“Human Nature” by David Berlinski: A Revew

I became fan of David Berlinksi, who calls himself a secular Jew, after reading The Devil’s Delusion: Atheism and Its Scientific Pretensions, described on Berlinkski’s personal website as

a biting defense of faith against its critics in the New Atheist movement. “The attack on traditional religious thought,” writes Berlinski, “marks the consolidation in our time of science as the single system of belief in which rational men and women might place their faith, and if not their faith, then certainly their devotion.”

Here is most of what I say in “Atheistic Scientism Revisited” about The Devil’s Delusion:

Berlinski, who knows far more about science than I do, writes with flair and scathing logic. I can’t do justice to his book, but I will try to convey its gist.

Before I do that, I must tell you that I enjoyed Berlinski’s book not only because of the author’s acumen and biting wit, but also because he agrees with me. (I suppose I should say, in modesty, that I agree with him.) I have argued against atheistic scientism in many blog posts (see below).

Here is my version of the argument against atheism in its briefest form (June 15, 2011):

  1. In the material universe, cause precedes effect.
  2. Accordingly, the material universe cannot be self-made. It must have a “starting point,” but the “starting point” cannot be in or of the material universe.
  3. The existence of the universe therefore implies a separate, uncaused cause.

There is no reasonable basis — and certainly no empirical one — on which to prefer atheism to deism or theism. Strident atheists merely practice a “religion” of their own. They have neither logic nor science nor evidence on their side — and eons of belief against them.

As for scientism, I call upon Friedrich Hayek:

[W]e shall, wherever we are concerned … with slavish imitation of the method and language of Science, speak of “scientism” or the “scientistic” prejudice…. It should be noted that, in the sense in which we shall use these terms, they describe, of course, an attitude which is decidedly unscientific in the true sense of the word, since it involves a mechanical and uncritical application of habits of thought to fields different from those in which they have been formed. The scientistic as distinguished from the scientific view is not an unprejudiced but a very prejudiced approach which, before it has considered its subject, claims to know what is the most appropriate way of investigating it. [The Counter Revolution Of Science]

As Berlinski amply illustrates and forcibly argues, atheistic scientism is rampant in the so-called sciences. I have reproduced below some key passages from Berlinski’s book. They are representative, but far from exhaustive (though I did nearly exhaust the publisher’s copy limit on the Kindle edition). I have forgone the block-quotation style for ease of reading, and have inserted triple asterisks to indicate (sometimes subtle) changes of topic. [Go to my post for the excerpts.]

On the strength of The Devil’s Delusion, I eagerly purchased Berlinski’s latest book, Human Nature. I have just finished it, and cannot summon great enthusiasm for it. Perhaps that is so because I expected a deep and extended examination of the title’s subject. What I got, instead, was a collection of 23 disjointed essays, gathered (more or less loosely) into seven parts.

Only the first two parts, “Violence” and “Reason”, seem to address human nature, but often tangentially. “Violence” deals specifically with violence as manifested (mainly) in war and murder. The first essay, titled “The First World War”, is a tour de force — a dazzling (and somewhat dizzying) reconstruction of the complex and multi-tiered layering of the historical precedent, institutional arrangements, and personalities that led to the outbreak of World War I.

Aha, I thought to myself, Berlinkski is warming to his task, and will flesh out the relevant themes at which he hints in the first essay. And in the second and third essays, “The Best of Times” and “The Cause of War”, Berlinski flays the thesis of Steven Pinker’s The Better Angels of Our Nature: Why Violence Has Declined. But my post, “The Fallacy of Human Progress“, does a better job of it, thanks to the several critics and related sources quoted therein.

Berlinski ends the third essay with this observation:

Men go to war when they think that they can get away with murder.

Which is tantamount to an admission that Berlinski has no idea why men go to war, or would rather not venture an opinion on the subject. There is much of that kind of diffident agnosticism throughout the book, which is captured in his reply to an interviewer’s question in the book’s final essay:

Q. Would you share with us your hunches and suspicions about spiritual reality, the trend in your thinking, if not your firm beliefs?

A. No. Either I cannot or I will not. I do not know whether I am unable or unwilling. The question elicits in me a stubborn refusal. Please understand. It is not an issue of privacy. I have, after all, blabbed my life away: Why should I call a halt here? I suppose that I am by nature a counter-puncher. What I am able to discern of the religious experience often comes about reactively. V. S. Naipaul remarked recently that he found the religious life unthinkable.

He does? I was prompted to wonder. Why does he?

His attitude gives rise to mine. That is the way in which I wrote The Devil’s Delusion: Atheism and Its Scientific Pretensions.

Is there anything authentic in my religious nature?

Beats me.

That is a legitimate reply, but — I suspect — an evasive one.

Returning to the book’s ostensible subject, the second part, “Reason”, addresses human nature mainly in a negative way, that is, by pointing out (in various ways) flaws in the theory of evolution. There is no effort to knit the strands into a coherent theme. The following parts stray even further from the subject of the book’s title, and are even more loosely connected.

This isn’t to say that the book fails to entertain, for it often does that. For example, buried in a chapter on language, “The Recovery of Case”, is this remark:

Sentences used in the ordinary give-and-take of things are, of course, limited in their length. Henry James could not have constructed a thousand-word sentence without writing it down or suffering a stroke. Nor is recursion needed to convey the shock of the new. Four plain-spoken words are quite enough: Please welcome President Trump.

(I assume, given Berlinski’s track record for offending “liberal” sensibilities, that the italicized words refer to the shock of Trump’s being elected, and are not meant to disparage Trump.)

But the book also irritates, not only by its failure to deliver what the title seems to promise, but also by Berlinski’s proclivity for using the abstruse symbology of mathematical logic where words would do quite nicely and more clearly. In the same vein — showing off — is the penultimate essay, “A Conversation with Le Figaro“, which reproduces (after an introduction by Berlinksi) of a transcript of the interview — in French, with not a word of translation. Readers of the book will no doubt be more schooled in French than the typical viewer of prime-time TV fare, but many of them will be in my boat. My former fluency in spoken and written French has withered with time, and although I could still manage with effort to decipher the meaning of the transcript, it proved not to be worth the effort so I gave up on it.

There comes a time when once-brilliant persons can summon flashes of their old, brilliant selves but can no longer emit a sustained ray of brilliance. Perhaps that is true of Berlinski. I hope not, and will give him another try if he gives writing another try.

“Hurricane Hysteria” and “Climate Hysteria”, Updated

In view of the persistent claims about the role of “climate change” as the cause of tropical cyclone activity (i.e, tropical storms and hurricanes) I have updated “Hurricane Hysteria“. The bottom line remains the same: Global measures of accumulated cyclone energy (ACE) do not support the view that there is a correlation between “climate change” and tropical cyclone activity.

I have also updated “Climate Hysteria“, which borrows from “Hurricane Hysteria” but also examines climate patterns in Austin, Texas, where our local weather nazi peddles his “climate change” balderdash.

Climate Hysteria

UPDATED 11/19/19

Recent weather events have served to reinforce climate hysteria. There are the (usual) wildfires in California, which have nothing to do with “climate change” (e.g., this, this, and this), but you wouldn’t know it if you watch the evening news (which I don’t but impressionable millions do).

Closer to home, viewers have been treated to more of the same old propaganda from our local weather nazi, who proclaims it “nice” when daytime high temperatures are in the 60s and 70s, and who bemoans higher temperatures. (Why does he stay in Austin, then?) We watch him because when he isn’t proselytizing “climate change” he delivers the most detailed weather report available on Austin’s TV stations.

He was in “climate change” heaven when in September and part of October Austin endured a heat wave that saw many new high temperatures for the relevant dates. To top it off, tropical storm Imelda suddenly formed in mid-September near the gulf coast of Texas and inundated Houston. According to him, both events were due to “climate change”. Or were they just weather? My money’s on the latter.

Let’s take Imelda, which the weather nazi proclaimed to be an example of the kind of “extreme” weather event that will occur more often as “climate change” takes us in the direction of catastrophe. Those “extreme” weather events, when viewed globally (which is the only correct way to view them) aren’t occurring more often. This is from “Hurricane Hysteria“, which I have just updated to include statistics compiled as of today (11/19/19):

[T]he data sets for tropical cyclone activity that are maintained by the Tropical Meteorology Project at Colorado State University cover all six of the relevant ocean basins as far back as 1972. The coverage goes back to 1961 (and beyond) for all but the North Indian Ocean basin — which is by far the least active.

Here is NOAA’s reconstruction of ACE in the North Atlantic basin through November 19, 2019, which, if anything, probably understates ACE before the early 1960s:

The recent spikes in ACE are not unprecedented. And there are many prominent spikes that predate the late-20th-century temperature rise on which “warmism” is predicated. The trend from the late 1800s to the present is essentially flat. And, again, the numbers before the early 1960s must understate ACE.

Moreover, the metric of real interest is global cyclone activity; the North Atlantic basin is just a sideshow. Consider this graph of the annual values for each basin from 1972 through November 19, 2019:

Here’s a graph of stacked (cumulative) totals for the same period:

The red line is the sum of ACE for all six basins, including the Northwest Pacific basin; the yellow line in the sum of ACE for the next five basins, including the Northeast Pacific basin; etc.

I have these observations about the numbers represented in the preceding graphs:

  • If one is a believer in CAGW (the G stands for global), it is a lie (by glaring omission) to focus on random, land-falling hurricanes hitting the U.S. or other parts of the Western Hemisphere.
  • The overall level of activity is practically flat between 1972 and 2019, with the exception of spikes that coincide with strong El Niño events.
  • There is nothing in the long-term record for the North Atlantic basin, which is probably understated before the early 1960s, to suggest that global activity in recent decades is unusually high.

Imelda was an outlier — an unusual event that shouldn’t be treated as a typical one. Imelda happened along in the middle of a heat wave and accompanying dry spell in central Texas. This random juxtaposition caused the weather nazi to drool in anticipation of climate catastrophe.

There are some problems with the weather nazi’s reaction to the heat wave. First, the global circulation models (GCMs) that forecast ever-rising temperatures have been falsified. (See the discussion of GCMs here.) Second, the heat wave and the dry spell should be viewed in perspective. Here, for example are annualized temperature and rainfall averages for Austin, going back to the decade in which “global warming” began to register on the consciousnesses of climate hysterics:

What do you see? I see a recent decline in Austin’s average temperature from the El Nino effect of 2015-2016. I also see a decline in rainfall that doesn’t come close to being as severe the a dozen or so declines that have occurred since 1970.

In fact, abnormal heat is to be expected when there is little rain and a lot of sunshine. Temperature data, standing by themselves, are of little use because of the pronounced urban-heat-island (UHI) effect (discussed here). Drawing on daily weather reports for Austin for the past five years, I find that Austin’s daily high temperature is significantly affected by rainfall, wind speed, wind direction, and cloud cover. For example (everything else being the same):

  • An additional inch of rainfall induces an temperature drop of 1.2 degrees F.
  • A wind of 10 miles an hour from the north induces a temperature drop of about 5.9 degrees F relative to a 10-mph wind from the south.
  • Going from 100-percent sunshine to 100-percent cloud cover induces a temperature drop of 0.7 degrees F. (The combined effect of an inch of rain and complete loss of sunshine is therefore 1.9 degrees F, even before other factors come into play.)

The combined effects of variations in rainfall, wind speed, wind direction, and cloud cover are far more than enough to account for the molehill temperature anomalies that “climate change” hysterics magnify into mountains of doom.

Further, there is no systematic bias in the estimates, as shown by the following plot of regression residuals:

Summer is the most predictable of the seasons; winter, the least predicable. Over- and under-estimates seem to be evenly distributed across the seasons. In other words, the regression doesn’t mask changes in seasonal temperature patterns. Note, however, that this fall (which includes both the hot spell and cold snap discussed above) has been dominated by below-normal temperatures, not above-normal ones.

Anyway, during the spell of hot, dry weather, the maximum temperature went as high as 16 degrees F above the 30-year average for relevant date. Two days later, the maximum temperature was 12 degrees F below the 30-year average for the relevant date. This suggests that recent weather extremes tell us a lot about the variability of weather in central Texas and nothing about “climate change”.

However, the 16-degree deviation above the 30-year average was far from the greatest during the period under analysis; above-normal deviations have ranged as high as 26 degrees F above 30-year averages. By contrast, during the recent cold snap, deviations have reached their lowest levels for the period under analysis. The down-side deviations are obvious in the preceding graph. The pattern suggests that, if anything, this fall in Austin has been abnormally cold rather than abnormally hot. But I am not holding my breath while waiting for the weather nazi to admit it.

What’s in a Trend?

I sometimes forget myself and use “trend”. Then I see a post like “Trends for Existing Home Sales in the U.S.” and am reminded why “trend” is a bad word. This graphic is the centerpiece of the post:

There was a sort of upward trend from June 2016 until August 2017, but the trend stopped. So it wasn’t really a trend was it? (I am here using “trend” in way that it seems to be used generally, that is, as a direction of movement into the future.)

After a sort of flat period, the trend turned upward again, didn’t it? No, because the trend had been broken, so a new trend began in the early part of 2018. But it was a trend only until August 2018, when it became a different trend — mostly downward for several months.

Is there a flat trend now, or as the author of the piece puts it: “Existing home sales in the U.S. largely continued treading water through August 2019”? Well that was the trend — temporary pattern is a better descriptor — but it doesn’t mean that the value of existing-home sales will continue to hover around $1.5 trillion.

The moral of the story: The problem with “trend” is that it implies a direction of movement into the future —  a future will look like a lot like the past. But a trend is only a trend for as long as it lasts. And who knows how long it will last, that is, when it will stop?

I hope to start a trend toward the disuse of “trend”. My hope is futile.

Not-So-Random Thoughts (XXIV)

“Not-So-Random Thoughts” is an occasional series in which I highlight writings by other commentators on varied subjects that I have addressed in the past. Other entries in the series can be found at these links: I, II, III, IV, V, VI, VII, VIII, IX, X, XI, XII, XIII, XIV, XV, XVI, XVII, XVIII, XIX, XX, XXI, XXII, and XXIII. For more in the same style, see “The Tenor of the Times” and “Roundup: Civil War, Solitude, Transgenderism, Academic Enemies, and Immigration“.

CONTENTS

The Transgender Trap: A Political Nightmare Becomes Reality

Spygate (a.k.a. Russiagate) Revisited

More Evidence for Why I Don’t Believe in “Climate Change”

Thoughts on Mortality

Assortative Mating, Income Inequality, and the Crocodile Tears of “Progressives”


The Transgender Trap: A Political Nightmare Becomes Reality

Begin here and here, then consider the latest outrage.

First, from Katy Faust (“Why It’s Probably Not A Coincidence That The Mother Transing Her 7-Year-Old Isn’t Biologically Related“, The Federalist, October 24, 2019):

The story of seven-year-old James, whom his mother has pressured to become “Luna,” has been all over my newsfeed. The messy custody battle deserves every second of our click-bait-prone attention: Jeffrey Younger, James’s father, wants to keep his son’s body intact, while Anne Georgulas, James’s mother, wants to allow for “treatment” that would physically and chemically castrate him.

The havoc that divorce wreaks in a child’s life is mainstage in this tragic case. Most of us children of divorce quickly learn to act one way with mom and another way with dad. We can switch to a different set of rules, diet, family members, bedtime, screen time limits, and political convictions in that 20-minute ride from mom’s house to dad’s.

Unfortunately for little James, the adaptation he had to make went far beyond meat-lover’s pizza at dad’s house and cauliflower crusts at mom’s: it meant losing one of the most sacred aspects of his identity—his maleness. His dad loved him as a boy, so he got to be himself when he was at dad’s house. But mom showered love on the version of James she preferred, the one with the imaginary vagina.

So, as kids are so apt to do, when James was at her house, he conformed to the person his mother loved. This week a jury ruled that James must live like he’s at mom’s permanently, where he can “transition” fully, regardless of the cost to his mental and physical health….

Beyond the “tale of two households” that set up this court battle, and the ideological madness on display in the proceedings, something else about this case deserves our attention: one of the two parents engaged in this custodial tug-of-war isn’t biologically related to little James. Care to guess which one? Do you think it’s the parent who wants to keep him physically whole? It’s not.

During her testimony Georgulas stated she is not the biological mother of James or his twin brother Jude. She purchased eggs from a biological stranger. This illuminates a well-known truth in the world of family and parenthood: biological parents are the most connected to, invested in, and protective of their children.

Despite the jury’s unfathomable decision to award custody of James to his demented mother, there is hope for James. Walt Hyer picks up the story (“Texas Court Gives 7-Year-Old Boy A Reprieve From Transgender Treatments“, The Federalist, October 25, 2019):

Judge Kim Cooks put aside the disappointing jury’s verdict of Monday against the father and ruled Thursday that Jeffrey Younger now has equal joint conservatorship with the mother, Dr. Anne Georgulas, of their twin boys.

The mother no longer has unfettered authority to manipulate her 7-year old boy into gender transition. Instead both mother and father will share equally in medical, psychological, and other decision-making for the boys. Additionally, the judge changed the custody terms to give Younger an equal amount of visitation time with his sons, something that had been severely limited….

For those who need a little background, here’s a recap. “Six-year-old James is caught in a gender identity nightmare. Under his mom’s care in Dallas, Texas, James obediently lives as a trans girl named ‘Luna.’ But given the choice when he’s with dad, he’s all boy—his sex from conception.

“In their divorce proceedings, the mother has charged the father with child abuse for not affirming James as transgender, has sought restraining orders against him, and is seeking to terminate his parental rights. She is also seeking to require him to pay for the child’s visits to a transgender-affirming therapist and transgender medical alterations, which may include hormonal sterilization starting at age eight.”

All the evidence points to a boy torn between pleasing two parents, not an overwhelming preference to be a girl….

Younger said at the trial he was painted as paranoid and in need of several years of psychotherapy because he doesn’t believe his young son wants to be a girl. But many experts agree that transgendering young children is hazardous.

At the trial, Younger’s expert witnesses testified about these dangers and provided supporting evidence. Dr. Stephen Levine, a psychiatrist renowned for his work on human sexuality, testified that social transition—treating them as the opposite sex—increases the chance that a child will remain gender dysphoric. Dr. Paul W. Hruz, a pediatric endocrinologist and professor of pediatrics and cellular biology at Washington University School of Medicine in Saint Louis, testified that the risks of social transition are so great that the “treatment” cannot be recommended at all.

Are these doctors paranoid, too? Disagreement based on scientific evidence is now considered paranoia requiring “thought reprogramming.” That’s scary stuff when enforced by the courts….

The jury’s 11-1 vote to keep sole managing conservatorship from the father shows how invasive and acceptable this idea of confusing children and transitioning them has become. It’s like we are watching a bad movie where scientific evidence is ignored and believing the natural truth of male and female biology is considered paranoia. I can testify from my life experience the trans-life movie ends in unhappiness, regret, detransitions, or sadly, suicide.

The moral of the story is that the brainwashing of the American public by the media may have advanced to the tipping point. The glory that was America may soon vanish with a whimper.


Spygate (a.k.a. Russiagate) Revisited

I posted my analysis of “Spygate” well over a year ago, and have continually updated the appended list of supporting reference. The list continues to grow as evidence mounts to support the thesis that the Trump-Russia collusion story was part of a plot hatched at the highest levels of the Obama administration and executed within the White House, the CIA, and the Department of Justice (including especially the FBI).

Margot Cleveland addresses the case of Michael Flynn (“Sidney Powell Drops Bombshell Showing How The FBI Trapped Michael Flynn“, The Federalist, October 25, 2019):

Earlier this week, Michael Flynn’s star attorney, Sidney Powell, filed under seal a brief in reply to federal prosecutors’ claims that they have already given Flynn’s defense team all the evidence they are required by law to provide. A minimally redacted copy of the reply brief has just been made public, and with it shocking details of the deep state’s plot to destroy Flynn….

What is most striking, though, is the timeline Powell pieced together from publicly reported text messages withheld from the defense team and excerpts from documents still sealed from public view. The sequence Powell lays out shows that a team of “high-ranking FBI officials orchestrated an ambush-interview of the new president’s National Security Advisor, not for the purpose of discovering any evidence of criminal activity—they already had tapes of all the relevant conversations about which they questioned Mr. Flynn—but for the purpose of trapping him into making statements they could allege as false” [in an attempt to “flip” Flynn in the Spygate affair]….

The timeline continued to May 10 when McCabe opened an “obstruction” investigation into President Trump. That same day, Powell writes, “in an important but still wrongly redacted text, Strzok says: ‘We need to lock in [redacted]. In a formal chargeable way. Soon.’” Page replies: “I agree. I’ve been pushing and I’ll reemphasize with Bill [Priestap].”

Powell argues that “both from the space of the redaction, its timing, and other events, the defense strongly suspects the redacted name is Flynn.” That timing includes Robert Mueller’s appointment as special counsel on May 17, and then the reentering of Flynn’s 302 on May 31, 2017, “for Special Counsel Mueller to use.”

The only surprise (to me) is evidence cited by Cleveland that Comey was deeply embroiled in the plot. I have heretofore written off Comey as an opportunist who was out to get Trump for his own reasons.

In any event, Cleveland reinforces my expressed view of former CIA director John Brennan’s central role in the plot (“All The Russia Collusion Clues Are Beginning To Point Back To John Brennan“, The Federalist, October 25, 2019):

[I]f the media reports are true, and [Attorney General William] Barr and [U.S. attorney John] Durham have turned their focus to Brennan and the intelligence community, it is not a matter of vengeance; it is a matter of connecting the dots in congressional testimony and reports, leaks, and media spin, and facts exposed during the three years of panting about supposed Russia collusion. And it all started with Brennan.

That’s not how the story went, of course. The company story ran that the FBI launched its Crossfire Hurricane surveillance of the Trump campaign on July 31, 2016, after learning that a young Trump advisor, George Papadopoulos, had bragged to an Australian diplomat, Alexander Downer, that the Russians had dirt on Hillary Clinton….

But as the Special Counsel Robert Mueller report made clear, it wasn’t merely Papadopoulos’ bar-room boast at issue: It was “a series of contacts between Trump Campaign officials and individuals with ties to the Russian government,” that the DOJ and FBI, and later the Special Counsel’s office investigated.

And who put the FBI on to those supposedly suspicious contacts? Former CIA Director John Brennan….

The evidence suggests … that Brennan’s CIA and the intelligence community did much more than merely pass on details about “contacts and interactions between Russian officials and U.S. persons involved in the Trump campaign” to the FBI. The evidence suggests that the CIA and intelligence community—including potentially the intelligence communities of the UK, Italy, and Australia—created the contacts and interactions that they then reported to the FBI as suspicious.

The Deep State in action.


More Evidence for Why I Don’t Believe in “Climate Change”

I’ve already adduced a lot of evidence in “Why I Don’t Believe in Climate Change” and “Climate Change“. One of the scientists to whom I give credence is Dr. Roy Spencer of the Climate Research Center at the University of Alabama-Huntsville. Spencer agrees that CO2 emissions must have an effect on atmospheric temperatures, but is doubtful about the magnitude of the effect.

He revisits a point that he has made before, namely, that the there is no “preferred” state of the climate (“Does the Climate System Have a Preferred Average State? Chaos and the Forcing-Feedback Paradigm“, Roy Spencer, Ph.D., October 25, 2019):

If there is … a preferred average state, then the forcing-feedback paradigm of climate change is valid. In that system of thought, any departure of the global average temperature from the Nature-preferred state is resisted by radiative “feedback”, that is, changes in the radiative energy balance of the Earth in response to the too-warm or too-cool conditions. Those radiative changes would constantly be pushing the system back to its preferred temperature state…

[W]hat if the climate system undergoes its own, substantial chaotic changes on long time scales, say 100 to 1,000 years? The IPCC assumes this does not happen. But the ocean has inherently long time scales — decades to millennia. An unusually large amount of cold bottom water formed at the surface in the Arctic in one century might take hundreds or even thousands of years before it re-emerges at the surface, say in the tropics. This time lag can introduce a wide range of complex behaviors in the climate system, and is capable of producing climate change all by itself.

Even the sun, which we view as a constantly burning ball of gas, produces an 11-year cycle in sunspot activity, and even that cycle changes in strength over hundreds of years. It would seem that every process in nature organizes itself on preferred time scales, with some amount of cyclic behavior.

This chaotic climate change behavior would impact the validity of the forcing-feedback paradigm as well as our ability to determine future climate states and the sensitivity of the climate system to increasing CO2. If the climate system has different, but stable and energy-balanced, states, it could mean that climate change is too complex to predict with any useful level of accuracy [emphasis added].

Which is exactly what I say in “Modeling and Science“.


Thoughts on Mortality

I ruminated about it in “The Unique ‘Me’“:

Children, at some age, will begin to understand that there is death, the end of a human life (in material form, at least). At about the same time, in my experience, they will begin to speculate about the possibility that they might have been someone else: a child born in China, for instance.

Death eventually loses its fascination, though it may come to mind from time to time as one grows old. (Will I wake up in the morning? Is this the day that my heart stops beating? Will I be able to break my fall when the heart attack happens, or will I just go down hard and die of a fractured skull?)

Bill Vallicella (Maverick Philosopher) has been ruminating about it in recent posts. This is from his “Six Types of Death Fear” (October 24, 2019):

1. There is the fear of nonbeing, of annihilation….

2. There is the fear of surviving one’s bodily death as a ghost, unable to cut earthly attachments and enter nonbeing and oblivion….

3. There is the fear of post-mortem horrors….

4. There is the fear of the unknown….

5. There is the fear of the Lord and his judgment….

6. Fear of one’s own judgment or the judgment of posterity.

There is also — if one is in good health and enjoying life — the fear of losing what seems to be a good thing, namely, the enjoyment of life itself.


Assortative Mating, Income Inequality, and the Crocodile Tears of “Progressives”

Mating among human beings has long been assortative in various ways, in that the selection of a mate has been circumscribed or determined by geographic proximity, religious affiliation, clan rivalries or alliances, social relationships or enmities, etc. The results have sometimes been propitious, as Gregory Cochran points out in “An American Dilemma” (West Hunter, October 24, 2019):

Today we’re seeing clear evidence of genetic differences between classes: causal differences.  People with higher socioeconomic status have ( on average) higher EA polygenic scores. Higher scores for cognitive ability, as well. This is of course what every IQ test has shown for many decades….

Let’s look at Ashkenazi Jews in the United States. They’re very successful, averaging upper-middle-class.   So you’d think that they must have high polygenic scores for EA  (and they do).

Were they a highly selected group?  No: most were from Eastern Europe. “Immigration of Eastern Yiddish-speaking Ashkenazi Jews, in 1880–1914, brought a large, poor, traditional element to New York City. They were Orthodox or Conservative in religion. They founded the Zionist movement in the United States, and were active supporters of the Socialist party and labor unions. Economically, they concentrated in the garment industry.”

And there were a lot of them: it’s harder for a sample to be very unrepresentative when it makes up a big fraction of the entire population.

But that can’t be: that would mean that Europeans Jews were just smarter than average.  And that would be racist.

Could it be result of some kind of favoritism?  Obviously not, because that would be anti-Semitic.

Cochran obviously intends sarcasm in the final two paragraphs. The evidence for the heritability of intelligence is, as he says, quite strong. (See, for example, my “Race and Reason: The Achievement Gap — Causes and Implications” and “Intelligence“.) Were it not for assortative mating among Ashkenazi Jews, they wouldn’t be the most intelligent ethnic-racial group.

Branko Milanovic specifically addresses the “hot” issue in “Rich Like Me: How Assortative Mating Is Driving Income Inequality“, Quillette, October 18, 2019):

Recent research has documented a clear increase in the prevalence of homogamy, or assortative mating (people of the same or similar education status and income level marrying each other). A study based on a literature review combined with decennial data from the American Community Survey showed that the association between partners’ level of education was close to zero in 1970; in every other decade through 2010, the coefficient was positive, and it kept on rising….

At the same time, the top decile of young male earners have been much less likely to marry young women who are in the bottom decile of female earners. The rate has declined steadily from 13.4 percent to under 11 percent. In other words, high-earning young American men who in the 1970s were just as likely to marry high-earning as low-earning young women now display an almost three-to- one preference in favor of high-earning women. An even more dramatic change happened for women: the percentage of young high-earning women marrying young high-earning men increased from just under 13 percent to 26.4 percent, while the percentage of rich young women marrying poor young men halved. From having no preference between rich and poor men in the 1970s, women currently prefer rich men by a ratio of almost five to one….

High income and wealth inequality in the United States used to be justified by the claim that everyone had the opportunity to climb up the ladder of success, regardless of family background. This idea became known as the American Dream. The emphasis was on equality of opportunity rather than equality of outcome….

The American Dream has remained powerful both in the popular imagination and among economists. But it has begun to be seriously questioned during the past ten years or so, when relevant data have become available for the first time. Looking at twenty-two countries around the world, Miles Corak showed in 2013 that there was a positive correlation between high inequality in any one year and a strong correlation between parents’ and children’s incomes (i.e., low income mobility). This result makes sense, because high inequality today implies that the children of the rich will have, compared to the children of the poor, much greater opportunities. Not only can they count on greater inheritance, but they will also benefit from better education, better social capital obtained through their parents, and many other intangible advantages of wealth. None of those things are available to the children of the poor. But while the American Dream thus was somewhat deflated by the realization that income mobility is greater in more egalitarian countries than in the United States, these results did not imply that intergenerational mobility had actually gotten any worse over time.

Yet recent research shows that intergenerational mobility has in fact been declining. Using a sample of parent-son and parent-daughter pairs, and comparing a cohort born between 1949 and 1953 to one born between 1961 and 1964, Jonathan Davis and Bhashkar Mazumder found significantly lower intergenerational mobility for the latter cohort.

Milanovic doesn’t mention the heritabiliity of intelligence, which is bound to be generally higher among children of high-IQ parents (like Ashkenzi Jews and East Asians), and the strong correlation between intelligence and income. Does this mean that assortative mating should be banned and “excess” wealth should be confiscated and redistributed? Elizabeth Warren and Bernie Sanders certainly favor the second prescription, which would have a disastrous effect on the incentive to become rich and therefore on economic growth.

I addressed these matters in “Intelligence, Assortative Mating, and Social Engineering“:

So intelligence is real; it’s not confined to “book learning”; it has a strong influence on one’s education, work, and income (i.e., class); and because of those things it leads to assortative mating, which (on balance) reinforces class differences. Or so the story goes.

But assortative mating is nothing new. What might be new, or more prevalent than in the past, is a greater tendency for intermarriage within the smart-educated-professional class instead of across class lines, and for the smart-educated-professional class to live in “enclaves” with their like, and to produce (generally) bright children who’ll (mostly) follow the lead of their parents.

How great are those tendencies? And in any event, so what? Is there a potential social problem that will  have to be dealt with by government because it poses a severe threat to the nation’s political stability or economic well-being? Or is it just a step in the voluntary social evolution of the United States — perhaps even a beneficial one?…

[Lengthy quotations from statistical evidence and expert commentary.]

What does it all mean? For one thing, it means that the children of top-quintile parents reach the top quintile about 30 percent of the time. For another thing, it means that, unsurprisingly, the children of top-quintile parents reach the top quintile more often than children of second-quintile parents, who reach the top quintile more often than children of third-quintile parents, and so on.

There is nevertheless a growing, quasi-hereditary, smart-educated-professional-affluent class. It’s almost a sure thing, given the rise of the two-professional marriage, and given the correlation between the intelligence of parents and that of their children, which may be as high as 0.8. However, as a fraction of the total population, membership in the new class won’t grow as fast as membership in the “lower” classes because birth rates are inversely related to income.

And the new class probably will be isolated from the “lower” classes. Most members of the new class work and live where their interactions with persons of “lower” classes are restricted to boss-subordinate and employer-employee relationships. Professionals, for the most part, work in office buildings, isolated from the machinery and practitioners of “blue collar” trades.

But the segregation of housing on class lines is nothing new. People earn more, in part, so that they can live in nicer houses in nicer neighborhoods. And the general rise in the real incomes of Americans has made it possible for persons in the higher income brackets to afford more luxurious homes in more luxurious neighborhoods than were available to their parents and grandparents. (The mansions of yore, situated on “Mansion Row,” were occupied by the relatively small number of families whose income and wealth set them widely apart from the professional class of the day.) So economic segregation is, and should be, as unsurprising as a sunrise in the east.

None of this will assuage progressives, who like to claim that intelligence (like race) is a social construct (while also claiming that Republicans are stupid); who believe that incomes should be more equal (theirs excepted); who believe in “diversity,” except when it comes to where most of them choose to live and school their children; and who also believe that economic mobility should be greater than it is — just because. In their superior minds, there’s an optimum income distribution and an optimum degree of economic mobility — just as there is an optimum global temperature, which must be less than the ersatz one that’s estimated by combining temperatures measured under various conditions and with various degrees of error.

The irony of it is that the self-segregated, smart-educated-professional-affluent class is increasingly progressive….

So I ask progressives, given that you have met the new class and it is you, what do you want to do about it? Is there a social problem that might arise from greater segregation of socio-economic classes, and is it severe enough to warrant government action. Or is the real “problem” the possibility that some people — and their children and children’s children, etc. — might get ahead faster than other people — and their children and children’s children, etc.?

Do you want to apply the usual progressive remedies? Penalize success through progressive (pun intended) personal income-tax rates and the taxation of corporate income; force employers and universities to accept low-income candidates (whites included) ahead of better-qualified ones (e.g., your children) from higher-income brackets; push “diversity” in your neighborhood by expanding the kinds of low-income housing programs that helped to bring about the Great Recession; boost your local property and sales taxes by subsidizing “affordable housing,” mandating the payment of a “living wage” by the local government, and applying that mandate to contractors seeking to do business with the local government; and on and on down the list of progressive policies?

Of course you do, because you’re progressive. And you’ll support such things in the vain hope that they’ll make a difference. But not everyone shares your naive beliefs in blank slates, equal ability, and social homogenization (which you don’t believe either, but are too wedded to your progressive faith to admit). What will actually be accomplished — aside from tokenism — is social distrust and acrimony, which had a lot to do with the electoral victory of Donald J. Trump, and economic stagnation, which hurts the “little people” a lot more than it hurts the smart-educated-professional-affluent class….

The solution to the pseudo-problem of economic inequality is benign neglect, which isn’t a phrase that falls lightly from the lips of progressives. For more than 80 years, a lot of Americans — and too many pundits, professors, and politicians — have been led astray by that one-off phenomenon: the Great Depression. FDR and his sycophants and their successors created and perpetuated the myth that an activist government saved America from ruin and totalitarianism. The truth of the matter is that FDR’s policies prolonged the Great Depression by several years, and ushered in soft despotism, which is just “friendly” fascism. And all of that happened at the behest of people of above-average intelligence and above-average incomes.

Progressivism is the seed-bed of eugenics, and still promotes eugenics through abortion on demand (mainly to rid the world of black babies). My beneficial version of eugenics would be the sterilization of everyone with an IQ above 125 or top-40-percent income who claims to be progressive [emphasis added].

Enough said.

More Unsettled Science

Now hear this:

We’re getting something wrong about the universe.

It might be something small: a measurement issue that makes certain stars looks closer or farther away than they are, something astrophysicists could fix with a few tweaks to how they measure distances across space. It might be something big: an error — or series of errors — in  cosmology, or our understanding of the universe’s origin and evolution. If that’s the case, our entire history of space and time may be messed up. But whatever the issue is, it’s making key observations of the universe disagree with each other: Measured one way, the universe appears to be expanding at a certain rate; measured another way, the universe appears to be expanding at a different rate. And, as a new paper shows, those discrepancies have gotten larger in recent years, even as the measurements have gotten more precise….

The two most famous measurements work very differently from one another. The first relies on the Cosmic Microwave Background (CMB): the microwave radiation leftover from the first moments after the Big Bang. Cosmologists have built theoretical models of the entire history of the universe on a CMB foundation — models they’re very confident in, and that would require an all-new physics to break. And taken together, Mack said, they produce a reasonably precise number for the Hubble constant, or H0, which governs how fast the universe is currently expanding.

The second measurement uses supernovas and flashing stars in nearby galaxies, known as Cepheids. By gauging how far those galaxies are from our own, and how fast they’re moving away from us, astronomers have gotten what they believe is a very precise measurement of the Hubble constant. And that method offers a different H0.

It’s possible that the CMB model is just wrong in some way, and that’s leading to some sort of systematic error in how physicists are understanding the universe….

It’s [also] possible … that the supernovas-Cepheid calculation is just wrong. Maybe physicists are measuring distances in our local universe wrong, and that’s leading to a miscalculation. It’s hard to imagine what that miscalculation would be, though…. Lots of astrophysicists have measured local distances from scratch and have come up with similar results. One possibility … is just that we live in a weird chunk of the universe where there are fewer galaxies and less gravity, so our neighborhood is expanding faster than the universe as a whole….

Coming measurements might clarify the contradiction — either explaining it away or heightening it, suggesting a new field of physics is necessary. The Large Synoptic Survey Telescope, scheduled to come online in 2020, should find hundreds of millions of supernovas, which should vastly improve the datasets astrophysicists are using to measure distances between galaxies. Eventually, … gravitational wave studies will get good enough to constrain the expansion of the universe as well, which should add another level of precision to cosmology. Down the road, … physicists might even develop instruments sensitive enough to watch objects expand away from one another in real time.

But for the moment cosmologists are still waiting and wondering why their measurements of the universe don’t make sense together.

Here’s a very rough analogy to the problem described above:

  • A car traveling at a steady speed on a highway passes two markers that are separated by a measured distance (expressed in miles). Dividing the distance between the markers by the time of travel between the markers (expressed in hours) gives the speed of the car in miles per hour.
  • The speed of the same car is estimated by a carefully calibrated radar gun, one that has been tested on many cars under conditions like those in which it is used on the car in question.
  • The two methods yield different results. They are so different that there is no overlap between the normal ranges of uncertainty for the two methods.

The problem is really much more complicated than that. In the everyday world of cars traveling on highways, relativistic effects are unimportant and can be ignored. In the universe where objects are moving away from each other at a vastly greater speed — a speed that seems to increase constantly — relativistic effects are crucial. By relativistic effects I mean the interdependence of distance, time, and speed — none of which is an absolute, and all of which depend on each other (maybe).

If the relativistic effects involved in measuring cosmological phenomena are well understood, they shouldn’t account for the disparate estimates of the Hubble constant (H0). This raises a possibility that isn’t mentioned in the article quoted above, namely, that the relativistic effects aren’t well understood or have been misestimated.

There are other possibilities; for example:

  • The basic cosmological assumption of a Big Bang and spatially uniform expansion is wrong.
  • The speed of light (and/or other supposed constants) isn’t invariant.
  • There is an “unknown unknown” that may never be identified, let alone quantified.

Whatever the case, this is a useful reminder that science is never settled.

More about Modeling and Science

This post is based on a paper that I wrote 38 years ago. The subject then was the bankruptcy of warfare models, which shows through in parts of this post. I am trying here to generalize the message to encompass all complex, synthetic models (defined below). For ease of future reference, I have created a page that includes links to this post and the many that are listed at the bottom.

THE METAPHYSICS OF MODELING

Alfred North Whitehead said in Science and the Modern World (1925) that “the certainty of mathematics depends on its complete abstract generality” (p. 25). The attraction of mathematical models is their apparent certainty. But a model is only a representation of reality, and its fidelity to reality must be tested rather than assumed. And even if a model seems faithful to reality, its predictive power is another thing altogether. We are living in an era when models that purport to reflect reality are given credence despite their lack of predictive power. Ironically, those who dare point this out are called anti-scientific and science-deniers.

To begin at the beginning, I am concerned here with what I will call complex, synthetic models of abstract variables like GDP and “global” temperature. These are open-ended, mathematical models that estimate changes in the variable of interest by attempting to account for many contributing factors (parameters) and describing mathematically the interactions between those factors. I call such models complex because they have many “moving parts” — dozens or hundreds of sub-models — each of which is a model in itself. I call them synthetic because the estimated changes in the variables of interest depend greatly on the selection of sub-models, the depictions of their interactions, and the values assigned to the constituent parameters of the sub-models. That is to say, compared with a model of the human circulatory system or an internal combustion engine, a synthetic model of GDP or “global” temperature rests on incomplete knowledge of the components of the systems in question and the interactions among those components.

Modelers seem ignorant of or unwilling to acknowledge what should be a basic tenet of scientific inquiry: the complete dependence of logical systems (such as mathematical models) on the underlying axioms (assumptions) of those systems. Kurt Gödel addressed this dependence in his incompleteness theorems:

Gödel’s incompleteness theorems are two theorems of mathematical logic that demonstrate the inherent limitations of every formal axiomatic system capable of modelling basic arithmetic….

The first incompleteness theorem states that no consistent system of axioms whose theorems can be listed by an effective procedure (i.e., an algorithm) is capable of proving all truths about the arithmetic of natural numbers. For any such consistent formal system, there will always be statements about natural numbers that are true, but that are unprovable within the system. The second incompleteness theorem, an extension of the first, shows that the system cannot demonstrate its own consistency.

There is the view that Gödel’s theorems aren’t applicable in fields outside of mathematical logic. But any quest for certainty about the physical world necessarily uses mathematical logic (which includes statistics).

This doesn’t mean that the results of computational exercises are useless. It simply means that they are only as good as the assumptions that underlie them; for example, assumptions about relationships between parameters, assumptions about the values of the parameters, and assumptions as to whether the correct parameters have been chosen (and properly defined) in the first place.

There is nothing new in that, certainly nothing that requires Gödel’s theorems by way of proof. It has long been understood that a logical argument may be valid — the conclusion follows from the premises — but untrue if the premises (axioms) are untrue. But it bears repeating — and repeating.

REAL MODELERS AT WORK

There have been mathematical models of one kind and another for centuries, but formal models weren’t used much outside the “hard sciences” until the development of microeconomic theory in the 19th century. Then came F.W. Lanchester, who during World War I devised what became known as Lanchester’s laws (or Lanchester’s equations), which are

mathematical formulae for calculating the relative strengths of military forces. The Lanchester equations are differential equations describing the time dependence of two [opponents’] strengths A and B as a function of time, with the function depending only on A and B.

Lanchester’s equations are nothing more than abstractions that must be given a semblance of reality by the user, who is required to make myriad assumptions (explicit and implicit) about the factors that determine the “strengths” of A and B, including but not limited to the relative killing power of various weapons, the effectiveness of opponents’ defenses, the importance of the speed and range of movement of various weapons, intelligence about the location of enemy forces, and commanders’ decisions about when, where, and how to engage the enemy. It should be evident that the predictive value of the equations, when thus fleshed out, is limited to small, discrete engagements, such as brief bouts of aerial combat between two (or a few) opposing aircraft. Alternatively — and in practice — the values are selected so as to yield results that mirror what actually happened (in the “replication” of a historical battle) or what “should” happen (given the preferences of the analyst’s client).

More complex (and realistic) mathematical modeling (also known as operations research) had seen limited use in industry and government before World War II. Faith in the explanatory power of mathematical models was burnished by their use during the war, where such models seemed to be of aid in the design of more effective tactics and weapons.

But the foundation of that success wasn’t the mathematical character of the models. Rather, it was the fact that the models were tested against reality. Philip M. Morse and George E. Kimball put it well in Methods of Operations Research (1946):

Operations research done separately from an administrator in charge of operations becomes an empty exercise. To be valuable it must be toughened by the repeated impact of hard operational facts and pressing day-by-day demands, and its scale of values must be repeatedly tested in the acid of use. Otherwise it may be philosophy, but it is hardly science. [Op cit., p. 10]

A mathematical model doesn’t represent scientific knowledge unless its predictions can be and have been tested. Even then, a valid model can represent only a narrow slice of reality. The expansion of a model beyond that narrow slice requires the addition of parameters whose interactions may not be well understood and whose values will be uncertain.

Morse and Kimball accordingly urged “hemibel thinking”:

Having obtained the constants of the operations under study … we compare the value of the constants obtained in actual operations with the optimum theoretical value, if this can be computed. If the actual value is within a hemibel ( … a factor of 3) of the theoretical value, then it is extremely unlikely that any improvement in the details of the operation will result in significant improvement. [When] there is a wide gap between the actual and theoretical results … a hint as to the possible means of improvement can usually be obtained by a crude sorting of the operational data to see whether changes in personnel, equipment, or tactics produce a significant change in the constants. [Op cit., p. 38]

Should we really attach little significance to differences of less than a hemibel? Consider a five-parameter model involving the conditional probabilities of detecting, shooting at, hitting, and killing an opponent — and surviving, in the first place, to do any of these things. Such a model can easily yield a cumulative error of a hemibel (or greater), given a twenty-five percent error in the value each parameter. (Mathematically, 1.255 = 3.05; alternatively, 0.755 = 0.24, or about one-fourth.)

ANTI-SCIENTIFIC MODELING

What does this say about complex, synthetic models such as those of economic activity or “climate change”? Any such model rests on the modeler’s assumptions as to the parameters that should be included, their values (and the degree of uncertainty surrounding them), and the interactions among them. The interactions must be modeled based on further assumptions. And so assumptions and uncertainties — and errors — multiply apace.

But the prideful modeler (I have yet to meet a humble one) will claim validity if his model has been fine-tuned to replicate the past (e.g., changes in GDP, “global” temperature anomalies). But the model is useless unless it predicts the future consistently and with great accuracy, where “great” means accurately enough to validly represent the effects of public-policy choices (e.g., setting the federal funds rate, investing in CO2 abatement technology).

Macroeconomic Modeling: A Case Study

In macroeconomics, for example, there is Professor Ray Fair, who teaches macroeconomic theory, econometrics, and macroeconometric modeling at Yale University. He has been plying his trade at prestigious universities since 1968, first at Princeton, then at MIT, and since 1974 at Yale. Professor Fair has since 1983 been forecasting changes in real GDP — not decades ahead, just four quarters (one year) ahead. He has made 141 such forecasts, the earliest of which covers the four quarters ending with the second quarter of 1984, and the most recent of which covers the four quarters ending with the second quarter of 2019. The forecasts are based on a model that Professor Fair has revised many times over the years. The current model is here. His forecasting track record is here.) How has he done? Here’s how:

1. The median absolute error of his forecasts is 31 percent.

2. The mean absolute error of his forecasts is 69 percent.

3. His forecasts are rather systematically biased: too high when real, four-quarter GDP growth is less than 3 percent; too low when real, four-quarter GDP growth is greater than 3 percent.

4. His forecasts have grown generally worse — not better — with time. Recent forecasts are better, but still far from the mark.

Thus:


This and the next two graphs were derived from The Forecasting Record of the U.S. Model, Table 4: Predicted and Actual Values for Four-Quarter Real Growth, at Prof. Fair’s website. The vertical axis of this graph is truncated for ease of viewing, as noted in the caption.

You might think that Fair’s record reflects the persistent use of a model that’s too simple to capture the dynamics of a multi-trillion-dollar economy. But you’d be wrong. The model changes quarterly. This page lists changes only since late 2009; there are links to archives of earlier versions, but those are password-protected.

As for simplicity, the model is anything but simple. For example, go to Appendix A: The U.S. Model: July 29, 2016, and you’ll find a six-sector model comprising 188 equations and hundreds of variables.

And what does that get you? A weak predictive model:

It fails a crucial test, in that it doesn’t reflect the downward trend in economic growth:

General Circulation Models (GCMs) and “Climate Change”

As for climate models, Dr. Tim Ball writes about a

fascinating 2006 paper by Essex, McKitrick, and Andresen asked, Does a Global Temperature Exist.” Their introduction sets the scene,

It arises from projecting a sampling of the fluctuating temperature field of the Earth onto a single number (e.g. [3], [4]) at discrete monthly or annual intervals. Proponents claim that this statistic represents a measurement of the annual global temperature to an accuracy of ±0.05 ◦C (see [5]). Moreover, they presume that small changes in it, up or down, have direct and unequivocal physical meaning.

The word “sampling” is important because, statistically, a sample has to be representative of a population. There is no way that a sampling of the “fluctuating temperature field of the Earth,” is possible….

… The reality is we have fewer stations now than in 1960 as NASA GISS explain (Figure 1a, # of stations and 1b, Coverage)….

Not only that, but the accuracy is terrible. US stations are supposedly the best in the world but as Anthony Watt’s project showed, only 7.9% of them achieve better than a 1°C accuracy. Look at the quote above. It says the temperature statistic is accurate to ±0.05°C. In fact, for most of the 406 years when instrumental measures of temperature were available (1612), they were incapable of yielding measurements better than 0.5°C.

The coverage numbers (1b) are meaningless because there are only weather stations for about 15% of the Earth’s surface. There are virtually no stations for

  • 70% of the world that is oceans,
  • 20% of the land surface that are mountains,
  • 20% of the land surface that is forest,
  • 19% of the land surface that is desert and,
  • 19% of the land surface that is grassland.

The result is we have inadequate measures in terms of the equipment and how it fits the historic record, combined with a wholly inadequate spatial sample. The inadequacies are acknowledged by the creation of the claim by NASA GISS and all promoters of anthropogenic global warming (AGW) that a station is representative of a 1200 km radius region.

I plotted an illustrative example on a map of North America (Figure 2).

clip_image006

Figure 2

Notice that the claim for the station in eastern North America includes the subarctic climate of southern James Bay and the subtropical climate of the Carolinas.

However, it doesn’t end there because this is only a meaningless temperature measured in a Stevenson Screen between 1.25 m and 2 m above the surface….

The Stevenson Screen data [are] inadequate for any meaningful analysis or as the basis of a mathematical computer model in this one sliver of the atmosphere, but there [are] even less [data] as you go down or up. The models create a surface grid that becomes cubes as you move up. The number of squares in the grid varies with the naïve belief that a smaller grid improves the models. It would if there [were] adequate data, but that doesn’t exist. The number of cubes is determined by the number of layers used. Again, theoretically, more layers would yield better results, but it doesn’t matter because there are virtually no spatial or temporal data….

So far, I have talked about the inadequacy of the temperature measurements in light of the two- and three-dimensional complexities of the atmosphere and oceans. However, one source identifies the most important variables for the models used as the basis for energy and environmental policies across the world.

Sophisticated models, like Coupled General Circulation Models, combine many processes to portray the entire climate system. The most important components of these models are the atmosphere (including air temperature, moisture and precipitation levels, and storms); the oceans (measurements such as ocean temperature, salinity levels, and circulation patterns); terrestrial processes (including carbon absorption, forests, and storage of soil moisture); and the cryosphere (both sea ice and glaciers on land). A successful climate model must not only accurately represent all of these individual components, but also show how they interact with each other.

The last line is critical and yet impossible. The temperature data [are] the best we have, and yet [they are] completely inadequate in every way. Pick any of the variables listed, and you find there [are] virtually no data. The answer to the question, “what are we really measuring,” is virtually nothing, and what we measure is not relevant to anything related to the dynamics of the atmosphere or oceans.

I am especially struck by Dr. Ball’s observation that the surface-temperature record applies to about 15 percent of Earth’s surface. Not only that, but as suggested by Dr. Ball’s figure 2, that 15 percent is poorly sampled.

And yet the proponents of CO2-forced “climate change” rely heavily on that flawed temperature record because it is the only one that goes back far enough to “prove” the modelers’ underlying assumption, namely, that it is anthropogenic CO2 emissions which have caused the rise in “global” temperatures. See, for example, Dr. Roy Spencer’s “The Faith Component of Global Warming Predictions“, wherein Dr. Spencer points out that the modelers

have only demonstrated what they assumed from the outset. It is circular reasoning. A tautology. Evidence that nature also causes global energy imbalances is abundant: e.g., the strong warming before the 1940s; the Little Ice Age; the Medieval Warm Period. This is why many climate scientists try to purge these events from the historical record, to make it look like only humans can cause climate change.

In fact the models deal in temperature anomalies, that is, departures from a 30-year average. The anomalies — which range from -1.41 to +1.68 degrees C — are so small relative to the errors and uncertainties inherent in the compilation, estimation, and model-driven adjustments of the temperature record, that they must fail Morse and Kimball’s hemibel test. (The model-driven adjustments are, as Dr. Spencer suggests, downward adjustments of historical temperature data for consistency with the models which “prove” that CO2 emissions induce a certain rate of warming. More circular reasoning.)

They also fail, and fail miserably, the acid test of predicting future temperatures with accuracy. This failure has been pointed out many times. Dr. John Christy, for example, has testified to that effect before Congress (e.g., this briefing). Defenders of the “climate change” faith have attacked Dr. Christy’s methods and finding, but the rebuttals to one such attack merely underscore the validity of Dr. Christy’s work.

This is from “Manufacturing Alarm: Dana Nuccitelli’s Critique of John Christy’s Climate Science Testimony“, by Mario Lewis Jr.:

Christy’s testimony argues that the state-of-the-art models informing agency analyses of climate change “have a strong tendency to over-warm the atmosphere relative to actual observations.” To illustrate the point, Christy provides a chart comparing 102 climate model simulations of temperature change in the global mid-troposphere to observations from two independent satellite datasets and four independent weather balloon data sets….

To sum up, Christy presents an honest, apples-to-apples comparison of modeled and observed temperatures in the bulk atmosphere (0-50,000 feet). Climate models significantly overshoot observations in the lower troposphere, not just in the layer above it. Christy is not “manufacturing doubt” about the accuracy of climate models. Rather, Nuccitelli is manufacturing alarm by denying the models’ growing inconsistency with the real world.

And this is from Christopher Monckton of Brenchley’s “The Guardian’s Dana Nuccitelli Uses Pseudo-Science to Libel Dr. John Christy“:

One Dana Nuccitelli, a co-author of the 2013 paper that found 0.5% consensus to the effect that recent global warming was mostly manmade and reported it as 97.1%, leading Queensland police to inform a Brisbane citizen who had complained to them that a “deception” had been perpetrated, has published an article in the British newspaper The Guardian making numerous inaccurate assertions calculated to libel Dr John Christy of the University of Alabama in connection with his now-famous chart showing the ever-growing discrepancy between models’ wild predictions and the slow, harmless, unexciting rise in global temperature since 1979….

… In fact, as Mr Nuccitelli knows full well (for his own data file of 11,944 climate science papers shows it), the “consensus” is only 0.5%. But that is by the bye: the main point here is that it is the trends on the predictions compared with those on the observational data that matter, and, on all 73 models, the trends are higher than those on the real-world data….

[T]he temperature profile [of the oceans] at different strata shows little or no warming at the surface and an increasing warming rate with depth, raising the possibility that, contrary to Mr Nuccitelli’s theory that the atmosphere is warming the ocean, the ocean is instead being warmed from below, perhaps by some increase in the largely unmonitored magmatic intrusions into the abyssal strata from the 3.5 million subsea volcanoes and vents most of which Man has never visited or studied, particularly at the mid-ocean tectonic divergence boundaries, notably the highly active boundary in the eastern equatorial Pacific. [That possibility is among many which aren’t considered by GCMs.]

How good a job are the models really doing in their attempts to predict global temperatures? Here are a few more examples:

Mr Nuccitelli’s scientifically illiterate attempts to challenge Dr Christy’s graph are accordingly misconceived, inaccurate and misleading.

I have omitted the bulk of both pieces because this post is already longer than needed to make my point. I urge you to follow the links and read the pieces for yourself.

Finally, I must quote a brief but telling passage from a post by Pat Frank, “Why Roy Spencer’s Criticism is Wrong“:

[H]ere’s NASA on clouds and resolution: “A doubling in atmospheric carbon dioxide (CO2), predicted to take place in the next 50 to 100 years, is expected to change the radiation balance at the surface by only about 2 percent. … If a 2 percent change is that important, then a climate model to be useful must be accurate to something like 0.25%. Thus today’s models must be improved by about a hundredfold in accuracy, a very challenging task.

Frank’s very long post substantiates what I say here about the errors and uncertainties in GCMs — and the multiplicative effect of those errors and uncertainties. I urge you to read it. It is telling that “climate skeptics” like Spencer and Frank will argue openly, whereas “true believers” work clandestinely to present a united front to the public. It’s science vs. anti-science.

CONCLUSION

In the end, complex, synthetic models can be defended only by resorting to the claim that they are “scientific”, which is a farcical claim when models consistently fail to yield accurate predictions. It is a claim based on a need to believe in the models — or, rather, what they purport to prove. It is, in other words, false certainty, which is the enemy of truth.

Newton said it best:

I do not know what I may appear to the world, but to myself I seem to have been only like a boy playing on the seashore, and diverting myself in now and then finding a smoother pebble or a prettier shell than ordinary, whilst the great ocean of truth lay all undiscovered before me.

Just as Newton’s self-doubt was not an attack on science, neither have I essayed an attack on science or modeling — only on the abuses of both that are too often found in the company of complex, synthetic models. It is too easily forgotten that the practice of science (of which modeling is a tool) is in fact an art, not a science. With this art we may portray vividly the few pebbles and shells of truth that we have grasped; we can but vaguely sketch the ocean of truth whose horizons are beyond our reach.


Related pages and posts:

Climate Change
Modeling and Science

Modeling Is Not Science
Modeling, Science, and Physics Envy
Demystifying Science
Analysis for Government Decision-Making: Hemi-Science, Hemi-Demi-Science, and Sophistry
The Limits of Science (II)
“The Science Is Settled”
The Limits of Science, Illustrated by Scientists
Rationalism, Empiricism, and Scientific Knowledge
Ty Cobb and the State of Science
Is Science Self-Correcting?
Mathematical Economics
Words Fail Us
“Science” vs. Science: The Case of Evolution, Race, and Intelligence
Modeling Revisited
The Fragility of Knowledge
Global-Warming Hype
Pattern-Seeking
Hurricane Hysteria
Deduction, Induction, and Knowledge
A (Long) Footnote about Science
The Balderdash Chronicles
Analytical and Scientific Arrogance
The Pretence of Knowledge
Wildfires and “Climate Change”
Why I Don’t Believe in “Climate Change”
Modeling Is Not Science: Another Demonstration
Ad-Hoc Hypothesizing and Data Mining
Analysis vs. Reality

Expressing Certainty (or Uncertainty)

I have waged war on the misuse of probability for a long time. As I say in the post at the link:

A probability is a statement about a very large number of like events, each of which has an unpredictable (random) outcome. Probability, properly understood, says nothing about the outcome of an individual event. It certainly says nothing about what will happen next.

From a later post:

It is a logical fallacy to ascribe a probability to a single event. A probability represents the observed or computed average value of a very large number of like events. A single event cannot possess that average value. A single event has a finite number of discrete and mutually exclusive outcomes. Those outcomes will not “average out” — only one of them will obtain, like Schrödinger’s cat.

To say that the outcomes will average out — which is what a probability implies — is tantamount to saying that Jack Sprat and his wife were neither skinny nor fat because their body-mass indices averaged to a normal value. It is tantamount to saying that one can’t drown by walking across a pond with an average depth of 1 foot, when that average conceals the existence of a 100-foot-deep hole.

But what about hedge words that imply “probability” without saying it: certain, uncertain, likely, unlikely, confident, not confident, sure, unsure, and the like? I admit to using such words, which are common in discussions about possible future events and the causes of past events. But what do I, and presumably others, mean by them?

Hedge words are statements about the validity of hypotheses about phenomena or causal relationships. There are two ways of looking at such hypotheses, frequentist and Bayesian:

While for the frequentist, a hypothesis is a proposition (which must be either true or false) so that the frequentist probability of a hypothesis is either 0 or 1, in Bayesian statistics, the probability that can be assigned to a hypothesis can also be in a range from 0 to 1 if the truth value is uncertain.

Further, as discussed above, there is no such thing as the probability of a single event. For example, the Mafia either did or didn’t have JFK killed, and that’s all there is to say about that. One might claim to be “certain” that the Mafia had JFK killed, but one can be certain only if one is in possession of incontrovertible evidence to that effect. But that certainty isn’t a probability, which can refer only to the frequency with which many events of the same kind have occurred and can be expected to occur.

A Bayesian view about the “probability” of the Mafia having JFK killed is nonsensical. Even If a Bayesian is certain, based on incontrovertible evidence, that the Mafia had JFK killed, there is no probability attached to the occurrence. It simply happened, and that’s that.

Lacking such evidence, a Bayesian (or an unwitting “man on the street”) might say “I believe there’s a 50-50 chance that the Mafia had JFK killed”. Does that mean (1) there’s some evidence to support the hypothesis, but it isn’t conclusive, or (2) that the speaker would bet X amount of money, at even odds, that incontrovertible evidence (if any) surfaces it will prove that the Mafia had JFK killed? In the first case, attaching a 50-percent probability to the hypothesis is nonsensical; how does the existence of some evidence translate into a statement about the probability of a one-off event that either occurred or didn’t occur? In the second case, the speaker’s willingness to bet on the occurrence of an event at certain odds tells us something about the speaker’s preference for risk-taking but nothing at all about whether or not the event occurred.

What about the familiar use of “probability” (a.k.a., “chance”) in weather forecasts? Here’s my take:

[W]hen you read or hear a statement like “the probability of rain tomorrow is 80 percent”, you should mentally translate it into language like this:

X guesses that Y will (or will not) happen at time Z, and the “probability” that he attaches to his guess indicates his degree of confidence in it.

The guess may be well-informed by systematic observation of relevant events, but it remains a guess. As most Americans have learned and relearned over the years, when rain has failed to materialize or has spoiled an outdoor event that was supposed to be rain-free.

Further, it is true that some things happen more often than other things but

only one thing will happen at a given time and place.

[A] clever analyst could concoct a probability of a person’s being shot by writing an equation that includes such variables as his size, the speed with which he walks, the number of shooters, their rate of fire, and the distance across the shooting range.

What would the probability estimate mean? It would mean that if a very large number of persons walked across the shooting range under identical conditions, approximately S percent of them would be shot. But the clever analyst cannot specify which of the walkers would be among the S percent.

Here’s another way to look at it. One person wearing head-to-toe bullet-proof armor could walk across the range a large number of times and expect to be hit by a bullet on S percent of his crossings. But the hardy soul wouldn’t know on which of the crossings he would be hit.

Suppose the hardy soul became a foolhardy one and made a bet that he could cross the range without being hit. Further, suppose that S is estimated to be 0.75; that is, 75 percent of a string of walkers would be hit, or a single (bullet-proof) walker would be hit on 75 percent of his crossings. Knowing the value of S, the foolhardy fellow offers to pay out $1 million dollars if he crosses the range unscathed — one time — and claim $4 million (for himself or his estate) if he is shot. That’s an even-money bet, isn’t it?

No it isn’t….

The bet should be understood for what it is, an either-or-proposition. The foolhardy walker will either lose $1 million or win $4 million. The bettor (or bettors) who take the other side of the bet will either win $1 million or lose $4 million.

As anyone with elementary reading and reasoning skills should be able to tell, those possible outcomes are not the same as the outcome that would obtain (approximately) if the foolhardy fellow could walk across the shooting range 1,000 times. If he could, he would come very close to breaking even, as would those who bet against him.

I omitted from the preceding quotation a sentence in which I used “more likely”:

If a person walks across a shooting range where live ammunition is being used, he is more likely to be killed than if he walks across the same patch of ground when no one is shooting.

Inasmuch as “more likely” is a hedge word, I seem to have contradicted my own position about the probability of a single event, such as being shot while walking across a shooting range. In that context, however, “more likely” means that something could happen (getting shot) that wouldn’t happen in a different situation. That’s not really a probabilistic statement. It’s a statement about opportunity; thus:

  • Crossing a firing range generates many opportunities to be shot.
  • Going into a crime-ridden neighborhood certainly generates some opportunities to be shot, but their number and frequency depends on many variables: which neighborhood, where in the neighborhood, the time of day, who else is present, etc.
  • Sitting by oneself, unarmed, in a heavy-gauge steel enclosure generates no opportunities to be shot.

The “chance” of being shot is, in turn, “more likely”, “likely”, and “unlikely” — or a similar ordinal pattern that uses “certain”, “confident”, “sure”, etc. But the ordinal pattern, in any case, can never (logically) include statements like “completely certain”, “completely confident”, etc.

An ordinal pattern is logically valid only if it conveys the relative number of opportunities to attain a given kind of outcome — being shot, in the example under discussion.

Ordinal statements about different types of outcome are meaningless. Consider, for example, the claim that the probability that the Mafia had JFK killed is higher than (or lower than or the same as) the probability that the moon is made of green cheese. First, and to repeat myself for the nth time, the phenomena in question are one-of-a-kind and do not lend themselves to statements about their probability, nor even about the frequency of opportunities for the occurrence of the phenomena. Second, the use of “probability” is just a hifalutin way of saying that the Mafia could have had a hand in the killing of JFK, whereas it is known (based on ample scientific evidence, including eye-witness accounts) that the Moon isn’t made of green cheese. So the ordinal statement is just a cheap rhetorical trick that is meant to (somehow) support the subjective belief that the Mafia “must” have had a hand in the killing of JFK.

Similarly, it is meaningless to say that the “average person” is “more certain” of being killed in an auto accident than in a plane crash, even though one may have many opportunities to die in an auto accident or a plane crash. There is no “average person”; the incidence of auto travel and plane travel varies enormously from person to person; and the conditions that conduce to fatalities in auto travel and plane travel vary just as enormously.

Other examples abound. Be on the lookout for them, and avoid emulating them.

Regarding Napoleon Chagnon

Napoleon Alphonseau Chagnon (1938-2019) was a noted anthropologist to whom the label “controversial” was applied. Some of the story is told in this surprisingly objective New York Times article about Chagnon’s life and death. Matthew Blackwell gives a more complete account in “The Dangerous Life of an Anthropologist” (Quilette, October 5, 2019). UPDATE 11/27/19: Alice Dreger’s article, “Napoleon Chagnon Is Dead” (The Chronicle Review, October 23, 2019) reveals the inner man and underscores his integrity.

Chagnon’s sin was his finding that “nature” trumped “nurture”, as demonstrated by his decades-long ethnographic field work among the Yanomamö, indigenous Amazonians who live in the border area between Venezuela and Brazil. As Blackwell tells it,

Chagnon found that up to 30 percent of all Yanomamö males died a violent death. Warfare and violence were common, and duelling was a ritual practice, in which two men would take turns flogging each other over the head with a club, until one of the combatants succumbed. Chagnon was adamant that the primary causes of violence among the Yanomamö were revenge killings and women. The latter may not seem surprising to anyone aware of the ubiquity of ruthless male sexual competition in the animal kingdom, but anthropologists generally believed that human violence found its genesis in more immediate matters, such as disputes over resources. When Chagnon asked the Yanomamö shaman Dedeheiwa to explain the cause of violence, he replied, “Don’t ask such stupid questions! Women! Women! Women! Women! Women!” Such fights erupted over sexual jealousy, sexual impropriety, rape, and attempts at seduction, kidnap and failure to deliver a promised girl….

Chagnon would make more than 20 fieldwork visits to the Amazon, and in 1968 he published Yanomamö: The Fierce People, which became an instant international bestseller. The book immediately ignited controversy within the field of anthropology. Although it commanded immense respect and became the most commonly taught book in introductory anthropology courses, the very subtitle of the book annoyed those anthropologists, who preferred to give their monographs titles like The Gentle Tasaday, The Gentle People, The Harmless People, The Peaceful People, Never in Anger, and The Semai: A Nonviolent People of Malaya. The stubborn tendency within the discipline was to paint an unrealistic façade over such cultures—although 61 percent of Waorani men met a violent death, an anthropologist nevertheless described this Amazonian people as a “tribe where harmony rules,” on account of an “ethos that emphasized peacefulness.”…

These anthropologists were made more squeamish still by Chagnon’s discovery that the unokai of the Yanomamö—men who had killed and assumed a ceremonial title—had about three times more children than others, owing to having twice as many wives. Drawing on this observation in his 1988 Science article “Life Histories, Blood Revenge, and Warfare in a Tribal Population,” Chagnon suggested that men who had demonstrated success at a cultural phenomenon, the military prowess of revenge killings, were held in higher esteem and considered more attractive mates. In some quarters outside of anthropology, Chagnon’s theory came as no surprise, but its implication for anthropology could be profound. In The Better Angels of Our Nature, Steven Pinker points out that if violent men turn out to be more evolutionarily fit, “This arithmetic, if it persisted over many generations, would favour a genetic tendency to be willing and able to kill.”…

Chagnon considered his most formidable critic to be the eminent anthropologist Marvin Harris. Harris had been crowned the unofficial historian of the field following the publication of his all-encompassing work The Rise of Anthropological Theory. He was the founder of the highly influential materialist school of anthropology, and argued that ethnographers should first seek material explanations for human behavior before considering alternatives, as “human social life is a response to the practical problems of earthly existence.” Harris held that the structure and “superstructure” of a society are largely epiphenomena of its “infrastructure,” meaning that the economic and social organization, beliefs, values, ideology, and symbolism of a culture evolve as a result of changes in the material circumstances of a particular society, and that apparently quaint cultural practices tend to reflect man’s relationship to his environment. For instance, prohibition on beef consumption among Hindus in India is not primarily due to religious injunctions. These religious beliefs are themselves epiphenomena to the real reasons: that cows are more valuable for pulling plows and producing fertilizers and dung for burning. Cultural materialism places an emphasis on “-etic” over “-emic” explanations, ignoring the opinions of people within a society and trying to uncover the hidden reality behind those opinions.

Naturally, when the Yanomamö explained that warfare and fights were caused by women and blood feuds, Harris sought a material explanation that would draw upon immediate survival concerns. Chagnon’s data clearly confirmed that the larger a village, the more likely fighting, violence, and warfare were to occur. In his book Good to Eat: Riddles of Food and Culture Harris argued that fighting occurs more often in larger Yanomamö villages because these villages deplete the local game levels in the rainforest faster than smaller villages, leaving the men no option but to fight with each other or to attack outside groups for meat to fulfil their protein macronutrient needs. When Chagnon put Harris’s materialist theory to the Yanomamö they laughed and replied, “Even though we like meat, we like women a whole lot more.” Chagnon believed that smaller villages avoided violence because they were composed of tighter kin groups—those communities had just two or three extended families and had developed more stable systems of borrowing wives from each other.

There’s more:

Survival International … has long promoted the Rousseauian image of a traditional people who need to be preserved in all their natural wonder from the ravages of the modern world. Survival International does not welcome anthropological findings that complicate this harmonious picture, and Chagnon had wandered straight into their line of fire….

For years, Survival International’s Terence Turner had been assisting a self-described journalist, Patrick Tierney, as the latter investigated Chagnon for his book, Darkness in El Dorado: How Scientists and Journalists Devastated the Amazon. In 2000, as Tierney’s book was being readied for publication, Turner and his colleague Leslie Sponsel wrote to the president of the American Anthropological Association (AAA) and informed her that an unprecedented crisis was about to engulf the field of anthropology. This, they warned, would be a scandal that, “in its scale, ramifications, and sheer criminality and corruption, is unparalleled in the history of Anthropology.” Tierney alleged that Chagnon and Neel had spread measles among the Yanomamö in 1968 by using compromised vaccines, and that Chagnon’s documentaries depicting Yanomamö violence were faked by using Yanomamö to act out dangerous scenes, in which further lives were lost. Chagnon was blamed, inter alia, for inciting violence among the Yanomamö, cooking his data, starting wars, and aiding corrupt politicians. Neel was also accused of withholding vaccines from certain populations of natives as part of an experiment. The media were not slow to pick up on Tierney’s allegations, and the Guardian ran an article under an inflammatory headline accusing Neel and Chagnon of eugenics: “Scientists ‘killed Amazon Indians to test race theory.’” Turner claimed that Neel believed in a gene for “leadership” and that the human genetic stock could be upgraded by wiping out mediocre people. “The political implication of this fascistic eugenics,” Turner told the Guardian, “is clearly that society should be reorganised into small breeding isolates in which genetically superior males could emerge into dominance, eliminating or subordinating the male losers.”

By the end of 2000, the American Anthropological Association announced a hearing on Tierney’s book. This was not entirely reassuring news to Chagnon, given their history with anthropologists who failed to toe the party line….

… Although the [AAA] taskforce [appointed to investigate Tierney’s accusations] was not an “investigation” concerned with any particular person, for all intents and purposes, it blamed Chagnon for portraying the Yanomamö in a way that was harmful and held him responsible for prioritizing his research over their interests.

Nonetheless, the most serious claims Tierney made in Darkness in El Dorado collapsed like a house of cards. Elected Yanomamö leaders issued a statement in 2000 stating that Chagnon had arrived after the measles epidemic and saved lives, “Dr. Chagnon—known to us as Shaki—came into our communities with some physicians and he vaccinated us against the epidemic disease which was killing us. Thanks to this, hundreds of us survived and we are very thankful to Dr. Chagnon and his collaborators for help.” Investigations by the American Society of Human Genetics and the International Genetic Epidemiology Society both found Tierney’s claims regarding the measles outbreak to be unfounded. The Society of Visual Anthropology reviewed the so-called faked documentaries, and determined that these allegations were also false. Then an independent preliminary report released by a team of anthropologists dissected Tierney’s book claim by claim, concluding that all of Tierney’s most important assertions were either deliberately fraudulent or, at the very least, misleading. The University of Michigan reached the same conclusion. “We are satisfied,” its Provost stated, “that Dr. Neel and Dr. Chagnon, both among the most distinguished scientists in their respective fields, acted with integrity in conducting their research… The serious factual errors we have found call into question the accuracy of the entire book [Darkness in El Dorado] as well as the interpretations of its author.” Academic journal articles began to proliferate, detailing the mis-inquiry and flawed conclusions of the 2002 taskforce. By 2005, only three years later, the American Anthropological Association voted to withdraw the 2002 taskforce report, re-exonerating Chagnon.

A 2000 statement by the leaders of the Yanomamö and their Ye’kwana neighbours called for Tierney’s head: “We demand that our national government investigate the false statements of Tierney, which taint the humanitarian mission carried out by Shaki [Chagnon] with much tenderness and respect for our communities. The investigation never occurred, but Tierney’s public image lay in ruins and would suffer even more at the hands of historian of science Alice Dreger, who interviewed dozens of people involved in the controversy. Although Tierney had thanked a Venezuelan anthropologist for providing him with a dossier of information on Chagnon for his book, the anthropologist told Dreger that Tierney had actually written the dossier himself and then misrepresented it as an independent source of information.

A “dossier” and its use to smear an ideological opponent. Where else have we seen that?

Returning to Blackwell:

Scientific American has described the controversy as “Anthropology’s Darkest Hour,” and it raises troubling questions about the entire field. In 2013, Chagnon published his final book, Noble Savages: My Life Among Two Dangerous Tribes—The Yanomamö and the Anthropologists. Chagnon had long felt that anthropology was experiencing a schism more significant than any difference between research paradigms or schools of ethnography—a schism between those dedicated to the very science of mankind, anthropologists in the true sense of the word, and those opposed to science; either postmodernists vaguely defined, or activists disguised as scientists who seek to place indigenous advocacy above the pursuit of objective truth. Chagnon identified Nancy Scheper-Hughes as a leader in the activist faction of anthropologists, citing her statement that we “need not entail a philosophical commitment to Enlightenment notions of reason and truth.”

Whatever the rights and wrong of his debates with Marvin Harris across three decades, Harris’s materialist paradigm was a scientifically debatable hypothesis, which caused Chagnon to realize that he and his old rival shared more in common than they did with the activist forces emerging in the field: “Ironically, Harris and I both argued for a scientific view of human behavior at a time when increasing numbers of anthropologists were becoming skeptical of the scientific approach.”…

Both Chagnon and Harris agreed that anthropology’s move away from being a scientific enterprise was dangerous. And both believed that anthropologists, not to mention thinkers in other fields of social sciences, were disguising their increasingly anti-scientific activism as research by using obscurantist postmodern gibberish. Observers have remarked at how abstruse humanities research has become and even a world famous linguist like Noam Chomsky admits, “It seems to me to be some exercise by intellectuals who talk to each other in very obscure ways, and I can’t follow it, and I don’t think anybody else can.” Chagnon resigned his membership of the American Anthropological Association in the 1980s, stating that he no longer understood the “unintelligible mumbo jumbo of postmodern jargon” taught in the field. In his last book, Theories of Culture in Postmodern Times, Harris virtually agreed with Chagnon. “Postmodernists,” he wrote, “have achieved the ability to write about their thoughts in a uniquely impenetrable manner. Their neo-baroque prose style with its inner clauses, bracketed syllables, metaphors and metonyms, verbal pirouettes, curlicues and figures is not a mere epiphenomenon; rather, it is a mocking rejoinder to anyone who would try to write simple intelligible sentences in the modernist tradition.”…

The quest for knowledge of mankind has in many respects become unrecognizable in the field that now calls itself anthropology. According to Chagnon, we’ve entered a period of “darkness in cultural anthropology.” With his passing, anthropology has become darker still.

I recount all of this for three reasons. First, Chagnon’s findings testify to the immutable urge to violence that lurks within human beings, and to the dominance of “nature” over “nurture”. That dominance is evident not only in the urge to violence (pace Steven Pinker), but in the strong heritability of such traits as intelligence.

The second reason for recounting Chagnon’s saga it is to underline the corruption of science in the service of left-wing causes. The underlying problem is always the same: When science — testable and tested hypotheses based on unbiased observations — challenges left-wing orthodoxy, left-wingers — many of them so-called scientists — go all out to discredit real scientists. And they do so by claiming, in good Orwellian fashion, to be “scientific”. (I have written many posts about this phenomenon.) Leftists are, in fact, delusional devotees of magical thinking.

The third reason for my interest in the story of Napoleon Chagnon is a familial connection of sorts. He was born in a village where his grandfather, also Napoleon Chagnon, was a doctor. My mother was one of ten children, most of them born and all of them raised in the same village. When the tenth child was born, he was given Napoleon as his middle name, in honor of Doc Chagnon.

Certainty about Uncertainty

Words fail us. Numbers, too, for they are only condensed words. Words and numbers are tools of communication and calculation. As tools, they cannot make certain that which is uncertain, though they often convey a false sense of certainty.

Yes, arithmetic seems certain: 2 + 2 = 4 is always and ever (in base-10 notation). But that is only because the conventions of arithmetic require 2 + 2 to equal 4. Neither arithmetic nor any higher form of mathematics reveals the truth about the world around us, though mathematics (and statistics) can be used to find approximate truths — approximations that are useful in practical applications like building bridges, finding effective medicines, and sending rockets into space (though the practicality of that has always escaped me).

But such practical things are possible only because the uncertainty surrounding them (e.g., the stresses that may cause a bridge to fail) is hedged against by making things more robust than they would need to be under perfect conditions. And, even then, things sometimes fail: bridges collapse, medicines have unforeseen side effects, rockets blow up, etc.

I was reminded of uncertainty by a recent post by Timothy Taylor (Conversable Economist):

For the uninitiated, “statistical significance” is a way of summarizing whether a certain statistical result is likely to have happened by chance, or not. For example, if I flip a coin 10 times and get six heads and four tails, this could easily happen by chance even with a fair and evenly balanced coin. But if I flip a coin 10 times and get 10 heads, this is extremely unlikely to happen by chance. Or if I flip a coin 10,000 times, with a result of 6,000 heads and 4,000 tails (essentially, repeating the 10-flip coin experiment 1,000 times), I can be quite confident that the coin is not a fair one. A common rule of thumb has been that if the probability of an outcome occurring by chance is 5% or less–in the jargon, has a p-value of 5% or less–then the result is statistically significant. However, it’s also pretty common to see studies that report a range of other p-values like 1% or 10%.

Given the omnipresence of “statistical significance” in pedagogy and the research literature, it was interesting last year when the American Statistical Association made an official statement “ASA Statement on Statistical Significance and P-Values” (discussed here) which includes comments like: “Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold. … A p-value, or statistical significance, does not measure the size of an effect or the importance of a result. … By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.”

Now, the ASA has followed up with a special supplemental issue of its journal The American Statistician on the theme “Statistical Inference in the 21st Century: A World Beyond p < 0.05” (January 2019).  The issue has a useful overview essay, “Moving to a World Beyond “p < 0.05.” by Ronald L. Wasserstein, Allen L. Schirm, and  Nicole A. Lazar. They write:

We conclude, based on our review of the articles in this special issue and the broader literature, that it is time to stop using the term “statistically significant” entirely. Nor should variants such as “significantly different,” “p < 0.05,” and “nonsignificant” survive, whether expressed in words, by asterisks in a table, or in some other way. Regardless of whether it was ever useful, a declaration of “statistical significance” has today become meaningless. … In sum, `statistically significant’—don’t say it and don’t use it.

. . .

So let’s accept the that the “statistical significance” label has some severe problems, as Wasserstein, Schirm, and Lazar write:

[A] label of statistical significance does not mean or imply that an association or effect is highly probable, real, true, or important. Nor does a label of statistical nonsignificance lead to the association or effect being improbable, absent, false, or unimportant. Yet the dichotomization into “significant” and “not significant” is taken as an imprimatur of authority on these characteristics. In a world without bright lines, on the other hand, it becomes untenable to assert dramatic differences in interpretation from inconsequential differences in estimates. As Gelman and Stern (2006) famously observed, the difference between “significant” and “not significant” is not itself statistically significant.

In the middle of the post, Taylor quotes Edward Leamer’s 1983 article, “Taking the Con out of Econometrics” (American Economic Review, March 1983, pp. 31-43).

Leamer wrote:

The econometric art as it is practiced at the computer terminal involves fitting many, perhaps thousands, of statistical models. One or several that the researcher finds pleasing are selected for re- porting purposes. This searching for a model is often well intentioned, but there can be no doubt that such a specification search in-validates the traditional theories of inference. … [I]n fact, all the concepts of traditional theory, utterly lose their meaning by the time an applied researcher pulls from the bramble of computer output the one thorn of a model he likes best, the one he chooses to portray as a rose. The consuming public is hardly fooled by this chicanery. The econometrician’s shabby art is humorously and disparagingly labelled “data mining,” “fishing,” “grubbing,” “number crunching.” A joke evokes the Inquisition: “If you torture the data long enough, Nature will confess” … This is a sad and decidedly unscientific state of affairs we find ourselves in. Hardly anyone takes data analyses seriously. Or perhaps more accurately, hardly anyone takes anyone else’s data analyses seriously.”

Economists and other social scientists have become much more aware of these issues over the decades, but Leamer was still writing in 2010 (“Tantalus on the Road to Asymptopia,” Journal of Economic Perspectives, 24: 2, pp. 31-46):

Since I wrote my “con in econometrics” challenge much progress has been made in economic theory and in econometric theory and in experimental design, but there has been little progress technically or procedurally on this subject of sensitivity analyses in econometrics. Most authors still support their conclusions with the results implied by several models, and they leave the rest of us wondering how hard they had to work to find their favorite outcomes … It’s like a court of law in which we hear only the experts on the plaintiff’s side, but are wise enough to know that there are abundant for the defense.

Taylor wisely adds this:

Taken together, these issues suggest that a lot of the findings in social science research shouldn’t be believed with too much firmness. The results might be true. They might be a result of a researcher pulling out “from the bramble of computer output the one thorn of a model he likes best, the one he chooses to portray as a rose.” And given the realities of real-world research, it seems goofy to say that a result with, say, only a 4.8% probability of happening by chance is “significant,” while if the result had a 5.2% probability of happening by chance it is “not significant.” Uncertainty is a continuum, not a black-and-white difference [emphasis added].

The italicized sentence expresses my long-held position.

But there is a deeper issue here, to which I alluded above in my brief comments about the nature of mathematics. The deeper issue is the complete dependence of logical systems on the underlying axioms (assumptions) of those systems, which Kurt Gödel addressed in his incompleteness theorems:

Gödel’s incompleteness theorems are two theorems of mathematical logic that demonstrate the inherent limitations of every formal axiomatic system capable of modelling basic arithmetic….

The first incompleteness theorem states that no consistent system of axioms whose theorems can be listed by an effective procedure (i.e., an algorithm) is capable of proving all truths about the arithmetic of natural numbers. For any such consistent formal system, there will always be statements about natural numbers that are true, but that are unprovable within the system. The second incompleteness theorem, an extension of the first, shows that the system cannot demonstrate its own consistency.

This is very deep stuff. I own the book in which Gödel proves his theorems, and I admit that I have to take the proofs on faith. (Which simply means that I have been too lazy to work my way through the proofs.) But there seem to be no serious or fatal criticisms of the theorems, so my faith is justified (thus far).

There is also the view that the theorems aren’t applicable in fields outside of mathematical logic. But any quest for certainty about the physical world necessarily uses mathematical logic (which includes statistics).

This doesn’t mean that the results of computational exercises are useless. It simply means that they are only as good as the assumptions that underlie them; for example, assumptions about relationships between variables, assumptions about the values of the variables, assumptions as to whether the correct variable have been chosen (and properly defined), in the first place.

There is nothing new in that, certainly nothing that requires Gödel’s theorems by way of proof. It has long been understood that a logical argument may be valid — the conclusion follows from the premises — but untrue if the premises (axioms) are untrue.

But it bears repeating and repeating — especially in the age of “climate change“. That CO2 is a dominant determinant of “global” temperatures is taken as axiomatic. Everything else flows from that assumption, including the downward revision of historical (actual) temperature readings, to ensure that the “observed” rise in temperatures agrees with — and therefore “proves” — the validity of climate models that take CO2 as dominant variable. How circular can you get?

Check your assumptions at the door.

What about the “Gay Gene”?

UPDATED 09/24/19

An article in Science discusses the findings of a recent “genome-wide association study (GWAS) [by Andrea Ganna et al.], in which the genome is analyzed for statistically significant associations between single-nucleotide polymorphisms (SNPs) and a particular trait.” That upshot is that

genetics could eventually account for an upper limit of 8 to 25% of same-sex sexual behavior of the population. However, when all of the SNPs they identified from the GWAS are considered together in a combined score, they explain less than 1%. Thus, although they did find particular genetic loci associated with same-sex behavior, when they combine the effects of these loci together into one comprehensive score, the effects are so small (under 1%) that this genetic score cannot in any way be used to predict same-sex sexual behavior of an individual.

Further, “Ganna et al. did not find evidence of any specific cells and tissues related to the loci they identified.”

Is this a big change from what was thought previously about genes and homosexuality? Yes, according to an article in ScienceNews:

The new study is an advance over previous attempts to find “gay genes,” says J. Michael Bailey, a psychologist at Northwestern University in Evanston, Ill., who was not involved in the new work. The study’s size is its main advantage, Bailey says. “It’s huge. Huge.”…

Previous sexual orientation genetic studies, including some Bailey was involved in, may also have suffered from bias because they relied on volunteers. People who offer to participate in a study, without being randomly selected, may not reflect the general population, he says. This study includes both men and women and doesn’t rely on twins, as many previous studies have….

This is the first DNA difference ever linked to female sexual orientation, says Lisa Diamond, a psychologist at the University of Utah in Salt Lake City who studies the nature and development of same-sex sexuality. The results are consistent with previous studies suggesting genetics may play a bigger role in influencing male sexuality than female sexuality. It’s not unusual for one sex of a species to be more fluid in their sexuality, choosing partners of both sexes, Diamond says. For humans, male sexuality may be more [but not very] tightly linked to genes.

But that doesn’t mean that genes control sexual behavior or orientation. “Same-sex sexuality appears to be genetically influenced, but not genetically determined,” Diamond says. “This is not the only complex human phenomenon for which we see a genetic influence without a great understanding of how that influence works.” Other complex human behaviors, such as smoking, alcohol use, personality and even job satisfaction all have some genetic component [emphasis added].

In sum, as an article in The Telegraph about the Ganna study explains,

[g]enes play just a small role in whether a person is gay, scientists have found, after discovering that environment has a far bigger impact on homosexuality….

[G]enes are responsible for between eight to 25 per cent of the probability of a person being gay, meaning at least three quarters is down to environment.

What’s most interesting about the commentary that I’ve read, including portions of the articles just quoted above, is what “environment” means to those who are eager to preserve the illusion of homosexuality as a condition that’s almost unavoidable.

The article in The Telegraph quotes one of the Ganna study’s authors, who is hardly a disinterested party:

As a gay man I’ve experienced homophobia and I’ve felt both hurt and isolated by it. This study disproves the notion there is a so-called ‘gay gene’ and disproves sexual behaviour is a choice.

Genetics absolutely plays an important role, many genes are involved, and altogether they capture perhaps a quarter of same-sex sexual behaviour, which means genetics isn’t even half the story. The rest is likely environmental.

It’s both biology and environment working together in incredibly complicated ways.

How does the study disprove that sexual behavior is a choice? It does so only if one makes the valiant assumption that environmental influences somehow don’t operate on behavior, and aren’t in turn shaped by behavior.

Another scientist, quoted in The Telegraph article, acknowledges my point:

There is an unexplained environmental effect that one can never put a finger on exactly it’s such a complex interplay between environment, upbringing, and genetics [emphasis added].

The Associated Press, always ready to spin news leftward, ran a story with the same facts and interpretations as those quoted earlier, but with the headline: “Study finds new genetic links to same-sex sexuality”. Which hides  the gist of the story: Homosexuality is mainly determined by environmental influences, which include and are shaped by behavioral choices.

What’s left unmentioned, of course, is that homosexuality is therefore predominantly a choice. What’s also left unmentioned are the environmental influences that are most likely to induce homosexual behavior. Here is my not-mutually-exclusive list of such influences:

  • shyness toward the opposite sex (caused by introversion, fear of rejection, self-assessment as unattractive)
  • proximity of potential sexual partners who are shy toward the opposite sex and/or open to experimentation, or who are seeking opportunities to seduce those who are shy and/or experimental
  • conditions conducive to experimentation (sleepovers, drunkenness, pornographic titillation)
  • encouraging or permissive milieu (parental encouragement/indifference, parental absence — as when a person is away at college, especially a “liberal” college populated by sexual experimenters/homosexuals)
  • general indifference or approbation (secularization of society, removal of criminal sanctions, legalization of sodomy, legalization of same-sex marriage, elite embrace and praise of non-traditional sexual behavior and roles: bisexuality, homosexuality, transgenderism).

All of those influences operate on the urge for sexual satisfaction, which is especially strong in adolescents and young adults. Given that urge, the incidence of introversion, the incidence of physical unattractiveness, the unconscionably large number of persons in college, and the social and legal trends of recent decades, it is almost shocking to learn that only about five percent of American adults think of themselves as LGBT. If you live in a “cosmopolitan” city of any size, you might believe that they account for a much larger fraction of the population. But that’s just due to the clustering effect — birds of a feather, and all that. Even if you don’t live in a “cosmopolitan” city, you may believe that far more than five percent of the population is LGBT because the producers of films and TV fare — “cosmopolitan” elites that they are — like to “celebrate diversity”. (Or, more aptly, shove it down your throat.)

In summary, I submit that most persons of the LGBT persuasion make a deliberate and often tragic choice about their “sexual identity”. (See, for example, my post, “The Transgender Fad and Its Consequences“, and Carlos D. Flores’s article, “The Absurdity of Transgenderism: A Stern But Necessary Critique“.)

I submit, further, that the study by Ganna et al. implies that conversion therapy could be effective, and that (politically correct) “scientific” opposition to it is based on the now-discredited view of homosexuality as a genetically immutable condition.

If the apologists for and promoters of LGBT “culture” were logically consistent in their insistence on homosexuality as a genetic condition, they would also acknowledge that intelligence is largely a matter of genetic inheritance. They don’t do that, generally, because they are usually leftists who subscribe to the blank-slate view of inherent equality.

If any trait is strongly genetic in origin, it is the abhorrence of homosexuality, which is a threat to the survival of the species.

UPDATE

There is some support for my hypothesis about environmental causes of homosexual behavior in Gregory Cochran’s post, “Gay Genes” at West Hunter, where he discusses the paper by Ganna et al.:

They found two SNPs [single-nucleotide polymorhpisms] that influenced both male and female homosexuality, two that affected males only, one that affected females only. All had very small but statistically significant effects….

The fraction of the variance influenced by these few SNPs is low, less than 1%. The contribution of all common SNPs is larger, estimated to be between 8% and 25%. Still small compared to traits like height and IQ, but then we knew that the heritability of homosexuality is not terribly high, from twin studies and such – political views are more heritable.

So genes influence homosexuality, but then they influence everything. Does it look as if the key causal link (assuming that there is one) is genetic? No, but then we knew that already, from high discordance for homosexuality in MZ twins.

Most interesting to me were the genetic correlations between same-sex behavior and various other traits [displayed in a graph]….

The genes correlated with male homosexuality are also correlated ( at a statistically significant level) with risk-taking, cannabis use,  schizophrenia, ADHD, major depressive disorder, loneliness, and number of sex partners. For female homosexuals, risk-taking, smoking, cannabis use, subjective well-being (-), schizophrenia, bipolar disorder, ADHD, major depressive disorder, loneliness, openness to experience, and number of sex partners.

Generally, the traits genetically correlated with homosexuality are bad things.  As far as I can see,  they look like noise, rather than any kind of genetic strategy.  Mostly, they accord with what we already knew about male and female homosexuals: both are significantly more likely to have psychiatric disorders, far more likely to use drugs.   The mental-illness association maybe looks stronger in lesbians.  The moderately-shared genetic architecture seems compatible with a noise model….

[The finding] that homosexuality was genetically correlated with various kinds of unpleasantness was apparently an issue in the preparation and publication of this paper. The authors were at some pains  to avoid hurting the feelings of the gay community, since avoiding hurting  feelings is the royal road to Truth, as shown by Galileo and Darwin.

If there are genes for bad behavior, it is the penchant for bad behavior that pushes some persons in the direction of homosexuality. But the choice is theirs. Many persons who are prone to bad behavior become serial philanderers, serial killers, sleazy politicians, etc., etc., etc. Homosexuality isn’t a necessary outcome of bad behavior, just a choice that many persons happen to make because of their particular circumstances.

P.S. I don’t mean to imply that bad behavior and homosexuality go hand-in-hand. As I suggest in the original post, there are other reasons for choosing homosexuality in those cases (the majority of them) where it isn’t a genetic predisposition.

(See also “The Myth That Same-Sex Marriage Causes No Harm“, “Two-Percent Tyranny“, and “Further Thoughts about Utilitarianism“.)

Analysis vs. Reality

In my days as a defense analyst I often encountered military officers who were skeptical about the ability of civilian analysts to draw valid conclusions from mathematical models about the merits of systems and tactics. I took me several years to understand and agree with their position. My growing doubts about the power of quantitative analysis of military matters culminated in a paper where I wrote that

combat is not a mathematical process…. One may describe the outcome of combat mathematically, but it is difficult, even after the fact, to determine the variables that made a difference in the outcome.

Much as we would like to fold the many different parameters of a weapon, a force, or a strategy into a single number, we can not. An analyst’s notion of which variables matter and how they interact is no substitute for data. Such data as exist, of course, represent observations of discrete events — usually peacetime events. It remains for the analyst to calibrate the observations, but without a benchmark to go by. Calibration by past battles is a method of reconstruction — of cutting one of several coats to fit a single form — but not a method of validation.

Lacking pertinent data, an analyst is likely to resort to models of great complexity. Thus, if useful estimates of detection probabilities are unavailable, the detection process is modeled; if estimates of the outcomes of dogfights are unavailable, aerial combat is reduced to minutiae. Spurious accuracy replaces obvious inaccuracy; untestable hypotheses and unchecked calibrations multiply apace. Yet the analyst claims relative if not absolute accuracy, certifying that he has identified, measured, and properly linked, a priori, the parameters that differentiate weapons, forces, and strategies.

In the end, “reasonableness” is the only defense of warfare models of any stripe.

It is ironic that analysts must fall back upon the appeal to intuition that has been denied to military men — whose intuition at least flows from a life-or-death incentive to make good guesses when choosing weapons, forces, or strategies.

My colleagues were not amused, to say the least.

I was reminded of all this by a recent exchange with a high-school classmate who had enlisted my help in tracking down a woman who, according to a genealogy website, is her first cousin, twice removed. The success of the venture is as yet uncertain. But if it does succeed it will be because of the classmate’s intimate knowledge of her family, not my command of research tools. As I said to my classmate,

You know a lot more about your family than I know about mine. I have all of the names and dates in my genealogy data bank, but I really don’t know much about their lives. After I moved to Virginia … I was out of the loop on family gossip, and my parents didn’t relate it to me. For example, when I visited my parents … for their 50th anniversary I happened to see a newspaper clipping about the death of my father’s sister a year earlier. It was news to me. And I didn’t learn of the death of my mother’s youngest brother (leaving her as the last of 10 children) until my sister happened to mention it to me a few years after he had died. And she didn’t know that I didn’t know.

All of which means that there’s a lot more to life than bare facts — dates of birth, death, etc. That’s why military people (with good reason) don’t trust analysts who draw conclusions about military weapons and tactics based on mathematical models. Those analysts don’t have a “feel” for how weapons and tactics actually work in the heat of battle, which is what matters.

Climate modelers are even more in the dark than military analysts because, unlike military officers with relevant experience, there’s no “climate officer” who can set climate modelers straight — or (more wisely) ignore them.

(See also “Modeling Is Not Science“, “The McNamara Legacy: A Personal Perspective“, “Analysis for Government Decision-Making: Hemi-Science, Hemi-Demi-Science, and Sophistry“, “Analytical and Scientific Arrogance“, “Why I Don’t Believe in ‘Climate Change’“, and “Predicting ‘Global’ Temperatures — An Analogy with Baseball“.)

Predicting “Global” Temperatures — An Analogy with Baseball

The following graph is a plot of the 12-month moving average of “global” mean temperature anomalies for 1979-2018 in the lower troposphere, as reported by the climate-research unit of the University of Alabama-Huntsville (UAH):

The UAH values, which are derived from satellite-borne sensors, are as close as one can come to an estimate of changes in “global” mean temperatures. The UAH values certainly are more complete and reliable than the values derived from the surface-thermometer record, which is biased toward observations over the land masses of the Northern Hemisphere (the U.S., in particular) — observations that are themselves notoriously fraught with siting problems, urban-heat-island biases, and “adjustments” that have been made to “homogenize” temperature data, that is, to make it agree with the warming predictions of global-climate models.

The next graph roughly resembles the first one, but it’s easier to describe. It represents the fraction of games won by the Oakland Athletics baseball team in the 1979-2018 seasons:

Unlike the “global” temperature record, the A’s W-L record is known with certainty. Every game played by the team (indeed, by all teams in organized baseball) is diligently recorded, and in great detail. Those records yield a wealth of information not only about team records, but also about the accomplishments of the individual players whose combined performance determines whether and how often a team wins its games. Given that information, and much else about which statistics are or could be compiled (records of players in the years and games preceding a season or game; records of each team’s owner, general managers, and managers; orientations of the ballparks in which each team compiled its records; distances to the fences in those ballparks; time of day at which games were played; ambient temperatures, and on and on).

Despite all of that knowledge, there is much uncertainty about how to model the interactions among the quantifiable elements of the game, and how to give weight to the non-quantifiable elements (a manager’s leadership and tactical skills, team spirit, and on and on). Even the professional prognosticators at FiveThirtyEight, armed with a vast compilation of baseball statistics from which they have devised a complex predictive model of baseball outcomes will admit that perfection (or anything close to it) eludes them. Like many other statisticians, they fall back on the excuse that “chance” or “luck” intrudes too often to allow their statistical methods to work their magic. What they won’t admit to themselves is that the results of simulations (such as those employed in the complex model devised by FiveThirtyEight),

reflect the assumptions underlying the authors’ model — not reality. A key assumption is that the model … accounts for all relevant variables….

As I have said, “luck” is mainly an excuse and rarely an explanation. Attributing outcomes to “luck” is an easy way of belittling success when it accrues to a rival.

It is also an easy way of dodging the fact that no model can accurately account for the outcomes of complex systems. “Luck” is the disappointed modeler’s excuse.

If the outcomes of baseball games and seasons could be modeled with great certainly, people wouldn’t bet on those outcomes. The existence of successful models would become general knowledge, and betting would cease, as the small gains that might accrue from betting on narrow odds would be wiped out by vigorish.

Returning now to “global” temperatures, I am unaware of any model that actually tries to account for the myriad factors that influence climate. The pseudo-science of “climate change” began with the assumption that “global” temperatures are driven by human activity, namely the burning of fossil fuels that releases CO2 into the atmosphere. CO2 became the centerpiece of global climate models (GCMs), and everything else became an afterthought, or a non-thought. It is widely acknowledged that cloud formation and cloud cover — obviously important determinants of near-surface temperatures — are treated inadequately (when treated at all). The mechanism by which the oceans absorb heat and transmit it to the atmosphere also remain mysterious. The effect of solar activity on cosmic radiation reaching Earth (and thus on cloud formation) remains is often dismissed despite strong evidence of its importance. Other factors that seem to have little or no weight in GCMs (though they are sometimes estimated in isolation) include plate techtonics, magma flows, volcanic activity, and vegetation.

Despite all of that, builders of GCMs — and the doomsayers who worship them — believe that “global” temperatures will rise to catastrophic readings. The rising oceans will swamp coastal cities; the earth will be scorched. except where it is flooded by massive storms; crops will fail accordingly; tempers will flare and wars will break out more frequently.

There’s just one catch, and it’s a big one. Minute changes in the value of a dependent variable (“global” temperature, in this case) can’t be explained by a model in which key explanatory variables are unaccounted for, about which there is much uncertainty surrounding the values of those explanatory variables that can be accounted for, and about which there is great uncertainty about the mechanisms by which the variables interact. Even an impossibly complete model would be wildly inaccurate given the uncertainty of the interactions among variables and the values of those variables (in the past as well as in the future).

I say “minute changes” because first graph above is grossly misleading. An unbiased depiction of “global” temperatures looks like this:

There’s a much better chance of predicting the success or failure of the Oakland A’s, whose record looks like this on an absolute scale:

Just as no rational (unemotional) person should believe that predictions of “global” temperatures should dictate government spending and regulatory policies, no sane bettor is holding his breath in anticipation that the success or failure of the A’s (or any team) can be predicted with bankable certainty.

All of this illustrates a concept known as causal density, which Arnold Kling explains:

When there are many factors that have an impact on a system, statistical analysis yields unreliable results. Computer simulations give you exquisitely precise unreliable results. Those who run such simulations and call what they do “science” are deceiving themselves.

The folks at FiveThirtyEight are no more (and no less) delusional than the creators of GCMs.

Simple Economic Truths Worth Repeating

From “Keynesian Multiplier: Fiction vs. Fact“:

There are a few economic concepts that are widely cited (if not understood) by non-economists. Certainly, the “law” of supply and demand is one of them. The Keynesian (fiscal) multiplier is another; it is

the ratio of a change in national income to the change in government spending that causes it. More generally, the exogenous spending multiplier is the ratio of a change in national income to any autonomous change in spending (private investment spending, consumer spending, government spending, or spending by foreigners on the country’s exports) that causes it.

The multiplier is usually invoked by pundits and politicians who are anxious to boost government spending as a “cure” for economic downturns. What’s wrong with that? If government spends an extra $1 to employ previously unemployed resources, why won’t that $1 multiply and become $1.50, $1.60, or even $5 worth of additional output?

What’s wrong is the phony math by which the multiplier is derived, and the phony story that was long ago concocted to explain the operation of the multiplier….

To show why the math is phony, I’ll start with a derivation of the multiplier. The derivation begins with the accounting identity Y = C + I + G, which means that total output (Y) = consumption (C) + investment (I) + government spending (G)….

Now, let’s say that b = 0.8. This means that income-earners, on average, will spend 80 percent of their additional income on consumption goods (C), while holding back (saving, S) 20 percent of their additional income. With b = 0.8, k = 1/(1 – 0.8) = 1/0.2 = 5. That is, every $1 of additional spending — let us say additional government spending (∆G) rather than investment spending (∆I) — will yield ∆Y = $5. In short, ∆Y = k(∆G), as a theoretical maximum.

But:

[The multiplier] it isn’t a functional representation — a model — of the dynamics of the economy. Assigning a value to b (the marginal propensity to consume) — even if it’s an empirical value — doesn’t alter that fact that the derivation is nothing more than the manipulation of a non-functional relationship, that is, an accounting identity.

Consider, for example, the equation for converting temperature Celsius (C) to temperature Fahrenheit (F): F = 32 + 1.8C. It follows that an increase of 10 degrees C implies an increase of 18 degrees F. This could be expressed as ∆F/∆C = k* , where k* represents the “Celsius multiplier”. There is no mathematical difference between the derivation of the investment/government-spending multiplier (k) and the derivation of the Celsius multiplier (k*). And yet we know that the Celsius multiplier is nothing more than a tautology; it tells us nothing about how the temperature rises by 10 degrees C or 18 degrees F. It simply tells us that when the temperature rises by 10 degrees C, the equivalent rise in temperature F is 18 degrees. The rise of 10 degrees C doesn’t cause the rise of 18 degrees F.

Therefore:

[T]he Keynesian investment/government-spending multiplier simply tells us that if ∆Y = $5 trillion, and if b = 0.8, then it is a matter of mathematical necessity that ∆C = $4 trillion and ∆I + ∆G = $1 trillion. In other words, a rise in I + G of $1 trillion doesn’t cause a rise in Y of $5 trillion; rather, Y must rise by $5 trillion for C to rise by $4 trillion and I + G to rise by $1 trillion. If there’s a causal relationship between ∆G and ∆Y, the multiplier doesn’t portray it.

In sum, the fiscal multiplier puts the cart before the horse. It begins with a non-functional, mathematical relationship, stipulates a hypothetical increase in GDP, and computes that increase in consumption (and other things) that would occur if that increase were to be realized.

As economist Steve Landsburg explains in “The Landsburg Multiplier: How to Make Everyone Rich”,

Murray Rothbard … observed that the really neat thing about this [fiscal stimulus] argument is that you can do exactly the same thing with any accounting identity. Let’s start with this one:

Y = L + E

Here Y is economy-wide income, L is Landsburg’s income, and E is everyone else’s income. No disputing that one.

Next we observe that everyone else’s share of the income tends to be about 99.999999% of the total. In symbols, we have:

E = .99999999 Y

Combine these two equations, do your algebra, and voila:

Y = 100,000,000

That 100,000,000 there is the soon-to-be-famous “Landsburg multiplier”. Our equation proves that if you send Landsburg a dollar, you’ll generate $100,000,000 worth of income for everyone else.

Send me your dollars, yearning to be free.

Tax cuts may stimulate economic activity, but not nearly to the extent suggested by the multiplier. Moreover, if government spending isn’t reduced at the same time that taxes are cut, and if there is something close to full employment of labor and capital, the main result of a tax cut will be inflation.

Government spending (as shown in “Keynsian Multiplier: Fact vs. Fiction” and “Economic Growth Since World War II“) doesn’t stimulate the economy, and usually has the effect of reducing private consumption and investment. That may be to the liking of big-government worshipers, but it’s bad for most of us.

Has Humanity Reached Peak Intelligence?

That’s the title of a post at BBC Future by David Robson, a journalist who has written a book called The Intelligence Trap: Why Smart People Make Dumb Mistakes. Inasmuch as “humanity” isn’t a collective to which “intelligence” can be attached, the title is more titillating than informative about the substance of the post, wherein Mr. Robson says some sensible things; for example:

When the researcher James Flynn looked at [IQ] scores over the past century, he discovered a steady increase – the equivalent of around three points a decade. Today, that has amounted to 30 points in some countries.

Although the cause of the Flynn effect is still a matter of debate, it must be due to multiple environmental factors rather than a genetic shift.

Perhaps the best comparison is our change in height: we are 11cm (around 5 inches) taller today than in the 19th Century, for instance – but that doesn’t mean our genes have changed; it just means our overall health has changed.

Indeed, some of the same factors may underlie both shifts. Improved medicine, reducing the prevalence of childhood infections, and more nutritious diets, should have helped our bodies to grow taller and our brains to grow smarter, for instance. Some have posited that the increase in IQ might also be due to a reduction of the lead in petrol, which may have stunted cognitive development in the past. The cleaner our fuels, the smarter we became.

This is unlikely to be the complete picture, however, since our societies have also seen enormous shifts in our intellectual environment, which may now train abstract thinking and reasoning from a young age. In education, for instance, most children are taught to think in terms of abstract categories (whether animals are mammals or reptiles, for instance). We also lean on increasingly abstract thinking to cope with modern technology. Just think about a computer and all the symbols you have to recognise and manipulate to do even the simplest task. Growing up immersed in this kind of thinking should allow everyone [hyperbole alert] to cultivate the skills needed to perform well in an IQ test….

[Psychologist Robert Sternberg] is not alone in questioning whether the Flynn effect really represented a profound improvement in our intellectual capacity, however. James Flynn himself has argued that it is probably confined to some specific reasoning skills. In the same way that different physical exercises may build different muscles – without increasing overall “fitness” – we have been exercising certain kinds of abstract thinking, but that hasn’t necessarily improved all cognitive skills equally. And some of those other, less well-cultivated, abilities could be essential for improving the world in the future.

Here comes the best part:

You might assume that the more intelligent you are, the more rational you are, but it’s not quite this simple. While a higher IQ correlates with skills such as numeracy, which is essential to understanding probabilities and weighing up risks, there are still many elements of rational decision making that cannot be accounted for by a lack of intelligence.

Consider the abundant literature on our cognitive biases. Something that is presented as “95% fat-free” sounds healthier than “5% fat”, for instance – a phenomenon known as the framing bias. It is now clear that a high IQ does little to help you avoid this kind of flaw, meaning that even the smartest people can be swayed by misleading messages.

People with high IQs are also just as susceptible to the confirmation bias – our tendency to only consider the information that supports our pre-existing opinions, while ignoring facts that might contradict our views. That’s a serious issue when we start talking about things like politics.

Nor can a high IQ protect you from the sunk cost bias – the tendency to throw more resources into a failing project, even if it would be better to cut your losses – a serious issue in any business. (This was, famously, the bias that led the British and French governments to continue funding Concorde planes, despite increasing evidence that it would be a commercial disaster.)

Highly intelligent people are also not much better at tests of “temporal discounting”, which require you to forgo short-term gains for greater long-term benefits. That’s essential, if you want to ensure your comfort for the future.

Besides a resistance to these kinds of biases, there are also more general critical thinking skills – such as the capacity to challenge your assumptions, identify missing information, and look for alternative explanations for events before drawing conclusions. These are crucial to good thinking, but they do not correlate very strongly with IQ, and do not necessarily come with higher education. One study in the USA found almost no improvement in critical thinking throughout many people’s degrees.

Given these looser correlations, it would make sense that the rise in IQs has not been accompanied by a similarly miraculous improvement in all kinds of decision making.

So much for the bright people who promote and pledge allegiance to socialism and its various manifestations (e.g., the Green New Deal, and Medicare for All). So much for the bright people who suppress speech with which they disagree because it threatens the groupthink that binds them.

Robson, still using “we” inappropriately, also discusses evidence of dysgenic effects in IQ:

Whatever the cause of the Flynn effect, there is evidence that we may have already reached the end of this era – with the rise in IQs stalling and even reversing. If you look at Finland, Norway and Denmark, for instance, the turning point appears to have occurred in the mid-90s, after which average IQs dropped by around 0.2 points a year. That would amount to a seven-point difference between generations.

Psychologist (and intelligence specialist) James Thompson has addressed dysgenic effects at his blog on the website of The Unz Review. In particular, he had a lot to say about the work of an intelligence researcher named Michael Woodley. Here’s a sample from a post by Thompson:

We keep hearing that people are getting brighter, at least as measured by IQ tests. This improvement, called the Flynn Effect, suggests that each generation is brighter than the previous one. This might be due to improved living standards as reflected in better food, better health services, better schools and perhaps, according to some, because of the influence of the internet and computer games. In fact, these improvements in intelligence seem to have been going on for almost a century, and even extend to babies not in school. If this apparent improvement in intelligence is real we should all be much, much brighter than the Victorians.

Although IQ tests are good at picking out the brightest, they are not so good at providing a benchmark of performance. They can show you how you perform relative to people of your age, but because of cultural changes relating to the sorts of problems we have to solve, they are not designed to compare you across different decades with say, your grandparents.

Is there no way to measure changes in intelligence over time on some absolute scale using an instrument that does not change its properties? In the Special Issue on the Flynn Effect of the journal Intelligence Drs Michael Woodley (UK), Jan te Nijenhuis (the Netherlands) and Raegan Murphy (Ireland) have taken a novel approach in answering this question. It has long been known that simple reaction time is faster in brighter people. Reaction times are a reasonable predictor of general intelligence. These researchers have looked back at average reaction times since 1889 and their findings, based on a meta-analysis of 14 studies, are very sobering.

It seems that, far from speeding up, we are slowing down. We now take longer to solve this very simple reaction time “problem”.  This straightforward benchmark suggests that we are getting duller, not brighter. The loss is equivalent to about 14 IQ points since Victorian times.

So, we are duller than the Victorians on this unchanging measure of intelligence. Although our living standards have improved, our minds apparently have not. What has gone wrong?

From a later post:

The Flynn Effect co-exists with the Woodley Effect. Since roughly 1870 the Flynn Effect has been stronger, at an apparent 3 points per decade. The Woodley effect is weaker, at very roughly 1 point per decade. Think of Flynn as the soil fertilizer effect and Woodley as the plant genetics effect. The fertilizer effect seems to be fading away in rich countries, while continuing in poor countries, though not as fast as one would desire. The genetic effect seems to show a persistent gradual fall in underlying ability.

Woodley’s claim is based on a set of papers written since 2013, which have been recently reviewed by [Matthew] Sarraf.

The review is unusual, to say the least. It is rare to read so positive a judgment on a young researcher’s work, and it is extraordinary that one researcher has changed the debate about ability levels across generations, and all this in a few years since starting publishing in psychology.

The table in that review which summarizes the main findings is shown below. As you can see, the range of effects is very variable, so my rough estimate of 1 point per decade is a stab at calculating a median. It is certainly less than the Flynn Effect in the 20th Century, though it may now be part of the reason for the falling of that effect, now often referred to as a “negative Flynn effect”….

Here are the findings which I have arranged by generational decline (taken as 25 years).

  • Colour acuity, over 20 years (0.8 generation) 3.5 drop/decade.
  • 3D rotation ability, over 37 years (1.5 generations) 4.8 drop/decade.
  • Reaction times, females only, over 40 years (1.6 generations) 1.8 drop/decade.
  • Working memory, over 85 years (3.4 generations) 0.16 drop/decade.
  • Reaction times, over 120 years (4.8 generations) 0.57-1.21 drop/decade.
  • Fluctuating asymmetry, over 160 years (6.4 generations) 0.16 drop/decade.

Either the measures are considerably different, and do not tap the same underlying loss of mental ability, or the drop is unlikely to be caused by dysgenic decrements from one generation to another. Bar massive dying out of populations, changes do not come about so fast from one generation to the next. The drops in ability are real, but the reason for the falls are less clear. Gathering more data sets would probably clarify the picture, and there is certainly cause to argue that on various real measures there have been drops in ability. Whether this is dysgenics or some other insidious cause is not yet clear to me.

My view is that whereas formerly the debate was only about the apparent rise in ability, discussions are now about the co-occurrence of two trends: the slowing down of the environmental gains and the apparent loss of genetic quality. In the way that James Flynn identified an environmental/cultural effect, Michael Woodley has identified a possible genetic effect, and certainly shown that on some measures we are doing less well than our ancestors.

How will they be reconciled? Time will tell, but here is a prediction. I think that the Flynn effect will fade in wealthy countries, persist with fading effect in poor countries, and that the Woodley effect will continue, though I do not know the cause of it.

Here’s my hypothesis, which I offer on the assumption that the test-takers are demographically representative of the whole populations of the countries in which they were tested: The less-intelligent portions of the populace are breeding faster than the more-intelligent portions. That phenomenon is magnified by the rapid growth of the Muslim component of Europe’s population and the rapid growth of the Latino component of America’s population.

(See also “The Learning Curve and the Flynn Effect“, “More about Intelligence“, “Selected Writings about Intelligence“, and especially “Intelligence“.)

Ad-Hoc Hypothesizing and Data Mining

An ad-hoc hypothesis is

a hypothesis added to a theory in order to save it from being falsified….

Scientists are often skeptical of theories that rely on frequent, unsupported adjustments to sustain them. This is because, if a theorist so chooses, there is no limit to the number of ad hoc hypotheses that they could add. Thus the theory becomes more and more complex, but is never falsified. This is often at a cost to the theory’s predictive power, however. Ad hoc hypotheses are often characteristic of pseudoscientific subjects.

An ad-hoc hypothesis can also be formed from an existing hypothesis (a proposition that hasn’t yet risen to the level of a theory) when the existing hypothesis has been falsified or is in danger of falsification. The (intellectually dishonest) proponents of the existing hypothesis seek to protect it from falsification by putting the burden of proof on the doubters rather than where it belongs, namely, on the proponents.

Data mining is “the process of discovering patterns in large data sets”. It isn’t hard to imagine the abuses that are endemic to data mining; for example, running regressions on the data until the “correct” equation is found, and excluding or adjusting portions of the data because their use leads to “counterintuitive” results.

Ad-hoc hypothesizing and data mining are two sides of the same coin: intellectual dishonesty. The former is overt; the latter is covert. (At least, it is covert until someone gets hold of the data and the analysis, which is why many “scientists” and “scientific” journals have taken to hiding the data and obscuring the analysis.) Both methods are justified (wrongly) as being consistent with the scientific method. But the ad-hoc theorizer is just trying to rescue a falsified hypothesis, and the data miner is just trying to conceal information that would falsify his hypothesis.

From what I have seen, the proponents of the human activity>CO2>”global warming” hypothesis have been guilty of both kinds of quackery: ad-hoc hypothesizing and data mining (with a lot of data manipulation thrown in for good measure).

The Learning Curve and the Flynn Effect

UPDATED 09/14/19

I first learned of the learning curve when I was a newly hired analyst at a defense think-tank. A learning curve

is a graphical representation of how an increase in learning (measured on the vertical axis) comes from greater experience (the horizontal axis); or how the more someone (or something) performs a task, the better they [sic] get at it.

In my line of work, the learning curve figured importantly in the estimation of aircraft procurement costs. There was a robust statistical relationship between the cost of making a particular model of aircraft and the cumulative number of such aircraft produced. Armed with the learning-curve equation and the initial production cost of an aircraft, it was easy to estimate of the cost of producing any number of the same aircraft.

The learning curve figures prominently in tests that purport to measure intelligence. Two factors that may explain the Flynn effect — a secular rise in average IQ scores — are aspects of learning: schooling and test familiarity and a generally more stimulating environment in which one learns more. The Flynn effect doesn’t measure changes in intelligence, it measures changes in IQ scores resulting from learning. There is an essential difference between ignorance and stupidity. The Flynn effect is about the former, not the latter.

Here’s a personal example of the Flynn effect in action. I’ve been doing The New York Times crossword puzzle online since February 18 of this year. I have completed all 210 puzzles published by TNYT from that date through the puzzle for September 15, with generally increasing ease:

The difficulty of the puzzle varies from day to day, with Monday puzzles being the easiest and Sunday puzzles being the hardest (as measured by time to complete). For each day of the week, my best time is more recent than my worst time, and the trend of time to complete is downward for every day of the week (as reflected in the graph above). In fact, in the past week I tied my best time for a Monday puzzle and set new bests for the Thursday and Friday puzzles.

I know that that I haven’t become more intelligent in the last 30 weeks. And being several decades past the peak of my intelligence, I am certain that it diminishes daily, though only fractionally so (I hope). I have simply become more practiced at doing the crossword puzzle because I have learned a lot about it. For example, certain clues recur with some frequency, and they always have the same answers. Clues often have double meanings, which were hard to decipher at first, but which have become easier to decipher with practice. There are other subtleties, all of which reflect the advantages of learning.

In a nutshell, I am no smarter than I was 30 weeks ago, but my ignorance of TNYT crossword puzzle has diminished significantly.

(See also “More about Intelligence“, “Selected Writings about Intelligence“, and especially “Intelligence“, in which I quote experts about the Flynn Effect.)

Modeling Is Not Science: Another Demonstration

The title of this post is an allusion to an earlier one: “Modeling Is Not Science“. This post addresses a model that is the antithesis of science. Tt seems to have been extracted from the ether. It doesn’t prove what its authors claim for it. It proves nothing, in fact, but the ability of some people to dazzle other people with mathematics.

In this case, a writer for MIT Technology Review waxes enthusiastic about

the work of Alessandro Pluchino at the University of Catania in Italy and a couple of colleagues. These guys [sic] have created a computer model of human talent and the way people use it to exploit opportunities in life. The model allows the team to study the role of chance in this process.

The results are something of an eye-opener. Their simulations accurately reproduce the wealth distribution in the real world. But the wealthiest individuals are not the most talented (although they must have a certain level of talent). They are the luckiest. And this has significant implications for the way societies can optimize the returns they get for investments in everything from business to science.

Pluchino and co’s [sic] model is straightforward. It consists of N people, each with a certain level of talent (skill, intelligence, ability, and so on). This talent is distributed normally around some average level, with some standard deviation. So some people are more talented than average and some are less so, but nobody is orders of magnitude more talented than anybody else….

The computer model charts each individual through a working life of 40 years. During this time, the individuals experience lucky events that they can exploit to increase their wealth if they are talented enough.

However, they also experience unlucky events that reduce their wealth. These events occur at random.

At the end of the 40 years, Pluchino and co rank the individuals by wealth and study the characteristics of the most successful. They also calculate the wealth distribution. They then repeat the simulation many times to check the robustness of the outcome.

When the team rank individuals by wealth, the distribution is exactly like that seen in real-world societies. “The ‘80-20’ rule is respected, since 80 percent of the population owns only 20 percent of the total capital, while the remaining 20 percent owns 80 percent of the same capital,” report Pluchino and co.

That may not be surprising or unfair if the wealthiest 20 percent turn out to be the most talented. But that isn’t what happens. The wealthiest individuals are typically not the most talented or anywhere near it. “The maximum success never coincides with the maximum talent, and vice-versa,” say the researchers.

So if not talent, what other factor causes this skewed wealth distribution? “Our simulation clearly shows that such a factor is just pure luck,” say Pluchino and co.

The team shows this by ranking individuals according to the number of lucky and unlucky events they experience throughout their 40-year careers. “It is evident that the most successful individuals are also the luckiest ones,” they say. “And the less successful individuals are also the unluckiest ones.”

The writer, who is dazzled by pseudo-science, gives away his Obamanomic bias (“you didn’t build that“) by invoking fairness. Luck and fairness have nothing to do with each other. Luck is luck, and it doesn’t make the beneficiary any less deserving of the talent, or legally obtained income or wealth, that comes his way.

In any event, the model in question is junk. To call it junk science would be to imply that it’s just bad science. But it isn’t science; it’s a model pulled out of thin air. The modelers admit this in the article cited by the Technology Review writer, “Talent vs. Luck, the Role of Randomness in Success and Failure“:

In what follows we propose an agent-based model, called “Talent vs Luck” (TvL) model, which builds on a small set of very simple assumptions, aiming to describe the evolution of careers of a group of people influenced by lucky or unlucky random events.

We consider N individuals, with talent Ti (intelligence, skills, ability, etc.) normally distributed in the interval [0; 1] around a given mean mT with a standard deviation T , randomly placed in xed positions within a square world (see Figure 1) with periodic boundary conditions (i.e. with a toroidal topology) and surrounded by a certain number NE of “moving” events (indicated by dots), someone lucky, someone else unlucky (neutral events are not considered in the model, since they have not relevant effects on the individual life). In Figure 1 we report these events as colored points: lucky ones, in green and with relative percentage pL, and unlucky ones, in red and with percentage (100􀀀pL). The total number of event-points NE are uniformly distributed, but of course such a distribution would be perfectly uniform only for NE ! 1. In our simulations, typically will be NE N=2: thus, at the beginning of each simulation, there will be a greater random concentration of lucky or unlucky event-points in different areas of the world, while other areas will be more neutral. The further random movement of the points inside the square lattice, the world, does not change this fundamental features of the model, which exposes dierent individuals to dierent amount of lucky or unlucky events during their life, regardless of their own talent.

In other words, this is a simplistic, completely abstract model set in a simplistic, completely abstract world, using only the authors’ assumptions about the values of a small number of abstract variables and the effects of their interactions. Those variables are “talent” and two kinds of event: “lucky” and “unlucky”.

What could be further from science — actual knowledge — than that? The authors effectively admit the model’s complete lack of realism when they describe “talent”:

[B]y the term “talent” we broadly mean intelligence, skill, smartness, stubbornness, determination, hard work, risk taking and so on.

Think of all of the ways that those various — and critical — attributes vary from person to person. “Talent”, in other words, subsumes an array of mostly unmeasured and unmeasurable attributes, without distinguishing among them or attempting to weight them. The authors might as well have called the variable “sex appeal” or “body odor”. For that matter, given the complete abstractness of the model, they might as well have called its three variables “body mass index”, “elevation”, and “race”.

It’s obvious that the model doesn’t account for the actual means by which wealth is acquired. In the model, wealth is just the mathematical result of simulated interactions among an arbitrarily named set of variables. It’s not even a multiple regression model based on statistics. (Although no set of statistics could capture the authors’ broad conception of “talent”.)

The modelers seem surprised that wealth isn’t normally distributed. But that wouldn’t be a surprise if they were to consider that wealth represents a compounding effect, which naturally favors those with higher incomes over those with lower incomes. But they don’t even try to model income.

So when wealth (as modeled) doesn’t align with “talent”, the discrepancy — according to the modelers — must be assigned to “luck”. But a model that lacks any nuance in its definition of variables, any empirical estimates of their values, and any explanation of the relationship between income and wealth cannot possibly tell us anything about the role of luck in the determination of wealth.

At any rate, it is meaningless to say that the model is valid because its results mimic the distribution of wealth in the real world. The model itself is meaningless, so any resemblance between its results and the real world is coincidental (“lucky”) or, more likely, contrived to resemble something like the distribution of wealth in the real world. On that score, the authors are suitably vague about the actual distribution, pointing instead to various estimates.

(See also “Modeling, Science, and Physics Envy” and “Modeling Revisited“.)

Consulting

There is a post at Politico about the adventures of McKinsey & Company, a giant consulting firm, in the world of intelligence:

America’s vast spying apparatus was built around a Cold War world of dead drops and double agents. Today, that world has fractured and migrated online, with hackers and rogue terrorist cells, leaving intelligence operatives scrambling to keep up.

So intelligence agencies did what countless other government offices have done: They brought in a consultant. For the past four years, the powerhouse firm McKinsey and Co., has helped restructure the country’s spying bureaucracy, aiming to improve response time and smooth communication.

Instead, according to nearly a dozen current and former officials who either witnessed the restructuring firsthand or are familiar with the project, the multimillion dollar overhaul has left many within the country’s intelligence agencies demoralized and less effective.

These insiders said the efforts have hindered decision-making at key agencies — including the CIA, National Security Agency and the Office of the Director of National Intelligence.

They said McKinsey helped complicate a well-established linear chain of command, slowing down projects and turnaround time, and applied cookie-cutter solutions to agencies with unique cultures. In the process, numerous employees have become dismayed, saying the efforts have at best been a waste of money and, at worst, made their jobs more difficult. It’s unclear how much McKinsey was paid in that stretch, but according to news reports and people familiar with the effort, the total exceeded $10 million.

Consulting to U.S.-government agencies on a grand scale grew out of the perceived successes in World War II of civilian analysts who were embedded in military organizations. To the extent that the civilian analysts were actually helpful*, it was because they focused on specific operations, such as methods of searching for enemy submarines. In such cases, the government client can benefit from an outside look at the effectiveness of the operations, the identification of failure points, and suggestions for changes in weapons and tactics that are informed by first-hand observation of military operations.

Beyond that, however, outsiders are of little help, and may be a hindrance, as in the case cited above. Outsiders can’t really grasp the dynamics and unwritten rules of organizational cultures that embed decades of learning and adaptation.

The consulting game is now (and has been for decades) an invasive species. It is a perverse outgrowth of operations research as it was developed in World War II. Too much of a “good thing” is a bad thing — as I saw for myself many years ago.
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* The success of the U.S. Navy’s antisubmarine warfare (ASW) operations had been for decades ascribed to the pioneering civilian organization known as the Antisubmarine Warfare Operations Research Group (ASWORG). However, with the publication of The Ultra Secret in 1974 (and subsequent revelations), it became known that code-breaking may have contributed greatly to the success of various operations against enemy forces, including ASW.