I count states I have visited through electoral votes, and am happy to say that New Jersey has joined the landslide, thanks to a fun side-trip to Hoboken (and La Isla Cuban restaurant)
(Also, why doesn’t the PATH train from New Jersey continue to Grand Central? Am I the first person to wonder this?)
Occasionally, you will encounter someone who says this:
Variation between human races is greater than variation within human races
If you do, you know you’ve encountered someone who has been indirectly exposed to the work of Richard Lewontin.
There are two forms of “Lewontin’s fallacy.” One is the original claim that Lewontin made. It is demonstrably untrue, which is obvious once examined with graduate-level statistical knowledge. A later, weaker version is simply nonsensical. I’ll address these in order.
Lewontin’s Original Fallacy
In 1972, Lewontin published an article called The apportionment of human diversity, using blood group proteins. The work is pretty typical for its time, except it extremely political correct connotations, and so eventually took on a life of its own. Rather than discuss the original article, which has been thoroughly debunked, bizarrely focuses on blood proteins anyway, here’s an analogy. (I’m too tired to do matrix algebra now.)
Say someone comes to you, and says this:
“The racial groups that map to what we consider ‘East Asian’ or ‘Caucasian’ do not exist. There is no attribute of either race you can find, in which the majority of variation is between races, rather than within races. Hair, skin tone, skeletal shape, and so on all vary within both populations, so that means there is only one population.
In other words, the groups “East Asian” and “Caucasian” are entirely social groups. It is impossible to write a machine learning system to tell an East Asian apart from a European, if you don’t include purely social constructs like name, clothing style, and so on.
The obvious refutation (which mathematically requires matrix algebra) is to ask why in the world you would use only one dimension of variation (like height, or skin tone) to classify individuals as part of multiple populations.
You can just use multiple indicators, together. That way if there has been a murder, say, and the corpse has been stripped of clothing and identification and has been dumped, you can use multiple indicators together to determine the race of the victim.
If there is DNA evidence, you can do the same.
Indeed, you can do the same with “races” such as “German” and “French”!
If for some reason you’re transported back to the 1970s, and all you have is blood proteins, you can do the same.
The solution to Lewontin’s fallacy is to use multiple indicators together, and not just one.
These days, it seems crazy to suggest it would be impossible to tell the race of an individual from DNA. There’s even a popular PBS show about the concept! But in the 1970s, some people really were that ignorant.
The Remnant that Remains
There’s no reason to take Lewontin’s original fallacy seriously, but sometimes you’ll hear a variation of it
Variation in intelligence between human races is greater than the mean difference of intelligence of the races
This is like saying moisture is taller than speed. It makes no sense.
In some areas of life, differences in variation between groups is the fact that matters most. For instance, on many measures (say IQ, or time orientation) males have greater variation than females, while both tend to have the same average. From this you would expect you would see many more male violent criminals than female violent criminals, and also more male CEOs of large companies than female CEOs of large companies. There is little if any difference in the average of these traits between the sexes. There is substantial difference in the variation of these traits between the sexes, though.
In other areas, averages matter. For instance, the average IQ of American whites from the south-eastern United States is lower than the average IQ of American whites from the northern states. From this you might wonder if large companies have a disproportionately small number of CEOs from the American South, while white southerners have responded to this “dixie ceiling” by organizing politically to obtain political goods that they cannot gain in the marketplace.
I have never seen anyone talk, in a popular setting, about a comparison between a variation on the one hand and an average on the other. Typically one or the other is relevant to the conversation, and bizarre second-order comparisons (what is the variability in height of Australians compared t the average height of South Americans) are simply uninterpretable. But if you’ve never worked with variation as a real thing (through calculating a standard deviation to solve a problem, say), the remnant of the fallacy is a good-guess by an ignorant laymen of what Lewontin may have been talking about.
Conclusion
The phrase “Variation between human races is greater than variation within human races” is meaningless. It either refers to an empirical incorrect claim from the 1970s, on the impossibility of using “blood proteins” to predict race, or an incoherent claim that compares averages against variation.
In 2008, I noted the “quantitative revolution,” which replaced the romantic academia many dream about with a discovery factory:
Both the old Academy and the Leftists, however, are under even more heartless attack from the Quantitative Revolution, the measurement-and-control movement that subjects everything to test-and-reject, measure-and-fund, quantitative certainties.
The romantic academia that lives in our heart is dying or dead. Given a future between the Tyranny of Leftists and the Tyranny of the Quantitative Revolutions, my sympathies go to the quantitativists. They save what can be saved, submitting the universities to Research, Application, and funded Goals.
Last year, I formalized that description of how academia works:
Professors, like most people, respond to the incentives of power, influence, and money.
The institution of tenure reduces uncertainty regarding money, and focuses the incentives on power and influence.
Power in academia comes from the number of bodies a professor has under him. These bodies might be apprentices (graduate students he advises), journeymen (post-docs who have a PhD and work at the lab, or staff researchers), or simple workers (lab technicians, etc).
Influence in academia comes from the extent to which one is successful in influencing one’s peers. This is typically measured in terms of influence scores, which are a product of how often the academic is cited, weighted by how important of a publication he is cited in.
The best route to both power and influence is to earn grant money.
Daniel Allington must read my blog, as he writes the same thing:
Even among successful players of the funding game – and certain digital humanists have been very successful players, of late – one may find disquiet at the game itself, at the disproportionate importance now attached to it, and at the negative impact it is having on the careers of new researchers and (in the long term) on access to the profession as a whole by accelerating the casualization of both teaching and research. The underlying problem – regretted by practically everyone with a genuine love of scholarship – is the ongoing reconstruction of all disciplines on the social model of the natural sciences and the creeping abandonment of ‘autonomy’ (in the sense used by Bourdieu, 1993 [1987]) in the academic field through tacit acceptance of the principle – shared by university administrators, government ministers, and hiring committees alike – that knowledge can and should be valued primarily for its moneymaking potential. In
Allington is particular worried about the “digital humanities” which provides a road out of the ghetto for humanities students. Allington criticizes this as revealing “the corrupting agenda of our paymasters” — apparently he sees nothing wrong with the old boys network that progress-based research replaces.
Everyone else (Daniel Drezner, Joshua Rothman, Rebecca Schuman, Katie Roiphe, Annemarie, tressiemc, etc) is making pretty obvious points about graduate schools and becoming famous for it.
Here are some actually useful questions to determine if you should go.
1. Do you want to go?
-> If No, DO NOT GO.
-> If Yes, read on.
2. Are you thinking about being a lawyer, medical doctor, or nurse?
-> If Yes, that’s not what I’m talking about. Read this blog instead.
-> If No, read on.
3. Is the degree you’re thinking of a STEM (Science, TEchnology Engineering, or Math) degree?
-> If Yes, GO. Start filling out applications. You’re done.
-> If No, read on.
4. Can you get a job even if your graduate degree is worthless?
-> If Yes, GO. Start filling out applications. You’re done.
-> If No, DON’T GO.
There, that saved you a lot of time.
The reason this quesitonaire is so short is that this is a good approximation of success.
1) Do something you enjoy, AND
2) Do something you can get paid for doing, AND
3) Do something you are great at
(3) will come with practice, which you will have plenty of it’s (1) and (2).
The lessons we should learn from all
the fighting in the Days of Old
when Providence bestowed Divine,
the Sanctuary purified:“Let the let encircle all you hold
and don’t uproot the olive grove.”So now Jerusalem, you know it’s not right
After all you’ve been through, you should know better than
To become the wicked ones
Almighty God once saved you from.”- Mirah Yom Tov Zeitlyn, “Jersualem“
It is wrong to use violence against people who are living peacefully. Sometimes we have to, because we don’t know ways to solve our problems that don’t involve being violent to peaceful people, but that assault remains wrong.
Likewise, it is dangerous to take actions that have unknowable costs. Sometimes we do so anyway, because the alternative is so wrong or is itself dangerous, but those excuses do not erase the danger that we introduce.
Finally, it is chaotic to introduce changes against the wishes of a democratic majority. This is does not mean doing so is necessarily wrong or dangerous, but it randomizes the purpose of elections (which are to allow the people to fire officials who they find unbearable), generates annoying social movements, and distracts the broader society from the more important goals of economic growth.
In Washington State, where I live, the people directly voted on, and approved, laws to legalize both marijuana and gay marriages. These are certainly dangerous (and gay marriage more so, as for most of American history marijuana was legal). But banning the right to contract in both cases is certainly wrong. Fortunately, Washington State’s legalization of both forms of contract was orderly, without judicial fiat or even legislatorial arrogant bullying the process.
In California, on the other hand, unelected judges took the dangerous and chaotic path of legalizing gay marriage (but not marijuana) by fiat. One wrong was undone — the one that was the most dangerous — but in a chaotic way.
This case is now before the Supreme Court. The nearest analogy I can think of is Roe v. Wade, which likewise was a dangerous and chaotic method of abolishing a wrong (violent persecution of post-conception birth control). Of course, Roe v. Wade also legalized another wrong, infanticide, so it is unlikely that the dangerous chaos ensuing from even a reckless ruling on the California gay marriage case will be as bad as what was caused by the Roe v. Wade decision.
My friend Adam Elkus recently asked what made Structural Equation Modeling (SEM) powerful for testing theories, besides the ability to test for null results.
This is my answer.
What I like about SEM is that it allows models to be created that better reflect theories than any other method I know. Other methods introduce a greater source of unmeasurable error — model error — than SEM, because those methods force you take your theories, translate them into another form, and then test those.
Take the example theory (which is crazy):
“While democracy exhibits substantial inertia — more democratic places stay more democratic, less democratic places stay less democratic — communication technology forces us to reshape our understandings of how democracy grows or declines. Within any community, the growth or decline in the strength of democratic institutions mediates outside international pressure entirely through smartphone connectivity.
“By strength of democratic institutions I mean such tings as average turn-over of political offices, number of political questions per year voters are asked to consider, the percentage of major editorials that our critical of government policy. By international pressure I mean UN resolutions that mention a country, statements by foreign ministers that reference a country, and number of applications for McDonalds franchise that were rejected. By smartphone connectivity I mean the fraction of the population that has smart-phones, the average number of web impressions per person to Wikipedia, and the average number of hours per day individuals spend playing Angry Birds.”
OK, let’s create the SEM for it. The generic measurement model for democracy, for time0 and time1 is (converted to a pseudo-Mplus language)
LATENT democracy0 (float);
MANIFEST democracy0 ONTO politicalTurnover0 (float); // [0...1]
MANIFEST democracy0 ONTO politicalQuestions0 (int); // [0...n]
MANIFEST democracy0 ONTO criticalEditorials0 (int); // [0..n]LATENT democracy1 (float);
MANIFEST democracy1 ONTO politicalTurnover0 (float); // [0...1]
MANIFEST democracy1 ONTO politicalQuestions0 (int); // [0...n]
MANIFEST democracy1 ONTO criticalEditorials0 (int); // [0..n]// … And
LATENT smartphoneConnectivity (float);
MANFIEST smartphoneConnectivity ONTO ownershipRate (float); // [0...1]
MANIFEST smartphoneConnectivity ONTO wikipediaRate (float); // [0...n]
MANIFEST smartphoneConnectivity ONTO angryBirds(float); // [0...24]// AND
LATENT internationalPressure (float);
MANIFEST internationalPressure ONTO unResolutions(int); // [0...n]
MANIFEST internationalPressure ONTO fmCriticisms(int); // [0...n]
MANIFEST internationalPressure ONTO mcRejections(int); // [0...n]// no time0, because we’re assuming that smartphoneConnectivity0 completely mediates the change in democratic trajectory, that isn’t the result of inertia
// OK, now we’d create our latent model
democracy0 LOADS ON democracy1; // the inertia of democracy
internationalPressure LOADS ONTO smartphoneConnectivity; // … smartphones mediate international pressure..
smartphoneConnectivity LOADS ONTO democracy1; // .. onto democracy
Because SEM allows us to so faithfully translate the model of our theories into the model of code, we now have a serious question that isn’t obvious from the paragraph, but is more obvious when we’re in he process of writing down
Because you see your theory in “code” (or matrices if you insist on the algebraic way to do this, which I’ve only used for class), it makes assumptions or mistake jump our more. Like in the first draft of this email I said that smartphones mediate onto democracy, but I didn’t define what they mediated — hence the inclusion of international Pressure.
Using graphviz/dot, here is a picture of what our model “looks” like:
(A more common format is for a left to right flow of manifest predictor indicators, latent predictor factors, latent outcome factors, and manifest outcome indicators. The format above is chosen to fit well on my blog page, and impatience in taking time to make it look better.)
As I said, the theory’s crazy — but SEM allows that theory to be translated into a model that can be directly tested, and frees us from having to waste our time with hacks like ANOVA, multiple regression, or dead theory disconnected from reality.
The Servants of Strategy
The humanities and the Sciences are siblings. Both serve Strategy. Graduates from the Sciences can usefully serve Strategy to the extent they understand the tools of prediction and control: improvement, and are not distracted by non-normal, revolutionary science. Graduates from the Humanities can usefully serve Strategy to the extend they understand the tools of understanding and explanation, and are not distracted by critical political agendas.
Why We Do What We Do
The purpose of Science is to “predict, control, and improve” phenomena. The sort of phenomenon that is being predicted (at a minimum), controlled (one would hope), and improved (ideally) tells you what sort of Science you are in. Cognitive Psychology focuses on cognitive behavior, “Behavioral” Psychology focuses on overt physical behavior, High-energy physics focuses on the behavior of matter under high energy conditions, and so on.
The purpose of the Humanities is to “understand, explain, and improve” phenomena. The sort of phenomenon that is being understood (at a minimum), explained (one would hope), and improved (ideally) tells you what sort of Humanities you are in. English Literature focuses on the written works of the English language, Geography on the nature of space, Anthropology on the nature of communities and so on.
The purpose of strategy is to “understand, control, and improve” phenomena. The sort of phenomenon that is being understood (at a minimum), controlled (one would hope), and improved (ideally) tells you what sort of Policy you are making. Political Strategy focuses on using political influence to obtain and hold offices. Business Strategy focuses on devoting capital and labor to earning a profit. Military Strategy focuses on using violence to achieve political outcomes.
A Division of Labor
These partially-overlapping purposes make a division of labor sensible. While strategists need to understand phenomenon, they do not need to be able to explain it, thus they can rely on the explanations of others. Likewise, strategists need to control phenomenon, but they do not need to be able to predict it, thus they can rely on the models and planning of others.
Those in the Sciences are useful to the extent they master the tools of prediction and control: tight exemplars, methodology, measurement, and statistics. Those in the Sciences can become useless by being distracted with revolutionary science.
Those in the Humanities are useful to the extent they master the tools of understanding and explanation, which largely overlaps with the “digital humanities.” Those in the Humanities can become useless by being distracted with political agendas.
Political Agendas, Like Revolutionary Science…
I’ve written a lot about revolutionary science, so instead I’ll focus on the danger of political agendas in the Humanities. Recently, there have been three articles on the humanities. Michael Berube‘s thoughtful “The Humanities, Unruffled,” Razib Khan‘s philippic Against the Cultural Anthropologists,” Graeme Wood‘s interesting Anthropology, Inc.,” and Megan McArdle‘s stupid “What’s the Use of the PhD?.” In different ways, these four articles all focus on the same two problems:
1. What is the way to ensure that the Humanities PhD fulfills its function of understanding, explaining, and improving society
2. Does “improving” imply a pragmatic or a political objective?
These two questions are interwoven. A pragmatic Humanities ensures jobs for graduates to informing policy-makers, a pragmatic Humanities is fruitful and useful. But a political humanities that focuses on “race studies,” “gender studies,” and so on is simply a predator and parasite on academia, using academic resources to achieve a political objective. Megan McArdle’s post is prety dumb — it’s on the same level of intellectualism as an Afghan hick who dismisses astronomy by saying — but both she and Khan are reacting against the entrenched leftism of the humanities.
What You Do
It’s possible to have a fascinating, rewarding, and fun career in the Sciences or in the Humanities, in academia, in non-profits, government, or in business. Both the Humanities and the Sciences understand the same world, and their purposes overlap in their call to improve the world. How well you learn the tools and avoid the pitfalls of fulfilling these purposes can matter a lot.
What is Overqualification?
Wikipedia defines overqualification as “the state of being skilled or educated beyond what is necessary for a job.” Unfortunately, the Wikipedia article focuses on negative aspects of overqualification, and why employers will deliberately avoid hiring overqualified employees.
Elite Employers May Prefer Hire Overqualified Employees
Employers — especially elite employers — will often intentionally hire overqualified candidates. The phenomenon is entirely overlooked in the PhD article, but is real and related to Thomas P.M. Barnett’s concept of a “cannibalizing agent.” As Barnett wrote in The Pentagon’s New Map
But it is in the security realm where the adjustment will be the hardest and take the longest, because it takes years — even decades — to raise new generations of military leaders and construct new force structures that match the perceived changes in the security environment. Unless, of course, you have a cannibalizing agent already in place, like a Special Operations Command. But cannibalizing agents do not become ascendant unless dramatically new rule sets are recognized as coming to the fore. When those new rule sets are recognized and given credence, we begin to understand…
Overqualified employees are destabilizers, because they are likely to be able to see through existing processes and procedures, introduce instability by lobbying for changes, and create ad hoc processes that more efficiently fulfill dashboard-level Key Performance Indicators.
Thus, elite employers will purposefully choose to hire overqualified employees when the employers believe the following
1) It is important to raise up a home-grown cadre of knowledgeable experts quickly
2) The short-term costs of over-qualification (distruption to internal business processes) are neglibible compared to potential costs
3) The employer has the ability to recognize “new rule sets” as they come to the fore, and thus recognize those overqualified employees who have already successfully been cannibalizing internal processes
A Graduate Student Seeking Elite Employment Misses the Point If He Complains about Overqualification
I saw all of this because of the recent online discussion started by Greg Ferenstein in his Atlantic piece: Former Political Scientist to Congress: Please Defund Political Science and Adam Elkus‘s response, “Relevant to Policy.” Ferenstein’s piece is an anguished good-bye to political science from someone who wanted an elite position, and saw only meaninglessness in his PhD training. As Ferenstein wrote: “After four years of desperately searching in vain for how my degree could make the world a better place, the lack of real-world impact convinced me to leave a Ph.D. program in political science.” Elkus calls parts of Ferentstein’s piece “ridiculous,” but the human pain that Ferenstein feels isn’t. After four years, Ferenstein realized he was being trained to be overqualified. Then he gave up.
Now, Greg Ferenstein certainly is not your typical grad school drop-out. If nothing else, Greg got his gripes published in The Atlantic, indicating some political operative skills. But note that Ferenstein fails the three-prong test for hiring an overqualified employee.
Specifically,
1) Ferenstein was not able to achieve his own goals in graduate school quickly (in that he ran out of time on his own schedule)
2) Ferenstein may be a high-cost employee (as he has demonstrated a preference to drop out when he is overqualified)
3) Ferenstein has not successfully “cannibalized” the graduate school experience by subverting it to his own ends (thus is less likely to do so in the future)
I don’t mean this to be a hit-piece on Ferenstein. I was briefly enrolled in a Political Science graduate program, which I left to earn my PhD in educational Psychology. I then took a position as a junior user researcher at my employer.
Overqualification Is Endemic to Elite Systems
The overqualification that Ferenstein complains about is not likely to go away. Even if some sort of practical degree (say a Masters of Arts in Government) may be more appropriate to a potential employee’s day to day tasks, and even if the typical value of a PhD is small, elite employers are looking for atypical candidates who are ambitious and have demonstrated overcoming obstacles that are both intensive (require a broad array of skills) and extensive (takes a long time). Elite employers will reward potential employees with cash, influence, or other benefits to generate the needed number of actual employees, which of course leaves many others outside the gates.
There’s much that’s debatable in Ferenstein’s original article, but its heart is a young man’s realization that he has been led for years to be overqualified for the sort of positions that he could actually find. He’s correct in his realization. He just displays an ignorance of why he was lead to such a state.
Bill Gates, the co-founder of the company I work for and a personal hero of mine, has an op-ed in the Wall Street Journal titled “My plan to fight the world’s biggest problems.” It’s an exciting piece because it ties together several of my recent posts very well.
Science allows us to predict, control, and improve variation in the world. In order to actually make progress to these goals, it’s important to establish exemplars of great work. This is enabled through operational definitions that allow concepts to be measured. The quest for progress in science collapses when measurement becomes too difficult tor too expensive.
But the reverse is also true: progress in science begins when measurement becomes accessible.
Bill Gates’ op-ed is so awesome because he brings us back to the real world. When someone says “science,” others thinks of some cartoon view of men in white coats in a laboratory. When someone says that goal of science is the prediction, improvement, and control of variation, someone else will say that such is a “very narrow definition of science, downgrading as it does understanding and explanation.”
But the person who writes you write like Bill Gates does — who never even bother with the word “science” and hammers in that improvements are real:
Such measuring tools, Mr. Rosen writes, allowed inventors to see if their incremental design changes led to the improvements—such as higher power and less coal consumption—needed to build better engines. There’s a larger lesson here: Without feedback from precise measurement, Mr. Rosen writes, invention is “doomed to be rare and erratic.” With it, invention becomes “commonplace.”
In the past year, I have been struck by how important measurement is to improving the human condition. You can achieve incredible progress if you set a clear goal and find a measure that will drive progress toward that goal—in a feedback loop similar to the one Mr. Rosen describes.
This may seem basic, but it is amazing how often it is not done and how hard it is to get right. Historically, foreign aid has been measured in terms of the total amount of money invested—and during the Cold War, by whether a country stayed on our side—but not by how well it performed in actually helping people. Closer to home, despite innovation in measuring teacher performance world-wide, more than 90% of educators in the U.S. still get zero feedback on how to improve.
An innovation—whether it’s a new vaccine or an improved seed—can’t have an impact unless it reaches the people who will benefit from it. We need innovations in measurement to find new, effective ways to deliver those tools and services to the clinics, family farms and classrooms that need them.
… that’s the sort of person who can make a difference. The theory of science, measurement, and improvement are all left below the surface. What is left is a how-to guide to build a better world.
I write this blog for selfish reasons, I enjoy learning about the world. Bill Gates does what he’s doing to change the world.
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