent⁵, for whom intelligence estimates are debated but tend to range 105-115 (with occasional samples suggesting even higher values, like Levinson 1957). In the Barbe 1964 Ohio sample (IQ ~143), 8% were Jewish⁶; in Terman’s (ratio IQ >140) 1920s sample in SF/LA, 10% were Jewish; Hollingworth’s 1930s sample (>180) turned up 51/55 or 90% Jewish⁷; Byrns 1936’s 1931 Wisconsin state sample found 18% of the Jewish sample to be in the top decile vs 10% American; in the Hunter College Elementary School sample 1948-1960 (>140, mean 157) in New York City, 62% were Jewish (Subotnik et al 1989, Subotnik et al 1993⁸). Given estimates of the Jewish population of children in those specific times and places, one could work backwards to estimate a Jewish mean.

We can calculate the fraction of the white sample being Jewish for each possible mean IQ:

proportion <- function (gain, cutoff=10000) {
   (pnorm(qnorm(1/cutoff)) / pnorm(qnorm(1/cutoff)-(gain/15))) }
possibleIQs <- seq(5, 15, by=0.5)
data.frame(Advantage=possibleIQs, Fraction.of.white=(sapply(possibleIQs, proportion) * 15) / 418)
   Advantage Fraction.of.white
1        5.0      0.1415427303
2        5.5      0.1633099334
3        6.0      0.1886246225
4        6.5      0.2180947374
5        7.0      0.2524371552
6        7.5      0.2924980125
7        8.0      0.3392769622
8        8.5      0.3939561508
9        9.0      0.4579348680
10       9.5      0.5328710150
11      10.0      0.6207307813
12      10.5      0.7238482059
13      11.0      0.8449966589
14      11.5      0.9874747049
15      12.0      1.1552093388
16      12.5      1.3528802227
17      13.0      1.5860693342
18      13.5      1.8614413902
19      14.0      2.1869615788
20      14.5      2.5721585555
21      15.0      3.0284424112

Judging from earlier samples with very high cutoffs, I’d guess TIP/SMPY has at least a majority Jewish, giving a mean IQ of ~110; this is pleasantly similar to estimates based on regular samples & estimation. This result is also similar to La Griffe du Lion’s 2003 threshold analysis estimating a mean IQ of 112 based on Ashkenazi overrepresentation among USSR championship chess players, 111 based on Western Fields Medal awards, and 110 based on the USA/Canada Putnam competition. But if the mean IQ was as high as 112, then almost every single white subject would be Jewish in every sampling, which seems implausible and like something so striking that anyone writing or involved with TIP/SMPY would have to have mentioned at some point - right?

For the same reason, the original estimate of 112 for the Asians strikes me as on the high side. This could be due to problems in the data like underestimating the Asian population at the time - perhaps the Southeast/Midwest states that TIP samples from were more than 0.7% Asian - or it could be due to sampling error (only n=579, after all).

Working backwards doesn’t immediately provide any measurement of precision or confidence intervals. Presumably someone has worked out analytic formulas which come with standard errors and confidence intervals, but I don’t know it. Instead, since the selection process which generated our data is straightforward (population mean -> millions of samples -> take top 1-in-10000s -> calculate overrepresentation), I can again use Approximate Bayesian computation (ABC) to turn a simulation of the data generating process into a method of Bayesian inference on the unknown parameters (population means) and get credible intervals.

What sort of confidence do we have in these estimates given that these RRs are based only on? We can simulate TIP/SMPY-like selection by taking the hypothetical means of the two groups, generating ~3 million simulates (579 * 10000) each, selecting the top 1/10000th, taking the RRs and then solving for the mean IQ. If we provide a prior on the means and we hold onto only the means which successfully generate TIP/SMPY-like fractions of 72% & 21%, this becomes ABC with the saved means forming the posterior distribution of means. (It would likely be faster to use MCMC like JAGS, but while JAGS provides truncated normal distributions which one could sample from quickly, and the necessary pnorm/qnorm functions, but it’s not clear to me how one could go about estimating the overperformance ratio and the binomial.⁹) For my priors, I believe that the rule of thumbs of 100/105 are accurate and highly unlikely to be more than a few points off, so I use a very weak prior of populations means being $\mathcal{N}(100/105, 4)$.

In exact ABC, we would keep only data which exactly matched 72%/22%, but that would require rejecting an extremely large number of samples. Here we’ll loosen it to ±2% tolerance:

simulateTIPSMPY <- function() {
    ## informative priors: IQs are somewhere close to where we would estimate based on other datasets
    whiteMean <- round(rnorm(1, mean=100, sd=5), digits=2)
    asianMean <- round(rnorm(1, mean=105, sd=5), digits=2)

    iqCutoff <- 100 + -qnorm(1/10000) * 15

    whites <- rnorm(0.770 * 579 * 10000, mean=whiteMean, sd=15)
    whiteSample <- max(1, sum(ifelse(whites>iqCutoff, 1, 0)))

    asians <- rnorm(0.007 * 579 * 10000, mean=asianMean, sd=15)
    asianSample <- max(1, sum(ifelse(asians>iqCutoff, 1, 0)))

    ## white+Asian = 92% of original total sample, so inflate by that much to preserve proportions: 1.08
    totalSample <- (whiteSample+asianSample) * (1 + (1-(white+asian)))

    whiteFraction <- round(whiteSample / totalSample, digits=2)
    asianFraction <- round(asianSample / totalSample, digits=2)
    # print(paste("samples: ", c(whiteSample, asianSample), "fractions: ", c(whiteFraction, asianFraction)))

    tolerance <- 0.02
    if ((abs(whiteFraction - 0.7218480138) < tolerance) && (abs(asianFraction - 0.2178929188) < tolerance)) {
      return(data.frame(White=whiteMean, Asian=asianMean))
    }
    }
library(parallel); library(plyr)
simulateSamples <- function(n.sample=10000, iters=getOption("mc.cores")) {
    ## because of rejection sampling, no run is guaranteed to produce a sample so we loop:
    results <- data.frame()
    while (nrow(results) < n.sample) {
        simResults <- ldply(mclapply(1:iters, function(i) { simulateTIPSMPY()  } ))
        results <- rbind(results, simResults)
        # print(paste("Samples so far: ", nrow(results)))
    }
    return(results) }
posteriorSamples <- simulateSamples()

mean(posteriorSamples$White < posteriorSamples$Asian)
# [1] 1
## we have relatively few samples, so get a better posterior estimate by shuffling the posterior samples & comparing many times:
mean(replicate(1000, mean(c(sample(posteriorSamples$White) < sample(posteriorSamples$Asian)))))
# [1] 0.9968822
quantile(probs=c(0.025, 0.975), posteriorSamples$White, na.rm=TRUE)
#     2.5%     97.5%
# 89.49975 101.38050
quantile(probs=c(0.025, 0.975), posteriorSamples$Asian, na.rm=TRUE)
#      2.5%     97.5%
# 101.37000 116.74075
par(mfrow=c(2,1))
hist(posteriorSamples$White, main="Posterior white mean IQ estimated from TIP/SMPY cutoff & ratio", xlab="IQ")
hist(posteriorSamples$Asian, main="Posterior Asian mean", xlab="IQ")

So sampling error does turn out to be substantial: our 95% credible intervals are white 90-101, Asian 101-116. Still, the overlap is minimal, with P=99.7% that the Asian mean is higher than the white.

We are able to conclude that the rank ordering is highly likely to be correct, and the results are consistent with the conventional wisdom, so there is no prima facie case for bias in the results: the ethnic composition is in line with what one would calculate from the design of TIP/SMPY and population means.

Genius Revisited: On the Value of High IQ Elementary Schools

Genius Revisited documents the longitudinal results of a high-IQ/gifted-and-talented elementary school; one of the most striking results is the general high education & income levels, but absence of great accomplishment on a national or global scale (eg a Nobel). The authors suggest that this may reflect harmful educational practices at the elementary school or the low predictive value of IQ. I suggest that their standards fall prey to a base-rate fallacy, and that the lack of accomplishment is inherent and unavoidable as it is driven by the regression to the mean caused by the relatively low correlation of early childhood & adult IQs; in contrast, its associated high-IQ/gifted-and-talented high school, which has access to more predictive IQ scores, has much higher achievement in proportion to its lesser regression to the mean (despite dilution by elementary students being automatically enrolled). This greater-regression problem can be lessened by the use of additional variables in admissions, such as parental IQs or high-quality genetic polygenic scores; unfortunately, these are either politically unacceptable or dependent on future scientific advances. This suggests that such elementary schools may not be a good use of resources or high school slots.

Genius Revisited: High IQ Children Grown Up, by Subotnik, Kassan, Summers & Wasser 1993 is a short (142 pages) book reporting the results of a longitudinal/followup study in 1988 of 210 of the 600 1948-1960 alumni of the Hunter College Elementary School (HCES) who had reached their 40s or so. (See also the brief more statistically-oriented report of the survey results in High IQ children at midlife: An investigation into the generalizability of Terman’s genetic studies of genius, Subotnik et al 1989; for a overview of gifted education with some mention of the HCES results, see Subotnik et al 2011.)

Hunter Elementary is a small elementary school in New York City enrolling ~50 students each year starting in preschool/kindergarten since the 1940s, who then typically enroll in the associated Hunter College High School, itself associated with Hunter College. Hunter Elementary is famous for extremely stringent admission based on IQ tests, yielding a student body with a mean IQ in the 150s (or around 1-in-10,000); the gifted students are taught a wide-ranging and enriched curriculum designed for gifted children. (If you’ve ever read about helicopter or tiger moms in Manhattan training their kids on IQ tests to get them into an elite kindergarten, Hunter Elementary is one of the kindergartens they have in mind.) As such, Hunter Elementary students might be expected to be extremely interesting and highlight the effects of great intelligence on one’s life: as they all are selected young and relatively systematically from NYC children, such a longitudinal study is going to be much more reliable than other attempts at studying high intelligence using cross-sectional or ad hoc recruitment from child psychologists.

HCES results

To summarize the results: contrary to stereotypes that bookish, nerdy, socially inept, absentminded, emotionally dense, arrogant and unfriendly, and that they are loners, high IQ children are physically and psychologically healthy, if not healthier; they are often socially capable; adult accomplishment and eminence increase with greater intelligence, with no particular cutoff visible at places like IQ 130; happiness is not particularly greater; male and female differences in achievement exist but are at least partially driven by other sex-linked differences in choice of field and work-life balance; particular ethnicities are under or overrepresented as one would calculate using the normal distribution from the study-specific cutoffs and ethnic means; and even with extremely high ability in all areas, people tend to eventually specialize in their greatest strength which is their comparative advantage; and overall educational credentials are much more common in the later groups than the earlier ones.

So what does Genius Revisited report? In general, it is surprisingly light on detailed quantification or analysis. Income and education are reported only cursorily; adult achievements are not gone into any sort of detail or categorization only than vague generalizations about there being lots of doctors, professors, and executives etc. They do not report adult IQs, or attempt any statistical analysis to compare IQs at admittance, graduation, or when contacted as adults, whether some subtests predict adult accomplishment better than others, whether there were differential regressions to the mean or whether there was any regression to the mean observed by graduation¹⁰, or comparison of any dropouts/transfers with the students who graduated Hunter Elementary and continued to Hunter High; the questionnaires are based on the old Terman questionnaires and don’t seem well focused to investigate modern concerns in gifted education or individual differences psychology. From this perspective, the book is quite a disappointment, as there are not many high IQ longitudinal datasets around - yet they waste the opportunity. Some further details and more fine-grained categorization of a few of the hundred variables collected are reported in Subotnik et al 1989 but the treatment is much less than it could have been.

What it does do is attempt as a sort of narrative ethnography by piecing together many quotes from the students about their Hunter Elementary experience and later life. This is interesting to me on a personal level because my parents had considered sending me to the Long Island School for the Gifted but ultimately decided against it; so in a way, reading their memories is a glimpse of a path not taken. The picture that emerges confirms in many respects the portrait of children in Terman/SMPY/TIP: the children are healthy, well-socialized, enjoy outdoor sports (particularly hiking); girls tend to not prefer the stereotypical childhood activities like dolls (which is interesting given SMPY results related to testosterone); reading is, of course, everyone’s favorite hobby, especially to help with researching their other hobbies; the burden of being labeled a genius or prodigy bothered some but apparently not most of them; students remembered Hunter Elementary extremely fondly and were glad to have gone there rather than regular school, although opinions on how Hunter Elementary could have been better are amusingly equally divided in Subotnik et al’s recounting (a good compromise leaves everyone unhappy); teachers likewise regarded teaching there as a plum assignment, as the students were highly cooperative, enthusiastic, almost always well-behaved, soaked up material like sponges, and would happily go off on tangents like debating the strategic value of Australia during WWII (in other words, what any would-be teacher dreams of teaching, instead of getting a class of bored, sleepy kids who act out and forget things the second you explain them); many students deliberately did not pursue the most demanding adult careers to have a work-life balance, particularly the women, with the usual differences in subject-area preferences; women were, as predicted given the later era than Terman, far more likely to pursue higher education and some sort of employment; students are highly successful, but none seemed particularly extraordinarily successful.

There is also a short comparison with Hunter Elementary in the 1990s; apparently much the same as in the 1960s, with the main interesting change that Hunter Elementary has added a racial quota for black students, but Subotnik et al claim that the mean IQ scores have not fallen substantially. It would be interesting to know exactly how much it has fallen, how many of the black students have immigrant parents, and how many students are now of East Asian descent.¹¹

Overall, the writing is clear and there is, if anything, insufficient technical jargon. Some dry humor appears in spots (eg in Subotnik et al 1989, a wry comment on rent control and the difficulties of longitudinal studies: the only addresses on file were those of the parents while the child attended the school. Fortunately, given the state of the New York City housing market, checking those addresses against the 1988 Manhattan phone book proved to be fairly productive).

Disappointingly average

Subotnik et al generally seem to hold what has been called the resource model of gifted education: high IQ children have much better odds of growing up into the great movers & shakers and thinkers of the world who have disproportionate influence on what happens (definitely); that special measures such as enriched education, schools with peers in intelligence, and accelerated courses will increase the yield of great (maybe); and that the increase justifies the upfront expenses (uncertain).

By success, they have very high standards; Gallagher’s foreword speaks for the rest of the book when it says:

The authors were disappointed to discover that although this sample succeeded admirably in traditional terms, with its share of physicians, lawyers, and professors, there were no creative rebels to shake society out of its complacency or revolutionize a field.

Further:

Norbert Wiener, in his book The Autobiography of an Ex-Genius [Ex-Prodigy: My Childhood and Youth], detailed his unhappy family life with a domineering father and enough personal problems to be in and out of mental institutions. Yet, it was this Norbert Wiener who gave the world cybernetics that revolutionized our society. What if he had had a happy family life with a warm and agreeable father? One is left to wonder whether Wiener would have had the drive and motivation to make this unique contribution. The same question can be posed for these Hunter College Elementary School graduates. Are many of them too satisfied, too willing to accept the superior rewards that their ability and opportunity have provided for them? What more could they have accomplished if they had a psychological worm eating inside them - whether that worm was low self-concept or a need to prove something to someone or to the world - that would have driven these people to greater efforts. What if their aptitudes had been challenged in a more hard-driving manner, like Wiener’s experience, into the development of a specific talent? This book raises many significant, sometimes disturbing issues… The authors raise some disturbing issues regarding the purposes of schools for the gifted. Indeed, just what is the contemporary rationale for funding schools or programs for the highly gifted student? If one is looking to such an institution as a source of leading students towards societal leadership (or, as the authors suggest, a path to eminence), then the Hunter College Elementary School of the past failed to realize such an aspiration. Indeed, this goal may well be beyond the reach of any elementary school…the [Hunter College] High School seeks to enhance students’ commitment to intellectual rigor and growth, develop opportunities for specialization, and commitment to caring and compassion. Will such a rationale foster more students down the path towards genius? The research literature and the current study would indicate that such a condition is a necessary but not sufficient condition to move students into making ground-breaking discoveries or toward professional eminence. Does it follow then that such schools should not exist? Or at least, not at public expense? I would vigorously argue against both reactions.

This fits with the general description of the Hunter Elementary cohort on pg3-4:

The mean IQ of the Hunter sample was 157, or approximately 3.5 standard deviations above the mean, with a range of 122 to 196 on the L-M form.

…Each class at Hunter College Elementary School from the years 1948 to 1960 contained about 50 students, yielding a total possible population of 600 graduates…35% of the total population of 1948-1960 HCES students (n=210) completed and returned study questionnaires

…Religious Affiliation: The Hunter group is approximately 62% Jewish, although they describe themselves as Jews more in terms of ethnic identity than religious practice. The group, as a whole, is not religious.

Educational Attainments: Over 80% of the study participants held at least a Master’s degree. Furthermore, 40% of the women and 68% of the men held either a Ph.D, LL.B., J.D., or M.D. degree.

Occupation and Income: Only two of the HCES women identified themselves primarily as homemakers. 53% were professionals, working as a teacher at the college or pre-college level, writer (journalist, author, editor), or psychologist. The same proportion of HCES men were professionals, serving as lawyers, medical doctors, or college teachers. The median income for men in 1988 was $75,000 (range = $500,000) and for women $40,000 (range = $169,000). Income levels were significantly different for men and women, even when matched by profession. For example, the median income for male college teachers or psychologists was $50,000 and for females, $30,000

By regular standards, this is a remarkably high degree of accomplishment. Even now, only a small fraction of the population can be said to hold a Ph.D, LL.B., J.D., or M.D., but in the Hunter Elementary cohort, you could hardly throw a rock without hitting a professor (16% of men), who would then be able to turn to the person standing next to them to have their wound treated (18% doctors), and turn to the person on the other side in order to sue you for assault (20% lawyers). For this cohort, the education baseline would be more like <7%, not >80%. Subotnik et al 1989 breaks it down a little more precisely in Table 2 Highest Degree Attained: for men, 4% not available, 20% Bachelors, 43% Masters, 40% Ph.D/L.L.B./J.D./M.D. The income levels are also sky-high: in 1988, median household income would’ve been ~$50,000, and the ranges like $500,000 indicate that Hunter Elementary incomes stem from life choices and career preferences as much as any limits from ability.

But it doesn’t fit the definition of great accomplishments. They mention no one winning a Nobel, or a Pulitzer, or being globally famous. Thus, in a real sense, Hunter Elementary has failed, and with it (the authors imply), the idea that IQ is the driving force behind greatness; thus, Subotnik et al spend much of the book, and other publications, pondering what is missing. If IQ is merely a necessary factor or threshold, but one that still leaves such a high chance of an ordinary life, what really makes the difference? Is the crucial ingredient a drive for mastery? Did Hunter Elementary accidentally quash students’ ambitions for a lifetime by de-emphasizing competition and grades? Or (as the other half of surveyed students maintained), did it have too much competition and broke the students mentally? Was Hunter Elementary too well-equipped a cocoon, leaving students unprepared for Hunter High and the real world, or not enough? Did the home environment determine this, or the curriculum? Did the broad academic curriculum leave students a mile wide and an inch deep and lacking in fundamentals acquired by drilling and repetition?

Sample size

But should we declare it a failure, considering the parallel lines of evidence from Roe, SMPY, and TIP? The mentioned standard is a high bar indeed. What percentage of the population can be truly said to revolutionize a field? It’s a lifetime’s work just to truly understand a field and reach the research frontier and make a meaningful contribution, and most of the population generally doesn’t even try but pursue other goals. Out of 600 students, is it reasonable to consider the Hunter Elementary experiment a failure because none has (yet - the Nobel Prize is increasingly delayed by decades)? As Gallagher then points out:

…Yet, there are very few such individuals alive in any particular era. The statistical odds against any one of them having graduated from one elementary school in New York City is great. Whether the creative rebel would have survived the selection process at Hunter, or any similar school, is one of those remaining questions that should puzzle and intrigue us.

If we consider the STEM Nobel Prizes, the USA has perhaps 1 per million people. So if even 1 HCES student had won a STEM Nobel out of 210, or 600, that would imply an enormous increase in odds ratio of >1666 ($\frac{\frac{1}{1}}{\frac{600}{1000000}}$); or to put it another, if we genuinely expected 1 or more Nobels from our HCES alumni, then to achieve that >1666 increases in odds with only +57 early-childhood IQ points, we’d also have to believe something along the lines of each individual IQ point on average increasing the odds by 29x! And of course, if we did believe in such effect sizes, we would still frequently expect to observe a HCES-sized cohort to not win a Nobel (eg if we had expected 1 Nobel prize per 600, for a probability of $\frac{1}{600}$ per student, then the probability of seeing 0 Nobels in n=600 is very high: $(1-\frac{1}{600})^{600} = 0.367$; to drive the non-Nobel probability down to <5%, we would have to expect >=3 Nobels per 600).

So it’s unclear how much weight we ought to put on the apparent failure of the HCES alumni, because even the ludicrously optimistic model is consistent with often seeing failure.

Weak childhood IQ scores: regression to the mean

How many people from Hunter Elementary come anywhere close? If we were to double-check in Wikipedia by looking for Notable people whose entries link to Hunter Elementary, perhaps because they were students there, we find painter Margaret Lefranc, linguist E. Adelaide Hahn, and minor actor Fred Melamed, and Supreme Court justice Elena Kagan (but while her mother taught at Hunter Elementary, she herself went to Hunter College High School - along with at least 95 other Notable people). I later learned that Hamilton star Lin-Manuel Miranda and scientist Adam Cohen also went to Hunter Elementary as well as High. Triple-checking in Google, this does seem to be a fair accounting - no billionaires or Nobelists suddenly pop out. If we were to judge by Wikipedia entries, it would seem that Hunter Elementary can claim around 5 Notable alumni while Hunter High can claim 96. (Checking the 96 WP entries by hand, most omit mention of the elementary school or whether they passed exams to get into Hunter High, but the ones who do always specify exams or a non-Hunter Elementary; only 1 entry, the group entry for the hip-hop band Dujeous, turns out to include a Hunter Elementary member: Loren Hammonds/Mojo the Cinematic. Overall, this comparison may be somewhat biased against Hunter Elementary but I don’t think hugely so.) This is not because Hunter High is 32x larger than Hunter Elementary: Hunter Elementary currently accepts ~50 students per year while Hunter High currently accepts ~175 + 50 grandfathered in from Hunter Elementary (total ~225), and is only 4.5x bigger - 3.5x if we exclude the Hunter Elementary alums (who do not appear in the 95+ listed, apparently). Even more strikingly, while I do not recognize the names of Lefranc, Hahn, Melamed, or Adam Cohen, I do recognize several names on the Hunter High list (Kagan, of course, but also Bruce Schneier, Mark Jason Dominus, some rappers in passing). This would imply that Hunter High grads are much more likely to achieve Notability than Hunter Elementary grads: something like 8 times more likely. Why?

Another way would be to ask what should we expect, from a statistical and psychometric point of view, from Hunter Elementary students, given the procedures and tests used? There are a number of statistical issues which can arise in intelligence research particularly: range restriction such as ceiling/floor effects, measurement error biasing correlations down and requiring correction, sampling error, loss of measurement invariance in IQ tests or test-specific learning leading to hollow gains (particularly prevalent in interventions), genetic confounding of correlations between IQ and other variables like SES, test-retest reliability, and so on. (Many of these are discussed in more detail in Hunter & Schmidt’s 2004 textbook Methods of Meta-analysis: Correcting Error & Bias in Research Findings.) As Hunter Elementary used and still uses a legitimate IQ test (Stanford-Binet Intelligence Scales), the results are not interventional or claimed to be causal, and we are concerned with them as a group compared to the general population, the last issue of reliability/predictive validity is the one which bothers me the most in trying to interpret the results.

Hunter Elementary uses IQ testing of ~5yo children, selecting those >IQ 140 and getting a mean of IQ 157 (3.8 SDs); these children are then kept enrolled in Hunter Elementary and grandfathered into Hunter High as long as their grades stay reasonable, with expulsions and transfers apparently rare (and little mentioned in the book). However, as is well known, childhood IQs are imperfect predictors of final adult IQs, for various neurological, developmental, and genetic reasons; the best possible estimate at 5yo will still only correlate with adult IQ at perhaps r=0.5-0.6. And having been selected for scoring extremely high on a particular test, Hunter Elementary kids must revert to mediocrity. What can we estimate their adult IQs to be? Since the majority of students are Jewish (or these days, split between those of Jewish and East Asian descent) whose mean is usually estimated at something like 110, we could predict that their adult IQs will not average 157, but will average 110 + (157-110) * 0.5 = 133. 133 IQ is nothing to sneeze at, but it is also only +2.2SDs and closer to 1 in 50 than 1-in-10,000; a Hunter Elementary school grad could easily not even qualify for MENSA. Or to put it another way, with 260 million people in the USA in 1993, there were around 3.6 million people with IQs >=133, of which the total Hunter Elementary cohort would represent 0.016%. If we consider cohorts of 600 children with adult mean IQs of 133, not many of them will be >157 at all - only 5% or ~32 students (mean(replicate(100000, sum(sort(rnorm(600, mean=133, sd=15))>157)))). The others will have developed into adult IQs below that, possibly much below that. This calculation doesn’t require any knowledge of outcomes and could have been done before Hunter Elementary opened: inherently, due to the limits of IQ tests in screening for extremely gifted adults based on noisy early childhood tests, most positives will be false positives. (This is the same as the famous mammography and terrorist screening examples of how an accurate test + low base-rate = surprisingly high false positive rate and low posterior probability.)

What about Hunter High? Hunter High tests sixth graders who enroll as 7th graders; 6th graders tend to be ~11yo, not 4-5yo. One correlation quoted by Eysenck is testing 11yos can have a correlation of ~0.95 with adult scores; so Hunter High grads, assuming they had the same mean (I haven’t seen any means quoted), would expect to revert to mediocrity down to 110 + (157-110) * 0.95 = 154 ie almost identical. (With a correlation of 0.9, 152, and so on). So out of 600 Hunter High alums, 252 will remain >157, or ~8x the Hunter Elementary rate.

That is, the overrepresentation of Hunter High graduates among Hunter-related Notable figures is almost identical to their overrepresentation among Hunter-related graduates who maintain their elite IQ status.

None of the materials I have read on Hunter Elementary, aside from one article in New York magazine¹² drawing on Lohman & Korb 2006’s Gifted Today but Not Tomorrow? Longitudinal changes in ability and achievement during elementary school, have mentioned the issue that IQ tests in such early childhood are simply not that predictive in finding extreme tails, or even alluded to it as a problem, so I have to wonder if Subotnik et al¹³ appreciate this point: from basic psychometric principles, we would predict that Hunter Elementary graduates will not be extraordinarily intelligent, will represent only the tiniest fraction of the population of intelligent people, and thus their adult accomplishment will not be out of line with what we observe - solid academic and social achievement. Nor is there any particular reason to attribute their failure to the atmosphere or curriculum or methods of Hunter Elementary itself.

Implications for gifted education

Given this, we would have to conclude that the idea of a gifted & talented elementary school is difficult to justify on any grounds related to focusing resources on students’ with future adult intelligence >150 as only a few percent of such students are findable with current IQ testing methods at that age, but that it makes far more sense to screen at a later age like 11yo and concentrate resources at high school or college levels. If we concluded that the gain from better education of those 5% in an elementary school is profitable and so a Hunter-like elementary school is a good idea, we should definitely not automatically enroll all such elementary school students in a even more expensive Hunter-like high school: each such grandfathered student is worth ~1/8th an outsider student in terms of potential. It would be much better to not grandfather the elementary school students - they have already been highly advantaged by the enriched education & peers, after all, so why should they be given an additional huge advantage over all the students outside the system who are equally deserving of the chance? The main reason would seem to be some sort of family or loyalty sentimental reasoning; if this bias cannot be overcome, the idea of a single vertically integrated feeder system may be actively harmful to gifted education.

Matters could be improved, though, with more broad-ranging tests.

For example, genetics: as adult IQ is a highly heritable trait with perhaps up to 80% of variance predictable from all genetic variants and >~50% predictable from all SNPs, with the heritability increasing with age and only ~25% at age 5 (Bouchard 2013), predictions of adult IQ based on 5yo testing could be improved substantially using their parents’ & siblings’ IQs, or by direct genetic prediction; this would help identify the children who are rejected because of developmental quirks but who would eventually live up to their genetic potential. If we consider a path model with genes ~> IQ (0.50), IQ_5yo ~>IQ (0.50), genes ~> IQ_5yo (0.25):

model <- 'IQ_adult ~ 0.8*Gene + 0.5*IQ_5
          IQ_5 ~ 0.25*Gene'
d <- simulateData(model)
s <- sem(model, std.ov=TRUE, data=d)
semPaths(s, "Standardized", "Estimates", style="lisrel", curve=0.8, nCharNodes=0, edge.color="black", label.scale=FALSE,
    residuals=FALSE, fixedStyle=1, freeStyle=1, exoVar=FALSE, sizeMan=10, sizeLat=24, label.cex=3, edge.label.cex = 2.2)

Then using an ideal SNP genetic score and a 5yo IQ test, one could expect to predict 0.5 + (1-0.25)*0.5 = 0.875 or 87% of variance, giving a prediction/adult IQ of sqrt(0.875) = 0.93; with this sort of predictive power, the reversion to mediocrity is minimal and Hunter Elementary kids would then have adult IQs of 110 + (157-110) * 0.93 = 153.

In that scenario, we could create a Hunter-like Elementary school which is as good at filtering as Hunter High is. While it’s unclear when we will be able to predict 50% of variance in adult IQs based on polygenic scores, in the near future we can hope for polygenic scores on the order of 10%, which would still be helpful: polygenicscore = 0.10; 110 + (157-110) * sqrt(polygenicscore + (1-0.25)*0.5) = 142.4. Besides waiting for better polygenic scores, other factors could be included in a predictive model such as parental IQs and income/education, sibling IQs, and race. I don’t know if such an elementary school for the gifted would be feasible, however: improved predictions will still maintain or increase the existing controversial racial disparities, the selection may strike the public as even more unfair than it is now, and will inherently yield classrooms with more cognitive inequality at the moment (despite how the slower students will tend to catch up with time) which may itself impede the educational mission or foster resentment & rivalry.

Ultimately, it would seem that the most justifiable reason for running Hunter Elementary is the reason that comes across most clearly reading the alumni reminiscences: because they would have been miserable in regular schools. If early-developing children must be subjected to mandatory education, then it should at least be with their peers.

Great Scott! Personal Name Collisions and the Birthday Paradox

How large does can a social circle be before first names no longer suffice for identification? Scott, I’m looking at you. –MakerOfDecisions, 29 July 2016

Scott here refers to any of Scott Alexander, Scott Adams, Scott Aaronson, Scott Sumner (and to a much lesser extent, Scott Garrabrant, Orson Scott Card, and Scott H. Young); a reference to a Scott on a site like Less Wrong is increasingly ambiguous.

When a large number of samples draw from a common pool of identifiers, collisions are common, leading to the birthday paradox: despite there being 365.25 days in the year, a classroom of just 23 people (who can cover at most 6% of the days in a year) is ~50% likely to have at least two people who share the same birthday and so birthdays cease being unique unambiguous identifiers. (Intuitively, you might expect the number to be much larger and closer to 180 than 23.)

We can verify this by simulation:

dupes <- function(a) { length(a) != length(unique(a)) }

identifiers <- function(n, ids, probabilities) { sample(1:ids, n, prob=probabilities, replace=TRUE) }

simulate <- function(n, ids, probabilities=rep(1/ids, ids), iters=10000) {
    sims <- replicate(iters, { id <- identifiers(n, ids, probabilities)
                               return(dupes(id)) })
    return(mean(sims)) }

simulate(23, 365)
# [1] 0.488
sapply(1:50, function(n) { simulate(n, 365) } )
#  [1] 0.0000 0.0029 0.0059 0.0148 0.0253 0.0400 0.0585 0.0753 0.0909 0.1196 0.1431 0.1689 0.1891
#      0.2310 0.2560 0.2779 0.3142 0.3500 0.3787 0.4206 0.4383 0.4681 0.5165 0.5455 0.5722 0.5935
# [27] 0.6227 0.6491 0.6766 0.7107 0.7305 0.7536 0.7818 0.7934 0.8206 0.8302 0.8465 0.8603 0.8746
#      0.8919 0.9040 0.9134 0.9248 0.9356 0.9408 0.9490 0.9535 0.9595 0.9623 0.9732

Similarly, in a group of people, it will be common for first names to overlap. How common? There are far more than 365.25 first names, especially as some first names are made up by parents.

Names have a highly skewed (often said to be a power law) distribution: the first few baby names make up an enormous fraction of all names, hence all the Ethan/Lucas/Mason baby boys in 2016. (One would think that parents would go out of their way to avoid too-popular names, but apparently not.)

Since there are only 10,000 things under heaven, one might think that the top 10000 personal names would give a good guess. At what n can we expect a collision?

findN <- function(ids, targetP=0.5, startingN=1, probabilities=rep(1/ids, ids)) {
    n <- startingN
    collisionProbability <- 0

    while (collisionProbability < targetP) {
        collisionProbability <- simulate(n, ids, probabilities)
        n <- n+1
    }
    return(n) }
findN(10000)
# [1] 118
simulate(118, 10000)
# [1] 0.5031

We could also use an approximation such as the square approximation: $n \approx \sqrt { 2m \times p(n)}$: sqrt(2 * 10000 * 0.5) ~> 100 Or the similar upper bound: ceiling(sqrt(2*10000*log(2))) ~> 118.

So the collision point is smaller than Dunbar’s number.

But all of these are themselves upper bounds because the case in which birthdays/names are uniformly distributed is the worst case. If there is any difference in the probabilities, a collision will happen much earlier. This makes sense since if 1 birthday happens with, say, P=0.99, then it’s almost impossible to go more than 3 or 4 birthdays without a collision. Likewise, if one birthday has P=0.50, and so on down to P=$:

sapply(1:23, function(n){ simulate(n, 365, probabilities=c(0.99, rep(0.01/364, 364)))})
# [1] 0.0000 0.9789 0.9995 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#     1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
sapply(1:23, function(n){ simulate(n, 365, probabilities=c(0.5, rep(0.5/364, 364)))})
# [1] 0.0000 0.2531 0.5031 0.6915 0.8182 0.8896 0.9402 0.9666 0.9808 0.9914 0.9951 0.9973 0.9988
#     0.9993 0.9991 0.9999 1.0000 1.0000 0.9999 1.0000 1.0000 1.0000 1.0000

How skewed are real names? Given Names Frequency Project provides Popular Given Names US, 1801-1999 (1990-1999, 909288 names) based on Social Security data. After deleting the first 4 lines of s1990m.txt, it can be loaded into R and the fractions used as probabilities to find the 50% collision point for US names:

names <- read.csv("s1990m.txt", header=FALSE)
summary(names)
#         V1            V2
#  Aaron   :  1   Min.   :   55.0000
#  Abdiel  :  1   1st Qu.:   86.0000
#  Abdullah:  1   Median :  183.0000
#  Abel    :  1   Mean   :  914.1923
#  Abraham :  1   3rd Qu.:  535.5000
#  Adam    :  1   Max.   :24435.0000
#  (Other) :852
sum(names$V2)
# [1] 784377
## "Scott" as fraction of all names:
2279 / 784377
# [1] 0.0029054906
## presumably male names:
2279 / (784377*0.5)
# [1] 0.005810981199

simulate(118, nrow(names), probabilities=names$V2/sum(names$V2))
# [1] 1
findN(nrow(names), probabilities=names$V2/sum(names$V2))
# [1] 15

So a more realistic analysis suggests n=15 is where unique first names will probably break down.

This only covers the 853 most common personal names, and the more names, the higher the n has to be to trigger a collision (making 15 something of a lower upper bound); to estimate 10000, we need to fit a distribution to extrapolate below that. The log normal distribution fits reasonably well and is easy to work with:

library(fitdistrplus)
fitdist(names$V2, "lnorm")
# Fitting of the distribution ' lnorm ' by maximum likelihood
# Parameters:
#            estimate    Std. Error
# meanlog 5.550448321 0.04640182299
# sdlog   1.359185357 0.03281096378

simulateLN <- replicate(100, {
    names <- rlnorm(10000, meanlog=5.550448321, sdlog=1.359185357)
    hit <- findN(length(names), startingN=46, probabilities=names/sum(names))
    return(hit)
    })
median(simulateLN)
# [1] 51

Since names will cluster by age group, location, profession, and whatnot, arguably even 51 is a bit of an upper bound.

Finally, one might ask the probability of a group with a great Scott, or to put it another way, the probability of it unfortunately getting away scot-free.

This is easy to answer; the probability of having 1 or more Scotts in a group is the probability of everyone having a name other than Scott. We saw that the probability of being named Scott was 0.0029054906 in the name dataset. So the probability of one person not being named Scott is 1-0.0029054906 or 0.997. So the probability of n people all being named not-Scott is 0.997ⁿ. The crossover point is ~239.

So an American social group cannot exceed n=51 before first names begin to break down, and it is all Scott’s fault at n=239.

Detecting fake (human) Markov chain bots

Some popular Twitter and Tumblr accounts use Markov chains trained on a corpus of writing such as Markov James Mitchens or two unrelated corpuses to create amusing mashups: programming documentation and H.P. Lovecraft’s horror/SF fiction or the King James Bible or the works of Karl Marx, Kim Kardashian and Kierkegaard, or Silicon Valley recruiting emails and Erowid drug use reports. The humor comes from the fact that the Markov chains have no understanding and are merely programs producing gibberish that occasionally present striking juxtapositions or insights. Much of their appeal derives in large part from the fact that while humans curate them, humans don’t write them. They depend on this authenticity to be striking.

Of course, there’s always the temptation to edit them or write them wholesale, perhaps because the Markov chains aren’t cooperating in producing any comedy gold to tweet that day, which deceives the reader. This poses an inverse Turing test: how would you detect a fake Markov chain account, that is, one where a human is pretending to be a computer and writing some of the text?

Markov chains are trained on a specific corpus and are a probabilistic generative model which encode the probability that a word X follows another word Y for all the words in that corpus (and similarly if they are operating on letters or on n-grams); there is no state or memory or look back or ability to model recursion. To generate text, one simply picks a random word Y, looks up the probabilities of all the words A…Z from Y, and picks a word at random weighted by those probabilities; then repeat indefinitely. Conversely, one could also use it to calculate the likelihood of a given text by multiplying the probability of each word in the text conditional on the previous one.

One difficulty is the potential for double-use of data: the first pass through a Markov chain account is already applying to the data a highly flexible Bayesian neural network with billions of parameters (one’s brain). If one spots an anomalous dataset and subsequent analysis confirms it, what does this mean? I am reminded of one past incident: someone had lost a great deal of money on a Bitcoin gambling website, and suspected the site had defrauded him. But he had contacted me only because he had had unusual losses. What does an analysis mean? Imagine that the top 1% of losers get angry and start looking into whether they were cheated; they go to a statistician who duly computes that based on the number of games played, there is a p=0.01 that they would lose as much or more as they did… If one had all the gambling records, one could look at the overall patterns and see if there are more losers than there should be given the rules of the game and a supposedly fair random number generator, but what does one do with 1 self-selected player? The data generation process is certainly neither random nor ignorable nor modelable without dubious assumptions.

A few possible attacks come to mind:

• observation of malformed syntax or lack of long-range dependencies
• vocabulary or output outside an independently trained Markov chain’s domain
• unusually low likelihood for an independently trained Markov chain to generate known samples
• unusually low likelihood for an independently trained Markov chain to generate known samples compared to newly generated samples filtered at a 1-in-100s quality level
• unusually high quality of known samples compared to newly generated samples from independently trained Markov chain filtered at a 1-in-100s quality level, tested nonparametrically or parametrically as a mixture model

Markov chains produce realistic-looking output and are efficient to create & run, but, compared to RNNs, they notoriously model recursive syntax poorly, such as nested parentheses (since they have no way of remembering whether a parenthetical comment had been started), and cannot extrapolate - for example, a word-level Markov chain can’t create new words, and would require n-grams to have available fragments of words which could be recombined. The memory-less nature of Markov chains also means that, lacking any memory which could model the long-range correlations found in natural English text like systematic use of particular names/topics/vocabulary, larger samples quickly veer off-topic and become gibberish and lack any coherency possibly even inside a single sentence. (RNNs also have this problem, but somewhat less.)

With the limits of a Markov chain in mind, it would be easy to detect faked Markov chain output with large samples: it is just difficult for a human to deliberately generate long text which is as nonsensical and syntactically invalid as a Markov chain creates, for the same reason an unpracticed human is a remarkably bad random number generator. However, for this same reason the selected Markov samples tend to be very short, usually no more than a sentence. It might be possible to measure this on the samples as a whole and observe higher entropy or memoryless-ness (eg by measuring compression performance or efficiency of a Markov chain in modeling the samples), but I would guess that usually the samples are not long enough or large enough for this to have reasonable statistical power as a test. This eliminates the easiest test.

Since the corpus is known in many of these cases, we can assume access to a Markov chain model which is similar (if not identical) to the one which supposedly wrote all the tweets. This gives us several possibilities.

We could exploit the lack of creativity of Markov chains and look for anything in the tweets which is not present in the original corpus. For example, if a word like cromulent appears neither in the Puppet documentation nor (having been coined in 1996, 59 years after he died) in H.P. Lovecraft’s fiction, then it would have a probability of 0 of being generated by any Puppet/Lovecraft Markov chain (as no word will have any transition probability to it). Such extra-corporal vocabulary immediately proves human authorship.

Continuing this same logic, we could take the corpus, train our own Markov chain (which will at least be similar), and use it to calculate the likelihood of all the tweets. A human-written tweet may be possible for the Markov chain to have written, but it will be far more unlikely than most of the ones the Markov chain actually wrote & were selected. So we would see that most of the tweets have reasonable log likelihoods, but that our suspicious ones will be far more extreme. (If the Markov chains are word-level, this test subsumes the impossible-word test: any tweet with a word not in the corpus, and hence not represented in the Markov chain, will have a meaningless likelihood.)

This likelihood test might not help if they are all equally extreme, in which case one could use our Markov chain in another manner, as a generative model, to try to estimate the likelihood of getting as great a tweet. For this, one samples several thousand samples from our Markov chain, and screens them for good ones. This creates an empirical distribution of the likelihoods of good tweets conditional on the null hypothesis of a Markov chain author; in this case, the null hypothesis is known to be true by construction. Then to test, one compares the known-Markov-chain tweets with the likelihoods of the suspect tweets (perhaps with a permutation test). They should be similar.

Alternately, if one doesn’t want to use likelihoods as a measure of improbability, one could instead use some human measure of funniness like having rating the originals and the samples on a scale 1-5, and comparing them. The original poster is probably not screening more than a few hundred generated tweets for each selected tweet, so given a similar level of stringency, one’s generated tweets should be equally good; if the originals turn out to be extremely better than yours, to a level where you would have to screen thousands of random samples, that is highly suspicious and suggests the originals were too good to be true.

With ratings or likelihoods, one could try to assume a decreasing distribution like an exponential: most samples will be incoherent and totally unfunny, many will be slightly funny, a few will be funny, and a very few will be very funny. The ratings on samples generated from our Markov chain will probably follow a smooth distribution. However, if a human is authoring some in an attempt to spice things up, they will be above the average of the Markov chain (otherwise why bother with cheating?), and if there is a substantial number of them, this will create an anomaly in the ratings of the originals - a bump indicating that the tweets are coming from two different populations. In this case, it can be modeled as a mixture model with either k=1 or k=2, and the p-value or Bayesian posterior probability calculated for 1 vs 2.

Optimal Existential Risk Reduction Investment

An existential risk is any risk which destroys or permanently cripples human civilization, such as an asteroid strike or pandemic. Since humanity might otherwise continue for millions of years, creating untold trillions of humans and colonizing the galaxy, human extinction represents the loss of literally astronomical amounts of utility. The loss is greater than any disaster up to extinction levels, as humanity can always recover from lesser disasters; but there is no recovery from a total destruction. Thus, the expected value of even a slight reduction in an exotic risk ought to itself be astronomical, or at least extremely large; under plausible values for well-characterized x-risks like asteroid strikes or nuclear war or pandemic, preventing them may be the charitable spending with the highest expected value and they should be receiving all charitable expenditures.

This strikes people as odd and dangerous reasoning. Is it really true that we should be spending almost unlimited amounts of money on these things and not otherwise extremely compelling charities like distributing malaria nets in Africa to save millions of lives or vaccine distribution or funding research into ending aging? And if we should, how do we choose what fraction to spend on global warming rather than artificial intelligence? What if someone discovers an entirely new x-risk not previously considered, like nearby supernovas or vacuum collapses or nanotechnology grey goo?

Thinking historically, it’s clear in retrospect that someone concerned about x-risk would be better off not going after the terminal goal of x-risk reduction but instead spending their money on instrumental goals such as science/technology or economic growth.

Imagine someone in England in 1500 who reasons the same way about x-risk: humanity might be destroyed, so preventing that is the most important task possible. He then spends the rest of his life researching the Devil and the Apocalypse. Such research is, unfortunately, of no value whatsoever unless it produces arguments for atheism demonstrating that that entire line of enquiry is useless and should not be pursued further. But as the Industrial and Scientific Revolutions were just beginning, with exponential increases in global wealth and science and technology and population, ultimately leading to vaccine technology, rockets and space programs, and enough wealth to fund all manner of investments in x-risk reduction, he could instead had made a perhaps small but real contribution by contributing to economic growth by work & investment or making scientific discoveries.

For example, Isaac Newton’s discoveries in astronomy and the laws of motion helped inaugurate threads of work that led directly to space satellites which can watch for asteroids with Earth-crossing orbits. Isaac Newton himself was concerned with x-risk, as he feared that Halley’s Comet would, centuries hence, plunge into the Sun and cause expansion destroying the Earth and humanity. What could Newton have done to directly reduce this x-risk at the time? Absolutely nothing. There were no feasible counter-measures nor any foreseeable technologies which could forestall a comet or protect humanity from the Sun engulfing the Earth; there was not and still is not a mine or bomb shelter deep enough for that. What he could have done is close to what he did do: make fundamental advances in science which posterity could build on and one day be rich and wise enough to do something about the x-risk. As it happens, Newton was not quite right about Halley’s Comet (comets are not a meaningful fraction of the Sun’s mass) but there was an x-risk he was unaware of: giant asteroid impacts. And the solutions to a giant comet - observe all comets carefully to project their future orbits, destroy it, redirect its orbit, evacuate human colonists to safety to unaffected planets (Newton suggested the satellites of the gas giants) - are much the same as for a giant asteroid impact, and all benefit from economic growth & greater science/technology (someone has to pay for, and design those satellites and spacecraft, after all).

Economic wealth & science/technology are all-purpose goods: they are useful for compound growth, and can also be spent on x-risk reduction. They are the ultimate instrumental goods. If one is badly ignorant, or poor, or unable to meaningfully reduce an x-risk, one is better off accepting the x-risk and instead spending resources on fixing the former problems. One would prefer to get rid of the x-risk as soon as possible, of course, but given one’s starting position, there may simply be no better strategy and the risk must be accepted.

This raises the question: what is the optimal distribution of resources to economic growth vs x-risk reduction over time which maximizes expected utility?

Intuitively, we might expect something like early on investing nothing at all in x-risk reduction as there’s not much money available to be spent, and money spent now costs a lot of money down the line in lost compound growth; and then as the economy reaches modern levels and the opportunity cost of x-risk becomes dire, money is increasingly diverted to x-risk reduction. One might analogize it to insurance - poor people skimp on insurance because they need the money for other things which hopefully will pay off later like education or starting a business, while rich people want to buy lots of insurance because they already have enough and they fear the risks. If this were an investment question, a good strategy would be something like the Kelly criterion or probability matching strategies like Thompson sampling: even if the expected value of x-risk reduction is higher than other investments, it only pays off very rarely and so receives a very small fraction of one’s investments. However, it’s not clear that the Kelly criterion or Thompson sampling are optimal or even relevant: because while Kelly avoids bankruptcy in the form of gambler’s ruin but does so only by making arbitrarily small bets to avoid going bankrupt & refusing to ever risk one’s entire wealth; with x-risks, the bankruptcy (extinction) can’t be avoided so easily, as the risk is there whether you like it or not, and one cannot turn it to 0. (This comes up often in discussion of why the Kelly criterion is relevant to decision-making under risk; see also Peters 2011 and the niche area of evolutionary finance like Evstigneev et al 2008/Lensberg & Schenk-Hoppé 2006 which draws connections between the Kelly criterion, probability matching, long-term survival & evolutionary fitness.) In economics, similar questions are often dealt with in terms of the life-cycle hypothesis in which economic agents strive to maximize their utility over a career/lifetime while avoiding inefficient intertemporal allocation of wealth (as Mark Twain put it, when in youth a dollar would bring a hundred pleasures, you can’t have it. When you are old, you get it & there is nothing worth buying with it then. It’s an epitome of life. The first half of it consists of the capacity to enjoy without the chance; the last half consists of the chance without the capacity.); in the life-cycle, one tries to build wealth as quickly as possible while young, even going into debt for investments like a college education, then begins saving up, consuming some, until retirement, at which point one consumes it all until one dies. But as far as I’ve seen any results, life-cycle models tend to not include any mechanism for spending in order to reduce mortality/aging and accept the risk of death as a given.

We could create a simple Markov decision process model. An agent (humanity), each time period (year), has a certain amount of wealth and an x-risk probability P. In this period, it can choose to allocate that wealth between economic growth, in which case it receives that investment plus a return, and it can buy a permanent percentage reduction in the x-risk for a fixed sum. For the reward, the x-risk is binary sampled with probability P; if the sample is true, then the reward is 0 and the decision process terminates, else the reward is the wealth and the process continues. Let’s imagine that this process can run up to 10,000 time periods, with a starting wealth of $248 billion (Angus Deaton’s estimate of PPP world GDP in 1500 https://en.wikipedia.org/wiki/List_of_regions_by_past_GDP_%28PPP%29 ), the economic growth rate is 2% (the long-run real growth rate of the global economy), the existential risk probability is 0.1% per year (arbitrarily chosen), and one can buy a reduction of 1% for a billion dollars. (We’ll work in trillions units to help numeric stability.) What strategy maximizes the cumulative rewards? A few simple ones come to mind:

1. the agent could simply ignore the x-risk and reinvests all wealth, which to a first approximation, is the strategy which has been followed throughout human history and is primarily followed now (lumping together NASA’s Spaceguard program, biowarfare and pandemic research, AI risk research etc probably doesn’t come to more than $1-2b a year in 2016). This maximizes economic growth rate but may backfire as the x-risk never gets reduced.
2. the agent could spend the full gain in its wealth from economic growth (2%) on x-risk reduction. The wealth doesn’t grow and the returns from x-risk reduction do diminish, but the x-risk is at least reduced greatly over time.
3. the agent could implement a sort of probability matching: it spends on x-risk reduction a fraction of its wealth equal to the current P. This reduces how much is spent on extremely small x-risk reductions, but it might be suboptimal because it’ll pay the largest fraction of its economy in the first time period, then second-largest in the second time period and so on, losing out on the potential compounding.
4. a more complicated hybrid strategy might work: it maximizes wealth like #1 for the first n time periods (eg n=516), and then it switches to #2 for the remaining time period
5. like #4, but switching from #1 to #3 for the remaining time periods.

constantInvestmentAgent <- function (t, w, xrp) { return(c(w, 0)) }
constantReductionAgent  <- function (t, w, xrp) { drawdown <- 0.9803921573; return(c(drawdown*w, (1-drawdown)*w)) }
probabilityMatchAgent   <- function (t, w, xrp) { return(c(w*(1-xrp), w*xrp)) }
investThenReduceAgent   <- function (t, w, xrp, n=516) { if (t<n) { return(constantInvestmentAgent(t, w, xrp)) } else { return(constantReductionAgent(t, w, xrp)) } }
investThenMatchAgent    <- function (t, w, xrp, n=516) { if (t<n) { return(constantInvestmentAgent(t, w, xrp)) } else { return(probabilityMatchAgent(t, w, xrp)) } }

simulateWorld <- function(agent, t=10000) {
    initialW <- 0.248
    initialP <- 0.001
    df <- data.frame(T=0, Wealth=initialW, XriskP=initialP)

    for (i in 1:t) {
        last <- tail(df, n=1)
        xrisk <- rbinom(1,1, p=last$XriskP)
        if (xrisk) { break; } else {
          choices <- agent(last$T, last$Wealth, last$XriskP)
          newXriskP <- last$XriskP * (1 - 0.01)^(choices[2] / 0.001)
          newWealth <- choices[1] * 1.02
          df <- rbind(df, data.frame(T=i, Wealth=newWealth, XriskP=newXriskP))
          }
         }
   df$Reward <- cumsum(df$Wealth)
   return(df)
   }

library(parallel); library(plyr)
simulateWorlds <- function(agent, iters=1000) {
    mean(ldply(mclapply(1:iters, function(i) { tail(simulateWorld(agent), n=1)$Reward }))$V1)  }

simulateWorlds(constantReductionAgent)
# [1] 2423.308636
simulateWorlds(investThenReduceAgent)
# [1] 10127204.73
simulateWorlds(constantInvestmentAgent)
# [1] 1.154991741e+76
simulateWorlds(investThenMatchAgent)
# [1] 7.53514145e+86
## Optimize the switch point:
which.max(sapply(seq(1, 10000, by=100), function(N) { simulateWorlds(function(t,w,xrp) { investThenMatchAgent(t, w, xrp, n=N) }, iters=100)}))
# [1] 3
simulateWorlds(function(t,w,xrp) { investThenMatchAgent(t, w, xrp, n=300) })
# [1] 9.331170221e+86
simulateWorlds(probabilityMatchAgent)
# [1] 1.006834082e+87

So of our 5 strategies, the constant reduction agent performs the worst (probably because with economic growth choked off, it can only buy small x-risk reductions), followed by the invest-then-reduce agent; then the get rich before you get old constant investment agent manages to often attain very high growth rates when it’s lucky enough that x-risks don’t strike early on; but far better than any of them, by orders of magnitude, are the partial and full probability matching agents. The partial probability matching agent turns out to have a suboptimal switch point t=516, and a more careful search of switch points finds that t~=300 is the best switch point and it exceeds the pure probability matcher which matches from the start.

What’s going on there? I suspect it’s something similar to the difference in multi-armed bandit problems between the asymptotically optimal solution and the optimal solution for a fixed horizon found using dynamic programming: in the former scenario, there’s an indefinite amount of time to do any exploration or investment in information, but in the latter, there’s only a finite time left and exploration/growth must be done up front and then the optimal decision increasingly shifts to exploitation rather than growth.

Why does probability matching in general work so well? It may simply be because it’s the only baseline strategy which adjusts its xrisk investment over time.

This doesn’t demonstrate that probability matching is optimal, just that it beats the other baseline strategies. Other strategies could be used to decrease xrisk investment over time - instead of being proportional to xrisk P, it could shrink linearly over time, or by square root, or logarithmically, or…

What reinforcement learning techniques might we use to solve this?

This problem represents a large Markov Decision Process with 1 discrete state variable (time, t=0-10000), 2 continuous state variables (wealth, and risk probability), and 1 continuous action (fraction of growth to allocate to the economy vs existential risk reduction). The continuous action can be discretized into 11 actions without probably losing anything (allocate 100%/90%..10%/0%), but the 2 state variables can’t be discretized easily because they can span many orders of magnitude.

• dynamic programming a decision tree with backwards induction: optimal, but requires discrete actions and state variables, and even if discretized, 10000 time steps would be infeasibly large.

• standard tabular learning: Q-learning, SARSA, temporal differences: requires discrete actions and state variables

  • Deep Q-Networks: requires discrete actions, but not state variables
• MDP solvers: value iteration etc: optimal, but requires discrete actions and state variables

• hybrid MDP solvers: optimal, and can handle a limited amount of continuous state variables (but not continuous actions), which would work here; but high quality software implementations are rarely available.

  One such hybrid MDP solver is hmpd, which solves problems specified in the PDDL Lisp-like DSL (judging from the examples, a version with probabilistic effects, so PPDDL 1.0?). After trying to write down a PPDDL model corresponding to this scenario, it seems that PPDDL is unable to represent probabilities or rewards which change with time and so cannot represent the increase in wealth or decrease in x-risk probability.

• policy gradients: can handle continuous state variables & actions but are highly complex and unstable; high quality software implementations are unavailable

Of the possible options, a DQN agent seems like the best choice: a small neural network should be able to handle the problem and DQN only requires the actions to be discretized. reinforce.js provides a DQN implementation in JS which I’ve used before, so I start there by rewriting the problem in JS

var script = document.createElement("script");
script.src = "https://www.gwern.net/docs/rl/armstrong-controlproblem/2016-02-02-karpathy-rl.js";
document.body.appendChild(script);

// environment: t, w, xrp
function simulate(environment, w_weight, xrp_weight) {
    var xrisk = Math.random() < environment.xrp
    if (xrisk) {
    return {reward: -100, alive: false, t: environment.t, w: environment.w, xrp: environment.xrp};
    } else {
    return {reward: Math.log(environment.w), alive: true, t: environment.t+1,
            w: environment.w*w_weight*1.02, xrp: environment.xrp * (Math.pow((1 - 0.01), (xrp_weight / 0.001))) }
 }
}
var defaultState = {t: 0, w: 0.248, xrp: 0.01}
// simulate(defaultState, 0.99, 0.01)
// simulate(defaultState, 0.99, 0.01)


var env = {};
env.getNumStates = function() { return 3; }; // there are only 3 state variables: t/w/xrp
env.getMaxNumActions = function() { return 11; }; // we'll specify 10 possible allocations: 1/0, 0.998/0.002 .. 0.98/0.02
var spec = {
  num_hidden_units: 200,
  experience_add_every: 20,
  learning_steps_per_iteration: 1,
  experience_size: 1000000,
  alpha: 0.01,
  epsilon: 1.0,
  gamma: 0.99 // minimal discounting
};
var agent = new RL.DQNAgent(env, spec);

var total_reward = 0;
state = defaultState;
spec.epsilon = 1.0; // reset epsilon if we've been running the loop multiple times

for(var i=0; i < 10000*3000; i++) {
   var action = agent.act(state)
   state = simulate(state, 1-(action/500), 0+(action/500) );
   agent.learn(state.reward);

   total_reward = total_reward + state.reward;
   if (Number.isInteger(Math.log(i) / Math.log(10)) ) { spec.epsilon = spec.epsilon / 1.5; } // decrease exploration

   if (!state.alive || state.t >= 10000) { // if killed by x-risk or horizon reached
     console.log(state.t, state.w, state.xrp, total_reward);
     total_reward = 0;
     state = defaultState;
     }
}

//exercise the trained agent to see how it thinks
total_reward=0
state=defaultState;
spec.epsilon = 0;
for (var t=0; t < 10000; t++) {
    action = agent.act(state)
    state = simulate(state, 1-(action/500), 0+(action/500) );
    total_reward = total_reward + state.reward
    console.log(action, state, total_reward);
    }

After a day of training, the DQN agent had learned to get up to 5e41, which was disappointingly inferior to the constant investment & probability matching agents (1e87). The NN looks big enough for this problem and the experience replay buffer was more than adequate; NNs in RL are known to have issues with the reward, though, and typically clamp the reward to a narrow range, so I suspected that rewards going up to 5e41 (interpreting wealth on each turn as the reward) might be playing havoc with convergence, and switched the reward to log wealth instead. This did not make a noticeable difference overnight (aside from the DQN agent now achieving 9.5e41). I wondered if the risk was too rare for easy learning and 100 neurons was not enough to approximate the curve over time, so I fixed a bug I noticed where the simulation did not terminate at t=10000, doubled led the neuron count, increased the initial x-risk to 1%, and began a fresh run. After 1 day, it reached 9.4e41 total reward (unlogged).

Cumulative log score for DQN after tweaks and ~2h of training: regularly reaches ~470k when it doesn’t die immediately (which happens ~1/20 of the time). In comparison, probability-matching agent averages a cumulative log score of 866k. After 2 days of training, the DQN had improved only slightly; the on-policy strategy appears mostly random aside from having driven the xrisk probability down to what appears to be the smallest float JS supports, so it still had not learned a meaningful compromise between xrisk reduction and investment.

TODO: revisit with MCTS at some point?

Model Criticism via Machine Learning

In Deep learning, model checking, AI, the no-homunculus principle, and the unitary nature of consciousness, Andrew Gelman writes

Here’s how we put it on the very first page of our book:

The process of Bayesian data analysis can be idealized by dividing it into the following three steps:

1. Setting up a full probability model - a joint probability distribution for all observable and unobservable quantities in a problem. The model should be consistent with knowledge about the underlying scientific problem and the data collection process.
2. Conditioning on observed data: calculating and interpreting the appropriate posterior distribution - the conditional probability distribution of the unobserved quantities of ultimate interest, given the observed data.
3. Evaluating the fit of the model and the implications of the resulting posterior distribution: how well does the model fit the data, are the substantive conclusions reasonable, and how sensitive are the results to the modeling assumptions in step 1? In response, one can alter or expand the model and repeat the three steps.

How does this fit in with goals of performing statistical analysis using artificial intelligence?

3. The third step - identifying model misfit and, in response, figuring out how to improve the model - seems like the toughest part to automate. We often learn of model problems through open-ended exploratory data analysis, where we look at data to find unexpected patterns and compare inferences to our vast stores of statistical experience and subject-matter knowledge. Indeed, one of my main pieces of advice to statisticians is to integrate that knowledge into statistical analysis, both in the form of formal prior distributions and in a willingness to carefully interrogate the implications of fitted models.

One way of looking at step #3 is to treat the human statistician as another model: specifically, he is a large neural network with trillions of parameters, who has been trained to look for anomalies & model misspecification, and to fix them when he finds them, retraining the model, until he can no longer easily distinguish the original data from the model’s predictions or samples. As he is such a large model with the ability to represent and infer a large class of nonlinearities, he can usually easily spot flaws where the current model’s distribution differs from the true distribution.

This bears a considerable resemblance to the increasing popularity of generative adversarial networks (GANs): using pairs of neural networks, one of which tries to generate realistic data, and a second which tries to classify or discriminate between real and realistic data. As the second learns ways in which the current realistic data is unrealistic, the first gets feedback on what it’s doing wrong and fixes it. So the loop is very similar, but fully automated. (A third set of approaches this resembles is actor-critic reinforcement learning algorithms.)

If we consider the kinds of models which are being critiqued, and what is critiquing, this gives us 4 possible combinations:

1. Simple/simple is useful for cases like linear regression where classic methods like examining residuals or R^2s or Cook indexes can often flag problems with the model.
2. Simple/complex is also useful, as the human statistician can spot additional problems.
3. Complex/simple is probably useless, as the NNs may easily have severe problems but will have fit any simple linear structure and fool regular diagnostics.

4. Complex/complex can be very useful in machine learning, but in different ways from a good simple model.

   Fast, simple, general - a good statistical method lets you choose one; a great method lets you choose two. (Consider linear models, decision trees, NNs, MCMC, ABC, discrete Bayesian networks, and exponential family vs nonparametric methods as examples of the tradeoffs here.)

So is quadrant 2 fully populated by human statisticians? We wouldn’t necessarily want to use GANs for everything we use statisticians for now, because neural networks can be too powerful and what we want from our models is often some sort of clear answer like does X predict Y? and simplicity. But we could replace the statistician with some other powerful critic from machine learning - like a NN, SVM, random forest, or other ensemble. So instead of having two NNs fighting each other as in a GAN, we simply have one specified model, and a NN which tries to find flaws in it, which can then be reported to the user. The loop then becomes: write down and fit a model to the real data; generative posterior predictive samples from the distribution; train a small NN on real data vs predictive data; the classification performance measures the plausibility of the predictive samples (perhaps something like a KL divergence), giving a measure of the model quality, and flags data points which are particularly easily distinguished as real; the human statistician now knows exactly which data points are not captured by the model and can modify the model; repeat until the NN’s performance declines to chance.

Let’s try an example. We’ll set up a simple linear model regression Y ~ A + B + C with a few problems in it:

1. the trend is not linear but slightly quadratic
2. the outcome variable is also right-censored at a certain point
3. and finally, the measured covariates have been rounded

set.seed(2016-11-23)
n <- 10000
ceiling <- 1
a <- rnorm(n)
b <- rnorm(n)
c <- rnorm(n)
y <- 0 + 0.5*a + 0.5*b + 0.5*c^2 + rnorm(n)
y_censored <- ifelse(y>=3, 3, y)
df <- data.frame(Y=y_censored, A=round(a, digits=1), B=round(b, digits=1), C=round(c, digits=1))

l <- lm(Y ~ A + B + C, data=df)
summary(l)
plot(l)
plot(df$Y, predict(l, df))

l2 <- lm(Y ~ A + B + I(C^2), data=df)
summary(l2)
plot(df$Y, predict(l2, df))

The censoring shows up immediately on the diagnostics as an excess of actual points at 3, but the quadraticity is subtler, and I’m not sure I can see the rounding at all.

library(randomForest)

## First, random forest performance under the null hypothesis

modelNull <- data.frame(Y=c(df$Y, df$Y), Real=c(rep(1, n), rep(0, n)), A=c(df$A, df$A), B=c(df$B, df$B), C=c(df$C, df$C))
r_n <- randomForest(as.ordered(Real) ~ Y + A + B + C, modelNull); r_n
#                Type of random forest: classification
#                      Number of trees: 500
# No. of variables tried at each split: 2
#
#         OOB estimate of  error rate: 100%
# Confusion matrix:
#       0     1 class.error
# 0     0 10000           1
# 1 10000     0           1

modelPredictions <- data.frame(Y=c(df$Y, predict(l, df)), Real=c(rep(1, n), rep(0, n)), A=c(df$A, df$A), B=c(df$B, df$B), C=c(df$C, df$C))
r <- randomForest(as.ordered(Real) ~ Y + A + B + C, modelPredictions); r
#                Type of random forest: classification
#                      Number of trees: 500
# No. of variables tried at each split: 2
#
#         OOB estimate of  error rate: 6.59%
# Confusion matrix:
#      0    1 class.error
# 0 9883  117      0.0117
# 1 1200 8800      0.1200

## many of the LM predictions are identical, but the RF is not simply memorizing them as we can jitter predictions and still get the same classification performance:
modelPredictions$Y2 <- jitter(modelPredictions$Y)
randomForest(as.ordered(Real) ~ Y2 + A + B + C, modelPredictions)
#...                Type of random forest: classification
#                      Number of trees: 500
# No. of variables tried at each split: 2
#
#         OOB estimate of  error rate: 6.57%
# Confusion matrix:
#      0    1 class.error
# 0 9887  113      0.0113
# 1 1200 8800      0.1200

Note we need to be careful about collecting the posterior predictive samples: if we collect 10000 posterior samples for each of the 10000 datapoints, we’ll store 10000² numbers which may cause problems. 1 should be enough.

library(runjags)
model <- 'model {
 for (i in 1:n) {
     mean[i] <- mu + betaA*A[i] + betaB*B[i] + betaC*C[i]
     Y[i] ~ dnorm(mean[i], tau)
     }

 sd   ~ dgamma(0.01, 0.01)
 tau  <- 1/sqrt(sd)

 mu ~ dnorm(0, 100)
 betaA ~ dnorm(0, 100)
 betaB ~ dnorm(0, 100)
 betaC ~ dnorm(0, 100)
}'
model <- run.jags(model, data = with(df, list(Y=c(Y, rep(NA, nrow(df))), A=c(A,A), B=c(B,B), C=c(C,C), n=2*nrow(df))), inits=list(mu=0.45, sd=0.94, betaA=0.47, betaB=0.46, betaC=0), monitor=c("Y"), n.chains = 1, sample=1)

posterior_predictive <- tail(n=10000, model$mcmc[[1]][1,])
plot(df$Y, posterior_predictive)

modelPredictions_r <- data.frame(Y=c(df$Y, posterior_predictive), Real=c(rep(1, n), rep(0, n)), A=c(df$A, df$A), B=c(df$B, df$B), C=c(df$C, df$C))
r <- randomForest(as.ordered(Real) ~ Y + A + B + C, modelPredictions_r); r
#         OOB estimate of  error rate: 49.11%
# Confusion matrix:
#      0    1 class.error
# 0 4953 5047      0.5047
# 1 4776 5224      0.4776

model_rounded <- 'model {
 for (i in 1:n) {
     roundA[i] ~ dround(A[i], 3)
     roundB[i] ~ dround(B[i], 3)
     roundC[i] ~ dround(C[i], 3)
     mean[i] <- mu + betaA*roundA[i] + betaB*roundB[i] + betaC*roundC[i]
     Y[i] ~ dnorm(mean[i], tau)
     }

 sd   ~ dgamma(0.01, 0.01)
 tau  <- 1/sqrt(sd)

 mu ~ dnorm(0, 100)
 betaA ~ dnorm(0, 100)
 betaB ~ dnorm(0, 100)
 betaC ~ dnorm(0, 100)
}'
model_r <- run.jags(model_rounded, data = with(df, list(Y=c(Y, rep(NA, nrow(df))), A=c(A,A), B=c(B,B), C=c(C,C), n=2*nrow(df))), inits=list(mu=0.45, sd=0.94, betaA=0.47, betaB=0.46, betaC=0), monitor=c("Y"), n.chains = 1, sample=1)

posterior_samples <- tail(n=10000, model_r$mcmc[[1]][1,])
posterior_predictive <- ifelse(posterior_samples>=3, 3, posterior_samples)
plot(df$Y, posterior_predictive)

modelPredictions_r <- data.frame(Y=c(df$Y, posterior_predictive), Real=c(rep(1, n), rep(0, n)), A=c(df$A, df$A), B=c(df$B, df$B), C=c(df$C, df$C))
r_r <- randomForest(as.ordered(Real) ~ Y + A + B + C, modelPredictions_r); r_r
#         OOB estimate of  error rate: 50.48%
# Confusion matrix:
#      0    1 class.error
# 0 4814 5186      0.5186
# 1 4909 5091      0.4909

model_rounded_censor <- 'model {
 for (i in 1:n) {
     roundA[i] ~ dround(A[i], 3)
     roundB[i] ~ dround(B[i], 3)
     roundC[i] ~ dround(C[i], 3)
     mean[i] <- mu + betaA*roundA[i] + betaB*roundB[i] + betaC*roundC[i]
     Y[i] ~ dnorm(mean[i], tau)
     is.censored[i] ~ dinterval(Y[i], c)
     }

 sd   ~ dgamma(0.01, 0.01)
 tau  <- 1/sqrt(sd)

 mu ~ dnorm(0, 100)
 betaA ~ dnorm(0, 100)
 betaB ~ dnorm(0, 100)
 betaC ~ dnorm(0, 100)
}'
model_r_c <- run.jags(model_rounded_censor, data = with(df, list(Y=c(Y, rep(NA, nrow(df))), A=c(A,A), B=c(B,B), C=c(C,C), n=2*nrow(df), is.censored=c(as.integer(Y==3), as.integer(Y==3)), c=3)), inits=list(mu=0.37, sd=1, betaA=0.42, betaB=0.40, betaC=0), monitor=c("Y"), n.chains = 1, adapt=0, burnin=500, sample=1)

posterior_samples <- tail(n=10000, model_r_c$mcmc[[1]][1,])
posterior_predictive <- ifelse(posterior_samples>=3, 3, posterior_samples)


modelPredictions_r_c <- data.frame(Y=c(df$Y, posterior_predictive), Real=c(rep(1, n), rep(0, n)), A=c(df$A, df$A), B=c(df$B, df$B), C=c(df$C, df$C))
r_r_c <- randomForest(as.ordered(Real) ~ Y + A + B + C, modelPredictions_r_c); r_r_c
#         OOB estimate of  error rate: 53.67%
# Confusion matrix:
#      0    1 class.error
# 0 4490 5510      0.5510
# 1 5224 4776      0.5224

model_rounded_censor_quadratic <- 'model {
 for (i in 1:n) {
     roundA[i] ~ dround(A[i], 3)
     roundB[i] ~ dround(B[i], 3)
     roundC[i] ~ dround(C[i], 3)
     mean[i] <- mu + betaA*roundA[i] + betaB*roundB[i] + betaC*roundC[i]^2
     Y[i] ~ dnorm(mean[i], tau)
     is.censored[i] ~ dinterval(Y[i], c)
     }

 sd   ~ dgamma(0.01, 0.01)
 tau  <- 1/sqrt(sd)

 mu ~ dnorm(0, 100)
 betaA ~ dnorm(0, 100)
 betaB ~ dnorm(0, 100)
 betaC ~ dnorm(0, 100)
}'

model_r_c_q <- run.jags(model_rounded_censor_quadratic, data = with(df, list(Y=c(Y, rep(NA, nrow(df))), A=c(A,A), B=c(B,B), C=c(C,C), n=2*nrow(df), is.censored=c(as.integer(Y==3), as.integer(Y==3)), c=3)), inits=list(mu=0.37, sd=1, betaA=0.42, betaB=0.40, betaC=0), monitor=c("Y"), n.chains = 1, adapt=0, burnin=500, sample=1)

posterior_samples <- tail(n=10000, model_r_c_q$mcmc[[1]][1,])
posterior_predictive <- ifelse(posterior_samples>=3, 3, posterior_samples)

modelPredictions_r_c_q <- data.frame(Y=c(df$Y, posterior_predictive), Real=c(rep(1, n), rep(0, n)), A=c(df$A, df$A), B=c(df$B, df$B), C=c(df$C, df$C))
r_r_c_q <- randomForest(as.ordered(Real) ~ Y + A + B + C, modelPredictions_r_c_q); r_r_c_q
#         OOB estimate of  error rate: 61.02%
# Confusion matrix:
#      0    1 class.error
# 0 3924 6076      0.6076
# 1 6127 3873      0.6127

trueNegatives <- modelPredictions_r_c_q[predict(r_r_c_q) == 0 & modelPredictions_r_c_q$Real == 0,]

Where can we go with this? The ML techniques can be used to rank existing Bayesian models in an effective if unprincipled way. Techniques which quantify uncertainty like Bayesian neural networks could give more effective feedback by highlighting the points the Bayesian NN is most certain are fake, guiding the analyst towards the worst-modeled datapoints and providing hints for improvement. More inspiration could be borrowed from the GAN literature, such as minibatch discrimination - as demonstrated above, the random forests only see one data point at a time, but in training GANs, it has proven useful to instead examine multiple datapoints at a time to encourage the generator to learn how to generate a wide variety of datapoints rather than modeling a few datapoints extremely well; a ML model which can predict multiple outputs simultaneously based on multiple inputs would be analogous (that is, instead of X ~ A + B + C, it would look more like X1 + X2 + X3 ... ~ A1 + B1 + C1 + A2 + B2 + C2 + ..., with the independent & dependent variables from multiple data points all fed in simultaneously as a single sample) and might be an even more effective model critic.

Proportion of Important Thinkers by Global Region Over Time in Charles Murray’s Human Accomplishment

Human Accomplishment is a 2003 book by Charles Murray reporting a large-scale citation analysis of biographical dictionaries & reference books on art/literature/science/mathematics/philosophy/science throughout history, quantifying the relative importance of significant individuals such as Isaac Newton or Immanuel Kant or Confucius and the temporal & geographical patterns; in particular, it demonstrates large European contributions throughout history and increasingly dramatically post-1400 AD. The dataset has been released.

Emil Kirkegaard created a visualization of of the proportion by rough geographic region (European/Asian/other) in R using ggplot2 and LOESS smoothing. Perhaps the most striking aspect of it is the Dark Ages showing up as a spike in Asian proportion. This visualization could be improved in a few ways:

• LOESS ignores the constraint that proportions must be 0-1 and naively extrapolates beyond the boundaries

• no visualization of uncertainty is provided, either in the form of graphing the raw data points by superimposing a scatterplot or by providing standard errors or credible intervals

  • LOESS can provide estimate local standard errors & confidence intervals but they are of questionable meaning in the absence of the underlying counts
• alternatively, the distribution of significant figures may not be treated correctly parametrically

• proportions may reflect a time-series with trends and so precision is overstated

These points can be addressed by:

1. switching from a LOESS plot to either splines or local binomial regressions
2. plotting the raw proportions grouped by decade or century
3. using a nonparametric bootstrap to calculate confidence intervals, a procedure which lends itself to visualization as an animation of plots of all the resamples, giving an intuitive sense of how important sampling error is to the overall pattern of curves and specific parts of history
4. alternately, instead of attempting to fit the proportion, one can fit the original count of significant figures in a binomial or log-normal Bayesian time-series model and sample from the posterior estimates of each region for each decade/century, and calculate posterior proportions, gaining full quantification of uncertainty, incorporation of any autocorrelation, and smoothing

I didn’t realize Kirkegaard’s R code was available so I wound up redoing it myself (and getting the same results):

## export CSV from spreadsheet in https://osf.io/z9cnk/
h <- read.csv("HA.csv", header=TRUE)
summary(h)
#      Serial                        Name            Fl               Birth              Death              Inventory     ScienceField
#  Min.   :   11.00   Descartes, René  :   4   Min.   :-700.000   Min.   :-640.000   Min.   :-559.00   Science   :1442          :2560
#  1st Qu.: 6144.50   Hooke, Robert    :   4   1st Qu.:1557.250   1st Qu.:1580.000   1st Qu.:1638.00   Lit.West  : 835   Tech   : 239
#  Median :12534.50   Leonardo da Vinci:   4   Median :1804.000   Median :1782.000   Median :1844.00   Music.West: 522   Phys   : 218
#  Mean   :15994.27   Archimedes       :   3   Mean   :1585.638   Mean   :1616.174   Mean   :1682.81   Art.West  : 479   Chem   : 204
#  3rd Qu.:21999.75   Bacon, Francis   :   3   3rd Qu.:1900.000   3rd Qu.:1863.000   3rd Qu.:1930.00   Phil.West : 155   Biol   : 193
#  Max.   :43134.00   d’Alembert, Jean :   3   Max.   :1949.000   Max.   :1910.000   Max.   :1997.00   Art.China : 111   Math   : 191
#                     (Other)          :3981                      NA's   :304        NA's   :351       (Other)   : 458   (Other): 397
#      Index             Duplicate           BirthCountry   WorkCountry      Ethnicity        Woman            No..of.Inventories
#  Min.   :  0.60000   Min.   :0.00000000   France : 564   France : 605   Germanic: 592   Min.   :0.00000000   Min.   :2.000000
#  1st Qu.:  3.54000   1st Qu.:0.00000000   Germany: 556   Britain: 574   French  : 565   1st Qu.:0.00000000   1st Qu.:2.000000
#  Median :  7.60000   Median :0.00000000   Britain: 554   Germany: 525   English : 441   Median :0.00000000   Median :2.000000
#  Mean   : 12.95713   Mean   :0.06221889   Italy  : 400   Italy  : 406   Italian : 397   Mean   :0.02198901   Mean   :2.228916
#  3rd Qu.: 15.89000   3rd Qu.:0.00000000   USA    : 306   USA    : 375   USA     : 276   3rd Qu.:0.00000000   3rd Qu.:2.000000
#  Max.   :100.00000   Max.   :1.00000000   China  : 239   China  : 239   Chinese : 240   Max.   :1.00000000   Max.   :4.000000
#  NA's   :115                              (Other):1383   (Other):1278   (Other) :1491                        NA's   :3753
levels(h$Ethnicity)
#  [1] "Ancient Greek" "Ancient Roman" "Arabic"        "Australian"    "Basque"        "Black"         "Bulgarian"     "Canadian"
#  [9] "Chinese"       "Croatian"      "Czech"         "Danish"        "Dutch"         "English"       "Estonian"      "Finnish"
# [17] "Flemish"       "French"        "Germanic"      "Greek"         "Hungarian"     "Icelandic"     "Indian"        "Irish"
# [25] "Italian"       "Japanese"      "Jewish"        "Latino"        "New Zealand"   "Norwegian"     "Polish"        "Portuguese"
# [33] "Romanian"      "Scots"         "Slavic"        "Slovenian"     "Spanish"       "Swedish"       "Swiss"         "USA"

european <- c("Ancient Greek", "Ancient Roman", "Australian", "Basque", "Bulgarian", "Canadian", "Croatian", "Czech", "Danish",
    "Dutch", "English", "Estonian", "Finnish", "Flemish", "French", "Germanic", "Greek", "Hungarian", "Icelandic", "Irish",
    "Italian", "Jewish", "New Zealand", "Norwegian", "Polish", "Portuguese", "Romanian", "Scots", "Slavic", "Slovenian",
    "Spanish", "Swedish", "Swiss", "USA")
asian    <- c("Chinese", "Indian", "Japanese")
other    <- c("Arabic", "Black", "Latino")
groupMembership <- function(e) { if (e %in% european) { "European" } else { if (e %in% asian) { "Asian" } else { "Other" } } }
h$Group <- as.factor(sapply(h$Ethnicity, groupMembership))
 summary(h$Group)
#   Asian European    Other
#     507     3379      116

## We use 'Fl' (floruit/flourished), when a person is believed to have done their most important work,
## since birth/death is often unavailable.
## group to decades by rounding:
h$Fl.decade <- round(h$Fl, digits=-1)
hd <- subset(select=c(Fl.decade, Group), h)

hdcount <- aggregate(cbind(Group) ~ Fl.decade+Group, length, data=hd)
colnames(hdcount)[3] <- "Count"
## sort by time:
hdcount <- hdcount[order(hdcount$Fl.decade),]
nrow(h); sum(hdcount$Count)
# [1] 4002
# [1] 4002
head(hdcount, n=20)
#     Fl.decade    Group Count
# 178      -700 European     3
# 179      -680 European     1
# 180      -650 European     1
# 1        -600    Asian     2
# 181      -600 European     2
# 182      -580 European     2
# 183      -570 European     2
# 2        -550    Asian     1
# 184      -550 European     1
# 185      -540 European     5
# 3        -520    Asian     1
# 186      -520 European     3
# 4        -510    Asian     1
# 187      -510 European     2
# 188      -500 European     2
# 189      -480 European     6
# 190      -460 European     3
# 191      -450 European     7
# 5        -440    Asian     1
# 192      -440 European    11

## One issue with the count data: decades with zero significant figures from a group
## (which happens frequently) get suppressed. Some tools can handle the omission
## automatically but many cannot, so we need to manually insert any missing decades with '0'
decades <- seq(-700, 1950, by=10)
for (i in 1:length(decades)) {
    d <- decades[i]
    if (nrow(hdcount[hdcount$Fl.decade==d & hdcount$Group=="European",])==0) {
        hdcount <- rbind(hdcount, data.frame(Fl.decade=d, Group="European", Count=0))}
    if (nrow(hdcount[hdcount$Fl.decade==d & hdcount$Group=="Asian",])==0) {
        hdcount <- rbind(hdcount, data.frame(Fl.decade=d, Group="Asian", Count=0))}
    if (nrow(hdcount[hdcount$Fl.decade==d & hdcount$Group=="Other",])==0) {
        hdcount <- rbind(hdcount, data.frame(Fl.decade=d, Group="Other", Count=0))}
    }
hdcount <- hdcount[order(hdcount$Fl.decade),]

library(ggplot2); library(gridExtra)
c1 <- with(hdcount, qplot(Fl.decade, Count, color=Group) + stat_smooth())
c2 <- with(hdcount, qplot(Fl.decade, log1p(Count), color=Group) + stat_smooth())
grid.arrange(c1, c2, ncol=1)

The growth in human population and accomplishment post-1400 is so dramatic that it obscures earlier temporal variations:

Log-transformed, we can still see the inverted-V shape of European counts, but it’s somewhat subtle and does leave room for doubt about sampling error. Moving on to reproducing the proportions plot:

## Create proportions by summing per decade, then looping over each group & dividing by total for that decade:
decadeTotals <- aggregate(Count ~ Fl.decade, sum, data=hdcount)
for (i in 1:nrow(hdcount)) {
        total <- decadeTotals[decadeTotals$Fl.decade == hdcount[i,]$Fl.decade,]$Count
        p <- hdcount[i,]$Count / total
        hdcount$Proportion[i] <- if(is.nan(p)) { 0 } else { p }
        hdcount$Total[i] <- total
}
with(hdcount, qplot(Fl.decade, Proportion, color=Group) + stat_smooth() + coord_cartesian(ylim = c(0, 1)))

We successfully reproduce it, modulo the LOESS standard errors (which can be disabled by adding se=FALSE to stat_smooth()), including the unwanted nonsensical extrapolations. It is possible with some tricky ggplot2 functionality to add in binomial smoothing (along with some jitter to unbunch the datapoints at the modal 0).

## roughly equivalent to:
# glm(cbind(Count,Total) ~ splines::ns(Fl.decade,3), family="binomial", data=hdcount, subset=Group=="European")
binomial_smooth <- function(...) { geom_smooth(se=FALSE, method = "glm", method.args = list(family = "binomial"), ...) }
with(hdcount, qplot(Fl.decade, Proportion, color=Group) +
    binomial_smooth(formula = y ~ splines::ns(x, 3)) +
    geom_jitter(aes(color=Group), width=0.013,, height=0.013))

This still doesn’t provide any indication of sampling error uncertainty, however. Kirkegaard provides one with CIs derived from bootstrapping, so I will provide something a little different: visualizing the uncertainty dynamically by graphing the smoothed proportions for each resample in an animation of hundreds of bootstrap samples.

So to do this bootstrap, we package up the various transformations from before, so we can sample-with-replacement the original dataset¹⁴, transform, and plot repeatedly:

transformAndProportion <- function(df) {
    df$Fl.decade <- round(df$Fl, digits=-1)
    dfd <- subset(select=c(Fl.decade, Group), df)
    dfdcount <- aggregate(cbind(Group) ~ Fl.decade+Group, length, data=dfd)
    colnames(dfdcount)[3] <- "Count"
    decades <- seq(-700, 1950, by=10)
    for (i in 1:length(decades)) {
        d <- decades[i]
        if (nrow(dfdcount[dfdcount$Fl.decade==d & dfdcount$Group=="European",])==0) {
    dfdcount <- rbind(dfdcount, data.frame(Fl.decade=d, Group="European", Count=0))}
        if (nrow(dfdcount[dfdcount$Fl.decade==d & dfdcount$Group=="Asian",])==0) {
    dfdcount <- rbind(dfdcount, data.frame(Fl.decade=d, Group="Asian", Count=0))}
        if (nrow(dfdcount[dfdcount$Fl.decade==d & dfdcount$Group=="Other",])==0) {
    dfdcount <- rbind(dfdcount, data.frame(Fl.decade=d, Group="Other", Count=0))}
    }
    dfdcount <- dfdcount[order(dfdcount$Fl.decade),]
    decadeTotals <- aggregate(Count ~ Fl.decade, sum, data=dfdcount)
    for (i in 1:nrow(dfdcount)) {
        p <- dfdcount[i,]$Count / decadeTotals[decadeTotals$Fl.decade == dfdcount[i,]$Fl.decade,]$Count
        dfdcount$Proportion[i] <- if(is.nan(p)) { 0 } else { p }
    }
    return(dfdcount)
    }

bootPlot <- function(df) {
    n <- nrow(df)
    bootDf <- df[sample(1:n, n, replace=TRUE),]
    bootDfdcount <- transformAndProportion(bootDf)
    ## WARNING: can't just call qplot due to animation/ggplot2 bug; have to assign & 'print'
    p <- with(bootDfdcount, qplot(Fl.decade, Proportion, color=Group) +
        binomial_smooth(formula = y ~ splines::ns(x, 3)) +
        geom_jitter(aes(color=Group), width=0.013,, height=0.013))
    print(p)
    }
library(animation)
saveGIF({for (i in 1:200) { bootPlot(h) }}, interval=0.15, ani.width=1300, ani.height=700,
    movie.name="2003-murray-humanaccomplishment-region-proportions-bootstrap.gif", clean=FALSE)

The bootstrap animation suggests to me that while the very earliest time-periods are opaque and the Dark Ages difference between Europe & Asia may be somewhat higher or lower, the overall shape doesn’t change much.

efit <- auto.arima(subset(hdcount, select=c("Fl.decade", "Count"), Group=="European")$Count)
afit <- auto.arima(subset(hdcount, select=c("Fl.decade", "Count"), Group=="Asian")$Count)
ofit <- auto.arima(subset(hdcount, select=c("Fl.decade", "Count"), Group=="Other")$Count)
par(mfrow=c(3,1))
plot(forecast(efit), ylim=c(0,200)); axis(side=1, labels=decades, at=seq(1, length(decades)))
plot(forecast(afit), ylim=c(0,200)); axis(side=1, labels=decades, at=seq(1, length(decades)))
plot(forecast(ofit), ylim=c(0,200)); axis(side=1, labels=decades, at=seq(1, length(decades)))
# https://imgur.com/a/qPL6a

Program for non-spaced-repetition review of past written materials for serendipity & rediscovery: Archive Revisiter

One reason to take notes/clippings and leave comments in stimulating discussions is to later benefit by having references & citations at hand, and gradually build up an idea from disparate threads and make new connections between them. For this purpose, I make extensive excerpts from web pages & documents I read into my Evernote clippings (functioning as a commonplace book), and I comment constantly on Reddit/LessWrong/#lesswrong etc. While expensive in time & effort, I often go back, months or years later, and search for a particular thing and expand & integrate it into another writing or expand it out to an entire essay of its own. (I also value highly not being in the situation where I believe something but I do not know why I believe it other than the conviction I read it somewhere, once.)

This sort of personal information management using simple personal information managers like Evernote works well enough when I have a clear memory of what the citation/factoid was, perhaps because it was so memorable, or when the citations or comments are in a nice cluster (perhaps because there was a key phrase in them or I kept going back & expanding a comment), but it loses out on key benefits to this procedure: serendipity and perspective.

As time passes, one may realize the importance of an odd tidbit or have utterly forgotten something or events considerably changed its meaning; in this case, you would benefit from revisiting & rereading that old bit & experiencing an aha! moment, but you don’t realize it. So one thing you could do is reread all your old clippings & comments, appraising them for reuse.

But how often? And it’s a pain to do so. And how do you keep track of which you’ve already read? One thing I do for my emails is semi-annually I (try to) read through my previous 6 months of email to see what might need to be followed up on¹⁵ or mined for inclusion in an article. (For example, an ignored request for data, or a discussion of darknet markets with a journalist I could excerpt into one of my DNM articles so I can point future journalists at that instead.) This is already difficult, and it would be even harder to expand. I have read through my LessWrong comment history… once. Years ago. It would be more difficult now.

Simply re-reading periodically in big blocks may work but is suboptimal: there is no interface easily set up to reread them in small chunks over time, no constraints which avoid far too many reads, nor is there any way to remove individual items which you are certain need never be reviewed again. Reviewing is useful but can be an indefinite timesink. (My sent emails are not too hard to review in 6-month chunks, but my IRC logs are bad - 7,182,361 words in #lesswrong alone - and my >38k Evernote clippings are worse; any lifestreaming will exacerbate the problem by orders of magnitude.) This is probably one reason that people who keep journals or diaries don’t reread Nor can it be crowdsourced or done by simply ranking comments by public upvotes (in the case of Reddit/LW/HN comments), because the most popular comments are ones you likely remember well & have already used up, and the oddities & serendipities you are hoping for are likely unrecognizable to outsiders.

This suggests some sort of reviewing framework where one systematically reviews old items (sent emails, comments, IRC logs by oneself), putting in a constant amount of time regularly and using some sort of ever expanding interval between re-reads as an item becomes exhausted & ever more likely to not be helpful. Similar to the logarithmically-bounded number of backups required for indefinite survival of data (Sandberg & Armstrong 2012), Deconstructing Deathism - Answering Objections to Immortality, Mike Perry 2013 (note: this is an entirely different kind of problem than those considered in Freeman Dyson’s immortal intelligences in Infinite in All Directions, which are more fundamental), discusses something like what I have in mind in terms of an immortal agent trying to review its memories & maintain a sense of continuity, pointing out that if time is allocated correctly, it will not consume 100% of the agent’s time but can be set to consume some bounded fraction:

It seems reasonable that past versions of the self would survive as we remember the events of times past, that is to say, our episodic memories, and this would have importance in our continuing to persist as what could be considered the same albeit also a changing, developing person. But in addition to this mnemonic reinforcement I imagine there would be a more general feeling of being a particular individual, an ambiance derived from but not referring to any specific past experiences. Ambiance alone would not be sufficient, I think, to make us who we are; episodic memories would also be necessary, yet it could considerably lessen the need for frequent recall and thus alleviate the problem of dilution.

Another interesting thought is that certain items might consistently be consulted more frequently than others. (Indeed, would this not be expected?) In this way it would actually be possible to bypass the dilution effect and instead allow a fixed fraction of time for perusal of any given item, even as more items were added indefinitely. A simple way of doing this could be first to allow some fixed fraction of the time for day-to-day affairs and other non-archival work (prime time), and spend the rest of the time on perusal of personal archives (archive time). The exact apportioning of prime versus archive time is not important here, but it will be instructive to consider how the archive time itself might be subdivided. A simple, if overly simplistic, strategy would be to have half this time devoted to the first century’s records, half the remainder to the second century, and so on. (Since there would only be a finite number of centuries, there would be some unused archive time at the end, which could be spent as desired. Note, however, that in the limit of infinite total time covering infinitely many centuries, the usage of archive time would approach but not exceed 100%.) In this way, then, there would be a fixed fraction of archive time, $2-n$, spent on the _n_th century’s records, regardless of how many centuries beyond the nth were lived or how many records accumulated. True, this way of apportioning time might not be much good beyond a few centuries; only about one trillionth the total time would be spent on the 40th century, for instance, around $\frac{1}{300}$ sec per 100 years. (Possibly a lot could be covered even in this brief interval of about 3 million nanoseconds, however.) But the apportionment scheme could be adjusted.

A more interesting and plausible, if slightly harder-to-describe scheme would be to choose a constant $c>0$ and allow the fraction $c \cdot (\frac{1}{n+c-1} - \frac{1}{n+c})$ to the _n_th-century records. It is easy to show that the time for all centuries will add up to 100% as before, whatever positive value of c we start with. Starting with $c=10$ will get 10% of the total time spent on the first century, with subsequent centuries receiving a diminishing share as before, but the rate of falloff will be much slower, so that the 40th century will still receive 0.4%, or about 5 months per 100 years, that is to say, 240 million nanoseconds per minute. If we suppose that our immortal settles eventually into a routine in which 10% of the time overall is archive time, there would be 24 million nanoseconds available each minute of life for the 40th century’s memories alone, if desired, with many other centuries getting more or less comparable or greater amounts of attention, and none omitted entirely. This, I think, makes at least a plausible case that a reasonable sense of one’s personal identity could be sustained indefinitely.

In the above examples the greatest proportion of archive time falls to the earlier records, which might be fitting since these should be the most important as formative years for the prospective immortal, thus the most important for identity maintenance. (Memory recall would also naturally occur during prime time; the emphasis here could be on recent events, to maintain a balance overall.) In summary, then, we have considered ways that the problem of dilution might be successfully managed. Relatively infrequent perusal of memories might still suffice to maintain the necessary continuity with past versions of the self, or proper scheduling could stabilize the frequency of recall and bypass the dilution effect, or both. We see in any case that the problem is not what it may seem at first sight. We have no guarantee, of course, that it would not get out of bounds, but certainly some grounds for hope.

So you could imagine some sort of software along the lines of spaced repetition systems like Anki/Mnemosyne/Supermemo which you spend, say, 10 minutes a day at, simply rereading a selection of old emails you sent, lines from IRC with n lines of surrounding context, Reddit & LW comments etc; with an appropriate backoff & time-curve, you would reread each item maybe 3 times in your lifetime (eg first after a delay of a month, then a year or two, then decades). Each item could come with a rating function where the user rates it as an important or odd-seeming or incomplete item and to be exposed again in a few years, or as totally irrelevant and not to be shown again - as for many bits of idle chit-chat, mundane emails, or intemperate comments is not an instant too soon! (More positively, anything already incorporated into an essay or otherwise reused likely doesn’t need to be resurfaced.)

This wouldn’t be the same as a spaced repetition system which is designed to recall an item as many times as necessary, at the brink of forgetting, to ensure you memorize it; in this case, the forgetting curve & memorization are irrelevant and indeed, the priority here is to try to eliminate as many irrelevant or useless items as possible from showing up again so that the review doesn’t waste time.

More specifically, you could imagine an interface somewhat like Mutt which reads in a list of email files (my local POP email archives downloaded from Gmail with getmail4, filename IDs), chunks of IRC dialogue (a grep of my IRC logs producing lines written by me +- 10 lines for context, hashes for ID), LW/Reddit comments downloaded by either scraping or API via the BigQuery copy up to 2015, and stores IDs, review dates, and scores in a database. One would use it much like a SRS system, reading individual items for 10 or 20 minutes, and rating them, say, upvote (this could be useful someday, show me this ahead of schedule in the future) / downvote (push this far off into the future) / delete (never show again). Items would appear on an expanding schedule. For example if one wanted to review items 4 times over the next 50 years (roughly my life expectancy), a schedule might be:

round({t=0:4; t^6.981})
# [1]     0     1   126  2142 15958

So in 1 day, then a third of a year, then after 5.8 years, then after 43 years. Alternately, a geometric series might be a bit kinder and not too front-loaded:

review <- function(n, r, a) { a * (1 - r^n) / (1 - r) }
reviews <- function(n, r, a) { sapply(1:n, function(nn) { review(nn, r, a) }) }
findR <- function (firstReview=31, n_total=3, years=50) {  optimize(interval=c(0, 1000),
    f = function(r) { abs(sum(sapply(1:n_total,
         function(n){review(n, a=firstReview, r=r)})) - (365*years)) })$minimum }
findR(firstReview=30, n_total=4, years=50)
# [1] 7.728823216
round(reviews(4, 7.728823216, 30))
# [1]    30   262  2054 15904

The geometric series allows for easy incorporation of rating modifications: a downvote penalty might multiply r by 1.5, vs 0.5 for upvotes. This would also allow some input from statistical algorithms which predict upvote/downvote/delete and advances/delays items based on that, which would hopefully quickly learn to avoid idle chit-chat and short performative utterances and start to prioritize more interesting & unusual items. (For example, a good start might be a SVM on a bag-of-words version of each item’s text, and then as the dataset ratings expand, more complicated algorithms could be plugged in.)

As far as I know, some to-do/self-help systems have something like a periodic review of past stuff, and as I mentioned, spaced repetition systems do something somewhat similar to this idea of exponential revisits, but there’s nothing like this at the moment.

On the value of new statistical methods

Genetic correlation research is a hot area in 2016-2017: my WP article passed 400 references in May 2017. What is particularly interesting reference-wise is that publications 2015-2017 make up around half of the results: so more genetic correlations calculated in the past 3 years than in the previous 80 years since first estimates were made somewhere in the 1930s or so.

For calculating them, there are 3 main methods.

1. twin registry studies require twin phenotypic measurements which can usually be collected by mailed surveys and to analyze them one computes some Pearson’s r or uses a standard SEM with additional covariance paths (doable with Wright’s path analysis back in the 1930s by hand), scaling roughly linearly with sample size, having excellent statistical power at a few hundred twin pairs and capturing full heritabilities
2. for GCTA, one requires full raw SNP data on 5000+ unrelated individuals at $100+ a sample, along with simultaneous phenotypic measurements of both traits and must use complicated custom software whose computation scales exponentially and can only examine a narrow subset of heritability
3. for LDSC, one requires public summary polygenic scores but they can be from separate GWASes and calculated on traits individually, and the computational complexity is closer to linear than exponential; the penalty for not needing raw SNP data from twice-measured individuals is that SNP costs double or more since multiple GWASes are used, and LDSC even more inefficient than GCTA, so you’ll need >10,000 individuals used in each polygenic score, and still need custom software.

In other words, the twin method is old, simple, requires small sample sizes, and easily obtained phenotypic measurements; while GCTA/LDSC is new, complicated, and requires expensive novel genetic sequencing data in huge sample sizes as well as the phenotypic measurements. So naturally LDSC gets used an order of magnitude more! Looking at the bibliography, we can guesstimate the rates at twin: 1 paper/year; GCTA (requiring raw data), 10/year; LDSC (public summary stats), 100/year.

Amazing the difference methods can make. It’s all about data access. For all its disadvantages, LDSC statistically works around the lack of individual-level raw data and makes do with the data that gets publicly released because it is not seen to violate privacy or bioethics, so any researcher can make use of the method on their particular dataset, while twin and GCTA require individual-level data which is jealously guarded by the owners.

Methodologists and statisticians are probably seriously undervalued: a good new method can cause a revolution.