nhaliday + priors-posteriors   57

Darwinian medicine - Randolph Nesse
The Dawn of Darwinian Medicine: https://sci-hub.tw/https://www.jstor.org/stable/2830330
TABLE 1 Examples of the use of the theory of natural selection to predict the existence of phenomena otherwise unsuspected
TABLE 2 A classification of phenomena associated with infectious disease
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november 2017 by nhaliday
trees are harlequins, words are harlequins — bayes: a kinda-sorta masterpost
lol, gwern: https://www.reddit.com/r/slatestarcodex/comments/6ghsxf/biweekly_rational_feed/diqr0rq/
> What sort of person thinks “oh yeah, my beliefs about these coefficients correspond to a Gaussian with variance 2.5″? And what if I do cross-validation, like I always do, and find that variance 200 works better for the problem? Was the other person wrong? But how could they have known?
> ...Even ignoring the mode vs. mean issue, I have never met anyone who could tell whether their beliefs were normally distributed vs. Laplace distributed. Have you?
I must have spent too much time in Bayesland because both those strike me as very easy and I often think them! My beliefs usually are Laplace distributed when it comes to things like genetics (it makes me very sad to see GWASes with flat priors), and my Gaussian coefficients are actually a variance of 0.70 (assuming standardized variables w.l.o.g.) as is consistent with field-wide meta-analyses indicating that d>1 is pretty rare.
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august 2017 by nhaliday
Stat 260/CS 294: Bayesian Modeling and Inference
- Priors (conjugate, noninformative, reference)
- Hierarchical models, spatial models, longitudinal models, dynamic models, survival models
- Testing
- Model choice
- Inference (importance sampling, MCMC, sequential Monte Carlo)
- Nonparametric models (Dirichlet processes, Gaussian processes, neutral-to-the-right processes, completely random measures)
- Decision theory and frequentist perspectives (complete class theorems, consistency, empirical Bayes)
- Experimental design
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july 2017 by nhaliday
To err is human; so is the failure to admit it
Lowering the cost of admitting error could help defuse these crises. A new issue of Econ Journal Watch, an online journal, includes a symposium in which prominent economic thinkers are asked to provide their “most regretted statements”. Held regularly, such exercises might take the shame out of changing your mind. Yet the symposium also shows how hard it is for scholars to grapple with intellectual regret. Some contributions are candid; Tyler Cowen’s analysis of how and why he underestimated the risk of financial crisis in 2007 is enlightening. But some disappoint, picking out regrets that cast the writer in a flattering light or using the opportunity to shift blame.
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june 2017 by nhaliday
Why we should love null results – The 100% CI
This is a must-read blog for many reasons, but biggest is: it REALLY matters if a hypothesis is likely to be true.
Strikes me that the areas of psychology with the most absurd hypotheses (ones least likely to be true) *AHEMSOCIALPRIMINGAHEM* are also...
...the ones with extremely small sample sizes. So this already-scary graph from the blogpost becomes all the more terrifying:
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june 2017 by nhaliday
Logic | West Hunter
All the time I hear some public figure saying that if we ban or allow X, then logically we have to ban or allow Y, even though there are obvious practical reasons for X and obvious practical reasons against Y.

No, we don’t.


compare: https://pinboard.in/u:nhaliday/b:190b299cf04a

Small Change Good, Big Change Bad?: https://www.overcomingbias.com/2018/02/small-change-good-big-change-bad.html
And on reflection it occurs to me that this is actually THE standard debate about change: some see small changes and either like them or aren’t bothered enough to advocate what it would take to reverse them, while others imagine such trends continuing long enough to result in very large and disturbing changes, and then suggest stronger responses.

For example, on increased immigration some point to the many concrete benefits immigrants now provide. Others imagine that large cumulative immigration eventually results in big changes in culture and political equilibria. On fertility, some wonder if civilization can survive in the long run with declining population, while others point out that population should rise for many decades, and few endorse the policies needed to greatly increase fertility. On genetic modification of humans, some ask why not let doctors correct obvious defects, while others imagine parents eventually editing kid genes mainly to max kid career potential. On oil some say that we should start preparing for the fact that we will eventually run out, while others say that we keep finding new reserves to replace the ones we use.


If we consider any parameter, such as typical degree of mind wandering, we are unlikely to see the current value as exactly optimal. So if we give people the benefit of the doubt to make local changes in their interest, we may accept that this may result in a recent net total change we don’t like. We may figure this is the price we pay to get other things we value more, and we we know that it can be very expensive to limit choices severely.

But even though we don’t see the current value as optimal, we also usually see the optimal value as not terribly far from the current value. So if we can imagine current changes as part of a long term trend that eventually produces very large changes, we can become more alarmed and willing to restrict current changes. The key question is: when is that a reasonable response?

First, big concerns about big long term changes only make sense if one actually cares a lot about the long run. Given the usual high rates of return on investment, it is cheap to buy influence on the long term, compared to influence on the short term. Yet few actually devote much of their income to long term investments. This raises doubts about the sincerity of expressed long term concerns.

Second, in our simplest models of the world good local choices also produce good long term choices. So if we presume good local choices, bad long term outcomes require non-simple elements, such as coordination, commitment, or myopia problems. Of course many such problems do exist. Even so, someone who claims to see a long term problem should be expected to identify specifically which such complexities they see at play. It shouldn’t be sufficient to just point to the possibility of such problems.


Fourth, many more processes and factors limit big changes, compared to small changes. For example, in software small changes are often trivial, while larger changes are nearly impossible, at least without starting again from scratch. Similarly, modest changes in mind wandering can be accomplished with minor attitude and habit changes, while extreme changes may require big brain restructuring, which is much harder because brains are complex and opaque. Recent changes in market structure may reduce the number of firms in each industry, but that doesn’t make it remotely plausible that one firm will eventually take over the entire economy. Projections of small changes into large changes need to consider the possibility of many such factors limiting large changes.

Fifth, while it can be reasonably safe to identify short term changes empirically, the longer term a forecast the more one needs to rely on theory, and the more different areas of expertise one must consider when constructing a relevant model of the situation. Beware a mere empirical projection into the long run, or a theory-based projection that relies on theories in only one area.

We should very much be open to the possibility of big bad long term changes, even in areas where we are okay with short term changes, or at least reluctant to sufficiently resist them. But we should also try to hold those who argue for the existence of such problems to relatively high standards. Their analysis should be about future times that we actually care about, and can at least roughly foresee. It should be based on our best theories of relevant subjects, and it should consider the possibility of factors that limit larger changes.

And instead of suggesting big ways to counter short term changes that might lead to long term problems, it is often better to identify markers to warn of larger problems. Then instead of acting in big ways now, we can make sure to track these warning markers, and ready ourselves to act more strongly if they appear.

Growth Is Change. So Is Death.: https://www.overcomingbias.com/2018/03/growth-is-change-so-is-death.html
I see the same pattern when people consider long term futures. People can be quite philosophical about the extinction of humanity, as long as this is due to natural causes. Every species dies; why should humans be different? And few get bothered by humans making modest small-scale short-term modifications to their own lives or environment. We are mostly okay with people using umbrellas when it rains, moving to new towns to take new jobs, etc., digging a flood ditch after our yard floods, and so on. And the net social effect of many small changes is technological progress, economic growth, new fashions, and new social attitudes, all of which we tend to endorse in the short run.

Even regarding big human-caused changes, most don’t worry if changes happen far enough in the future. Few actually care much about the future past the lives of people they’ll meet in their own life. But for changes that happen within someone’s time horizon of caring, the bigger that changes get, and the longer they are expected to last, the more that people worry. And when we get to huge changes, such as taking apart the sun, a population of trillions, lifetimes of millennia, massive genetic modification of humans, robots replacing people, a complete loss of privacy, or revolutions in social attitudes, few are blasé, and most are quite wary.

This differing attitude regarding small local changes versus large global changes makes sense for parameters that tend to revert back to a mean. Extreme values then do justify extra caution, while changes within the usual range don’t merit much notice, and can be safely left to local choice. But many parameters of our world do not mostly revert back to a mean. They drift long distances over long times, in hard to predict ways that can be reasonably modeled as a basic trend plus a random walk.

This different attitude can also make sense for parameters that have two or more very different causes of change, one which creates frequent small changes, and another which creates rare huge changes. (Or perhaps a continuum between such extremes.) If larger sudden changes tend to cause more problems, it can make sense to be more wary of them. However, for most parameters most change results from many small changes, and even then many are quite wary of this accumulating into big change.

For people with a sharp time horizon of caring, they should be more wary of long-drifting parameters the larger the changes that would happen within their horizon time. This perspective predicts that the people who are most wary of big future changes are those with the longest time horizons, and who more expect lumpier change processes. This prediction doesn’t seem to fit well with my experience, however.

Those who most worry about big long term changes usually seem okay with small short term changes. Even when they accept that most change is small and that it accumulates into big change. This seems incoherent to me. It seems like many other near versus far incoherences, like expecting things to be simpler when you are far away from them, and more complex when you are closer. You should either become more wary of short term changes, knowing that this is how big longer term change happens, or you should be more okay with big long term change, seeing that as the legitimate result of the small short term changes you accept.

The point here is the gradual shifts of in-group beliefs are both natural and no big deal. Humans are built to readily do this, and forget they do this. But ultimately it is not a worry or concern.

But radical shifts that are big, whether near or far, portend strife and conflict. Either between groups or within them. If the shift is big enough, our intuition tells us our in-group will be in a fight. Alarms go off.
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may 2017 by nhaliday
[1705.03394] That is not dead which can eternal lie: the aestivation hypothesis for resolving Fermi's paradox
If a civilization wants to maximize computation it appears rational to aestivate until the far future in order to exploit the low temperature environment: this can produce a 10^30 multiplier of achievable computation. We hence suggest the "aestivation hypothesis": the reason we are not observing manifestations of alien civilizations is that they are currently (mostly) inactive, patiently waiting for future cosmic eras. This paper analyzes the assumptions going into the hypothesis and how physical law and observational evidence constrain the motivations of aliens compatible with the hypothesis.


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

Life Versus Dark Energy: How An Advanced Civilization Could Resist the Accelerating Expansion of the Universe: https://arxiv.org/abs/1806.05203
The presence of dark energy in our universe is causing space to expand at an accelerating rate. As a result, over the next approximately 100 billion years, all stars residing beyond the Local Group will fall beyond the cosmic horizon and become not only unobservable, but entirely inaccessible, thus limiting how much energy could one day be extracted from them. Here, we consider the likely response of a highly advanced civilization to this situation. In particular, we argue that in order to maximize its access to useable energy, a sufficiently advanced civilization would chose to expand rapidly outward, build Dyson Spheres or similar structures around encountered stars, and use the energy that is harnessed to accelerate those stars away from the approaching horizon and toward the center of the civilization. We find that such efforts will be most effective for stars with masses in the range of M∼(0.2−1)M⊙, and could lead to the harvesting of stars within a region extending out to several tens of Mpc in radius, potentially increasing the total amount of energy that is available to a future civilization by a factor of several thousand. We also discuss the observable signatures of a civilization elsewhere in the universe that is currently in this state of stellar harvesting.
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may 2017 by nhaliday
Reversal of Fortune | West Hunter
“particularly in the fetus”. You’d think so, but people have looked at Dutch draftees who were in the womb during the famine of 1944. They found no effects of famine exposure on Ravens scores at age 19. Schizophrenia doubled, though. Schiz also doubled in the Chinese cohort exposed to the Great Leap Forward famine.

Cohort Profile: The Dutch Hunger Winter Families Study: https://academic.oup.com/ije/article/36/6/1196/814573
Nutrition and Mental Performance: https://sci-hub.bz/10.1126/science.178.4062.708
Schizophrenia after prenatal exposure to the Dutch Hunger Winter of 1944-1945: https://sci-hub.bz/10.1001/archpsyc.1992.01820120071010
Prenatal famine exposure and cognition at age 59 years: https://sci-hub.bz/10.1093/ije/dyq261

You might be right. There is reason to suspect that prenatal exposure to alcohol is far riskier in some populations than others – in particular populations that have limited historical exposure to alcohol. Fetal Alcohol Syndrome (FAS) is very rare in France, for example – yet they drink, I’m told.

The kind of conservatism that shows up politically doesn’t have any predictive value. In other words, liars and morons. They’re why God made baseball bats. Once upon a time, I said this: “The American right doesn’t have room for anyone who knows jack shit about anything, or whose predictions have ever come true.” I’ll stick with that.

full quote here: http://www.rpgcodex.net/forums/index.php?threads/planescape-torment-problems.9208/
The American right doesn't have room for anyone who knows jack shit about anything, or whose predictions have ever come true. Of course they're all liars. In the words of one of their semi-prominent members, himself plenty despicable: "Science, logic, rational inquiry, thoughtful reflection, mean nothing to them. It's all posturing and moral status games and sucking up to halfwits like GWB and clinging to crackpot religion, and of course amoral careerism. " I think my correspondent forgot to mention their propensity for eating shit and rolling around in their own vomit, but nobody's perfect.

I’ve mused that it’s generally believed that iodine benefits females more than males, and the timing of iodization in the US matches up reasonably well with the rise of feminism…
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may 2017 by nhaliday
Faces in the Clouds | West Hunter
This was a typical Iraq story: somehow, we had developed an approach to intelligence that reliably produced fantastically wrong answers, at vast expense. What so special about Iraq? Nothing, probably – except that we acquired ground truth.

Those weren’t leads, any more than there are really faces in the clouds. They were excuses to sell articles, raise money, and finally one extra argument in favor of a pointless war. Without a hard fact or two, it’s all vapor, useless.

Our tactical intelligence was fine in the Gulf War, but that doesn’t mean that the military, or worse yet the people who make and influence decisions had any sense, then or now.

For example, I have long had an amateur interest in these things, and I got the impression, in the summer of 1990, that Saddam Hussein was about to invade Kuwait. I was telling everyone at work that Saddam was about to invade, till they got bored with it. This was about two weeks before it actually happened. I remember thinking about making a few investments based on that possible event, but never got around to, partly because I was really sleepy, since we had a month-old baby girl at home.

As I recall, the “threat officer” at the CIA warned about this, but since the higher-ups ignored him, his being correct embarrassed them, so he was demoted.

The tactical situation was as favorable as it ever gets, and most of it was known. We had near-perfect intelligence:: satellite recon, JSTARS, etc Complete air domination, everything from Warthogs to F-15s. . Months to get ready. A huge qualitative weapons superiority. For example, our tanks outranged theirs by about a factor of two, had computer-controlled aiming, better armor, infrared sights, etc etc etc etc. I counted something like 13 separate war-winning advantages at the time, and that count was obviously incomplete.. And one more: Arabs make terrible soldiers, generally, and Iraqis were among the worst.

But I think that most of the decisionmakers didn’t realize how easy it would be – at all – and I’ve never seen any sign that Colin Powell did either. He’s a “C” student type – not smart. Schwartzkopf may have understood what was going on: for all I know he was another Manstein, but you can’t show how good you are when you beat a patzer.

For me it was a hobby – I was doing adaptive optics at the time in Colorado Springs. All I knew about particular military moves was from the newspapers, but my reasoning went like this:

A. Kuwait had a lot of oil. Worth stealing, if you could get away with it.

B. Kuwait was militarily impotent and had no defense treaty with anyone. Most people found Kuwaitis annoying.

C. Iraq owed Kuwait something like 30 billion dollars, and was generally deep in debt due to the long conflict with Iran

D. I figured that there was a fair chance that the Iraqi accusations of Kuwaiti slant drilling were true

E. There were widely reported Iraqi troop movements towards Kuwait

F. Most important was my evaluation of Saddam, from watching the long war with Iran. I thought that Saddam was a particular combination of cocky and stupid, the sort of guy to do something like this. At the time I did not know about April Glaspie’s, shall we say, poorly chosen comments.
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april 2017 by nhaliday
Say a little prior for me: more on climate change - Statistical Modeling, Causal Inference, and Social Science
We have only one planet. This fact radically constrains the kinds of risks that are appropriate to take at a large scale. Even a risk with a very low probability becomes unacceptable when it affects all of us – there is no reversing mistakes of that magnitude.
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april 2017 by nhaliday
A Rejection of 'Broken Windows Policing' Over Race Actually Hurts Minority Neighborhoods | Manhattan Institute
Late-night slightly controversial criminal justice thread:

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

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

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

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

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

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

Do parole abolition and Truth-in-Sentencing deter violent crimes in Virginia?: http://link.springer.com.sci-hub.tw/article/10.1007/s00181-017-1332-4

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

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

Short- and long-term effects of imprisonment on future felony convictions and prison admissions: http://www.pnas.org/content/early/2017/09/26/1701544114.short
Prison isn't criminogenic—offenders have higher rates of re-incarceration because of technical parole violations
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january 2017 by nhaliday
When does Robert Cottrell just stop reading? (from the comments) - Marginal REVOLUTION
I read full-time to edit The Browser, and I abandon a hundred articles for every one that I finish. I generally stop if I hit “eponymous”, or “toxic”, or “trigger warning”, or “make no mistake”. Summary labelling of anything in an article as “complex” means that the writer does not understand or cannot explain the material. I don’t often read beyond headlines that use the words “surprising”, “secret”, “really”, “not” or “… and why it matters”. Any headline ending in a question mark is a bad sign. I know writers don’t usually write their own headlines, but the headline represents a best effort to say what is useful in the article by a sympathetic person who has been paid to read it.
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may 2016 by nhaliday

bundles : abstractpatternspredictionthinkingvague

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