Category Archives: Science

Ways of Science

Imagine if I told you, we should trust corporate CEOs, or politicians, or public school teachers to be “self-correcting.” Imagine if I said that because they have similar degrees from similar institutions, they alone should judge their own, and they alone will correct any flaws or mistakes that appear.

You would say I’m insane.

Or, more generously, you say that such a blind faith might be acceptable for limited times in emergency situation. For instance, during a war we might put our faith in our military, up to and including the Commander-in-Chier. Indeed, that may be the best decision. But let that trust last to long, and you end up with an Egypt, or a North Korea: a kleptocracy good only at keeping itself in power.

Science, as an institution, is very new. While there were always mathematicians, and always engineers, scientists (full time employees of universities who democratically control their departments and whose primary job was research) are very new as a profession. It dates to 1880 or so, nearly a generation after Abraham Lincoln died, in the United States, though it appeared (minus the democratic control) about a generation earlier in what is now Germany.

The first “wave” of American science was made possible by the Department of Agriculture, which funded research programs to increase farm productivity. The Department of Agriculture is still heavily involved in science, to the extent that reliance of Department of Agriculture funds (which have always been bureaucratically allocated) are a political issue in the Association of American Universities.

US-Department-of-Agriculture

This first “wave” was not self-correcting. It was bureaucratically-correcting. The Department of Agriculture (motto: “Agriculture is the foundation of manufacture and commerce”) was guided by clear and well understood metrics (such as cross yield per acre) which allowed a fair and reasonable prioritizing of grant proposals. This system has worked well for a century, though obviously is open to political corruption.

National_Science_Foundation_Seal

The second “wave” of American science was made possible by the experimentation of the FDR and Truman administration. There was widespread consensus of extending the Department of Agriculture model to other fields. While Senator Harley Kilgore (D-WV) focused a direct extension, emphasizing local stakeholders for research, Vanaveer Bush emphasized self-regulation of science, with scientists alone deciding which scientific research would be funded. The institution created to push this was called the “National Science Foundation” (motto: “Supporting Education and Research across all the fields of Science, Mathematics and Technology”). As Wikipedia summarizes:

[Vannevar] Bush did not like the idea of letting social interests and community members drive science policy. He feared that the selection of research projects would become politicized, and he also had complete faith in the ability of scientists to pick the best possible projects. Furthermore, in contrast to Kilgore, he felt that the agency should have the narrower mandate of pursuing only basic science, rather than basic and applied science. Unlike Kilgore, he believed the public should not own research results and products, instead responsible researchers should own the research results. Broadly speaking, Bush’s vision was significantly more narrow than Kilgore’s proposal. It maintained the status quo in patenting arrangements, it limited project selection to scientists, and it narrowed projects to basic research

Unlike the Department of Agriculture model, which focused on bureaucratic control and practical research, the National Science Foundation focused on self-correction and abstract research. Instead of the corrupt but sustainable Department of Agricultural Model, the Untied States decided to focus on a pristine but unsustainable model that relied on the high moral standing of a small number of experts.

And now, we may be near the end of all that.


You don’t hear about Department of Agricultural research scandals because there are none. Seriously, bing it. Google it.  Everyone knows that political pull matters. But the focus on applied research means that there is little room for “trust me” by scientists. The corn grows with less water or it doesn’t. The goose has lower morality or she doesn’t. The bull’s calves are healthier or they aren’t. Too many stakeholders are too dependent on scientific progress in agriculture for falsified results to spread.

The Department of Agriculture’s “stakeholders” aren’t the easily ignored, like veterans or under-represented minorities. They are large land-owners, large farm services, and agribusiness. There’s well known waste and inefficiency in the Department of Agriculture, but the model (while inefficient) is fundamentally sound and sustainable.

Meanwhile, in NSF-land, most “findings” are false. Not some, not much, most. No profession “self-corrects” without outside pressure. Instead, careering spreads, and questioning results of another is considered “bullying.” Like in any corrupt political system, “anti-corruption” is simply a mask for elimination of enemies, because everyone knows everyone is corrupt.


A small number of revolutionaries, for a limited amount of time, can take great advantage of an unaccountable lack of power. Before they remember they have families, before others who want to be like them succeed in their goals, great change is possible. Maybe that time period lasts twenty years. But the NSF model, which is based on honesty and self-correction, is surely past its prime. Most research is false. Uncovered faked results are on the rise.

This is the background of the “replication crisis” in NSF-land.  Pure science has lost her cloak, which hid her flaws.   And the wolves are circling.

In the future research may still be under the “NSF” umbrella. But the importance of peer-review and self-correction in science are on the decline. Their time has come and gone.

Pimps, Hos, and When to Get Out of the Ghetto

I recently compared Humanities (Cultural Anthropology, English Literature, History, Philosophy) professors at research universities as pimps who rule the ghetto. Razib Khan liked the analogy so much he extended it with question: “do pimps facilitate good healthy sex for society, or do they encourage the spread of unpalatable contagion by perpetuating the ghetto and its conditions?

The answer: In the ghetto, pimps provide wages to hos who, depending on their character, either become accustomed to the poverty (thus joining the self-perpetuating underclass) or use the capital they accumulate to escape the ghetto.

To review: the ghetto is a neighborhood defined by economic deprivation. A very noticeable ghetto in academic life is the humanities ghetto of low wages and low employment:

wages_employment_majors_humanities_ghetto_md

Remember that the ghetto has four types of people: pimps (who make the best of a bad environment by running the ghetto), escapees (including those who are planning their escape), losers (including hos who work for pimps), and disaster tourists (including johns who provide the wages for the losers). Here’s an example of a loser ho:

“I am not a welfare queen,” says Melissa Bruninga-Matteau.

That’s how she feels compelled to start a conversation about how she, a white woman with a Ph.D. in medieval history and an adjunct professor, came to rely on food stamps and Medicaid. Ms. Bruninga-Matteau, a 43-year-old single mother who teaches two humanities courses at Yavapai College, in Prescott, Ariz., says the stereotype of the people receiving such aid does not reflect reality. Recipients include growing numbers of people like her, the highly educated, whose advanced degrees have not insulated them from financial hardship.

But the “ghetto” is larger than just the humanities. Many non-progressive sciences are in the ghetto, because they are run by old boys networks — by their pimps. Likewise, even having progressive research programs does not (necessarily) protect against ghettoization. Using information from Indeed (which suffers from all sorts of biases, but the relative values of which have face validity), this is a chart of the overlapping ghettos by PhD concentration, against where you want to be:

salary_by_phd_md

The humanities does not confine you to poverty (you can escape). Whether or not science comes to an end, the myth that scientific training means a successful life certainly should. Being in a normal science does not guarantee success. Personal success comes from finding something that can provide you with joy, provide you with the ability to be the best, and provide you with pay. If you have these attributes in what you are doing, you can be successful, whatever your compensation (in terms of money, power, and prestige).

Petroleum engineers who enjoy their work can enjoy these from heights. Pimps can enjoy these from the ghetto. But without these three, you are much more likely to be miserable.

3-circles-hedgehog-concept

In other words: if you can’t pimp, get out of the ghetto.

Science and Steam

Reactions to two of my recent posts — Mark Safranki‘s excerpt of my review of America 3.0. and Phil Arena‘s comment on my post on antiscience, plus some twitter conversations with Colin Wight — got me thinking.

What is the relationship of Science to the great economic systems we’ve had — hydrological, steam-powered, and now whatever-comes-next.

Well, in a hydrological system you’re either at the Malthusian limit or quite good at killing people off through war of disease.

Science is too risky (might not work, might have bad consequences if it does work) to spend much resources on in a pre-steam, pre-industrial society. So you get a few intellectual giants shouting to each other across time — like the nameless Chinese inventors or named European ones — with relatively little utility within a human lifetime.

But once you have steam-power, and the economic system it enables, society becomes incredibly wealthy. So you get science, institution science, whether in the form of corporate labs, or academic science, or the Department of Agriculture. The methods of advancement are so different, and the pace of change is so much quicker, this Science in a modern science is a different beast from pre-steam science — natural philosoph– which was basically bored men every once in a while discovering something.

What comes after the reign of steam, and the industrial society? What does Science look like after the next transformation?

It will be exciting to find out!

The Place of Rational Choice

After criticizing Patrick Thaddeus Jackson‘s antiscientific and dangerous attack on Rational Choice Theory, I then turned around and attacked Rational Choice Theory itself for not being a scientific theory (though it can be a useful tool).

The lesson, I guess, is that simply having the right enemies does not make you right yourself.

My critiqued of both Jackson and Rational Choice attracted the attention of Phil Arena, both regarding antiscience and, more interestingly, regarding Rational Choice. Phil was kind enough to provide with me two articles, “Does Preference Cycling Invalidate “Rational Choice Theory”?,” and “Rat Choice Apologetics II” in which he had previously attempted to defend Rational Choice Theory from similar attacks.

Phil’s posts emphasize that Rational Choice is not a scientific theory.

The first post, on preference cycling, is an extended “just-so” defense of Rational Choice theorizing against laboratory falsification. Phil writes:

My big point here is that those who seek to justify a wholesale rejection of “rational choice theory” by observing that some laboratory experiments have found that some individuals exhibit behavior that appears to reflect cyclical preferences are overplaying their hand.

But Phil’s bigger points seems to be that any laboratory finding does not falsify Rational Choice, because some collection of mathematical formulas can be modified post-hoc to account for the behavior observed. This speeks to the cleverness of the Rational Choice theorists — like Freudians or Jungians, any observation of evidence of their model.

Rational Choice is like Interviewing, because just as no experimental result can falsify Rational Choice, no experimental result can falsify the feelings of an interview subject. Few who are planning a complex intervention would do so without interviews of one sort or another, and it may be that Rational Choice is likewise useful. But just as the interview is a tool, not a scientific theory, Rational Choice is a tool, not a scientific theory.

In the follow-up Post, Phil goes farther to protect not just Rational Choice Theory, but any implementation of a rational choice theory, from falsification:

Amongst formal theorists, there is significant disagreement about how to evaluate models in general. On one end of the spectrum, you have the strict interpretation of EITM, as espoused here and seems to be Morton’s preferred view here, though she does discuss other views. This view holds that formal models are important for ensuring logical consistency of theoretical arguments, but the value of these arguments is ultimately judged empirically. On the other, you have Primo and Clarke, who argue that there are many different roles we could ask our models to serve, some of which do not require any kind of empirical assessment. My own views, as I’ve indicated before, are closer to those of Primo and Clarke.

This is not scientifically serious. But Rational Choice Theory is not a scientific theory, so of course it doesn’t have to be. The purpose of science is to improve, predict, or control behavior (at whatever unit of analysis we are working), but the purpose of tools such as interviews, case study, and rational choice is to inspire scientists to come up with scientific theories that can make control, predict, and improve behavior.

Phil’s a clear writer, so his point is written clearly. And he’s write that science has certain requirements — such as predictive validity — that are as hard to get away from as Rational Choice Theory’s unfalsifiable assumptions:

When we evaluate arguments empirically, we make a huge, non-falsifiable assumption that the future will be like the past. Otherwise, it would be meaningless to claim to be testing the claim that X causes Y by observing historical patterns of association between X and Y. On a certain level, we all understand this. That is why folks worry about omitted variable bias with observational studies and external validity with experiments. But I’m not sure how many people really appreciate the depth of the problem.

But of course the difference is that the scientific requirement for predictive validity enables it to fulfill its mission of predicting, improving, and controlling behavior (at whatever unit of analysis we are functioning). Rational Choice Theory rejects the scientific need to predict, improve, or control behavior, because it is a “formal model” which are “logical consistency” and thus do not need “empirical assessment.” That is, Rational Choice is a form of “qualitative” (or better, investigatory) analysis, where mathematical equation balancing takes the place of interviews or subjective impressions.

Rational Choice has a place in science, like any investigatory or qualitative method (introspection, interviews, case studies, etc): to generate hypotheses. Rational Choice should be a part of science to the extent its scientifically useful. But like interviews, case studies, and the such, we can’t generalize from rational choice theorizing, but of course we can generalize from the empirical findings such theorizing might lead us to.

Against Rational Choice

I recently wrote two posts, “Four Types of Anti-Science” and “Academia, Science, and Anti-Science,” which took Patrick Thaddeus Jackson to task for his post, “The Society of Individuals.” I even criticized Phil Arena for not being sufficiently critical of Jackon’s writing in his post, “Should We Keep Hidden the Way People Behave When their Actions are Hidden?

But here’s the thing: I’m not a fan of “Rational Choice.” It’s a useful tool, but Rational Choice Theory is not a scientific theory.

For emphasis: Rational Choice Theory itself is not a scientific theory — it’s a tautology that’s used for creating theories, but it’s based on a basically absurd premise that is as protected from refutation as the worst nonsense from Sigmund Freud and Carl Jung.

The core foundation of Rational Choice Theory is that individuals have a discoverable complete transitive preference schedule. This is a ridiculous assumption. It’s also unfalsifiable in terms of the theory that generated it.

  • Discoverable means it is possible for researchers to uncover this. A list of desired possibilities, which occurs entirely in the mind and doesn’t consistently direct action, is irrelevant to Rational Choice Theory.
  • Complete means it contains all possible actions and choices. Some of these may be unknown at the time that a decision is made, but once it is known, it does not change the order of preferences.
  • Transitive means the order is consistent, that there are no loops or self-referential cycles. For instance, if you would rather have money than a job, and would rather be comfortable than have money, therefore you would rather be comfortable than have a job.
  • Preference Schedule means that this is the list that controls actions. It’s important to note that Rational Choice Theory is not a psychological theory. There is no need, whatsoever, for Rational Choice to explain the “reasons” for choices, or the subjective experience of the chooser.

It is the transitive requirement which prevents Rational Choice Theory from being a scientific theory. For instance, in the example above, even if we could discover that the subject who prefers a job to comfort, then the Rational Choice Theorist would say there must really be some other elements we hadn’t considered — say a desire to be useful and a desire not to be worthless, which are the real preferences.

Rational Choice Theory is the No True Scotsman fallacy writ large.

All that said, Rational Choice is a method for generating theories. Some are falsified. Others are not and are found to be useful. Like Evolutionary Psychology with its mythical “Era of Evolutionary Adaptation,” Rational Choice’s discoverable complete transitive preference schedule is a tool that enables scientists to create scientific theories about the world, rather than a scientific theory in itself.

Dr. Jackson’s attack on Rational Choice Theory was anti-science, because it privileged his idiosyncratic idealistic prejudices against the scientific method.

He would have been far more useful if he had merely stated it was not a scientific theory at all.

Academia, Science, and Anti-Science

Dr. Patrick Thaddeus Jackson’s anti-scientific critique of rational choice theory made me think more of Academia, and its relationship to Science.

Academia and Science are not the same thing. Indeed, for a long time most U.S. government science funding was channeled thru the Department of Agriculture. Many of the great scientific advancements in the United States were likewise made outside the typical academic environment, such as Bell Labs, General Electric, the Manhattan Project, and the Apollo Program. While academia were involved in these places to varying extent, none of them ran on the basis of academic freedom.

How Academia works is not the only way of how Science works. Science already has too many enemies to be dragged down into the political muck with Academics who themselves attack science in addition to creating political enemies. Academia is already under too much attack — such as from teachers union attempting to harvest profits from the public school system – to stay healthy under the anti-Scientific strain.

The proper role of non-Scientific academics is teaching, service, and research that builds useful things. The digital humanities are an amazing and lucrative example of such useful, non-Scientific work in Academia. Jason Heppler of Stanford University runs an awesome blog on such things, Likewise, the cool Geographic Travels blogs emphasizes the utility of spatial and cultural geography. There’s plenty of room for such activity in Academia, too.

But that space is threatened by the anti-scientists — especially elite anti-scientists — who simultaneously attack Science and also generate political enemies. Dr. Jackson’s post titled “The Society of Individuals,” for instance, is an attack on Rational Choice research programs while also attacking politically relevant philosophers for being sexist and morally repugnant.

Science in the Academy is too precious for those who attack Science and the foundations of the Academy. It is a tragedy such parasitic rhetoric is found in the system. It is a waste of resources all around.

A further tragedy is that when non-scientific academics engage in tangential political debates, the (natural) political reaction can be ineffective, counterproductive, and chaotic. Dr. Jackson’s piece is surely an example of the sort of research that Senator Coburn hoped to put a stop to by taking away National Science Foundation support for political science.” But the NSF supports actual scientific work, so the consequences of the defunding are to weaken the Academy, weaken Science, but previously strengthen the voices of those anti-scientific talking heads who might otherwise be drowned out by scientific Academics.

Over at gnxp, Razib Khan has surged that anti-science cultural anthropology “be extirpated from the academy.” More generally, anti-scientists of all types should be too. But there’s no easy or obvious way to do this without risking the Academic Freedom that anti-scientists use to attack science

In conclusion, anti-science should be extirpated from the academy. But I have no idea of how this should be done.

Four Types of Anti-Science

There are scientists, but this post is not about them.

(If you want my career advise for folks who like science, please read the following posts instead: “How Academia Works,” “When It Sucks to Be Young, “Science, Paradigms, and the Old Boys Network,” and How to Escape the Humanities Ghetto.”)

There are people who oppose science in ideological grounds, either out of a specific distaste for science, or else because scientific research or findings leads (or is seen to lead) to objectionable conclusions, or else because they do not know what science is and attack it as part of their other activities.. This post is about them.

Let’s consider two dimensions of anti-scientists, by the nature of their strength.

  • The size dimension accounts for the number of their confederates int their attempt to retard or stop scientific progress.
  • The seriousness dimension accounts for the intellectual rigor and elite infiltration that they and their confederates have gained.

antiscience_dimensions

We can describe each corner of this taxonomy:

  • Popular X Elite: The elite and the public are united against scientific investigation. This is the case in most non-medical human biodiversity research, because of the ideological and historical connotations of such research in the eyes of many. Thus, Human Biomonoculturalists are examples of popular, elite anti-scientists.
  • Popular X Downtrodden: Large, widespread public animosity towards science, but without elite support. In the United States and many Muslim countries, attitudes toward evolutionary biology fall into this category. So Creationists are examples of a popular, downtrodden anti-scientists.
  • Small X Downtrodden: A politically unpopular and generally disenfranchised group is opposed to science, but has not yet gained any form of transaction. So Flat Earthers are examples of small, downtrodden anti-scientists.
  • Small X Elite: A small, highly trained cadre of experts, with elite credentials, attempts to overturn scientific funding. In this post I’ll describe Collectivist Ideologues as examples of small, elite anti-scientists.

An example of such a small but serious attack on science — of Collectivist Ideologues — is Dr. Patrick Thaddeus Jackson’s recent post, “The Society of Individuals,” which appeared at the popular political science blog Duck of Minerva

antiscience_types

The writing in Dr. Jackson’s article is dense, but the argument boils down to the following

1. Rational Choice Theory immorally operationalizes social decisions on the individual, not the society level

So we have two fundamentally different models here: autonomous individuals — prototypical males? — with preferences making strategic calculations, and relationally embedded actors (I’m not going to push the gender point any further here, but I think that many feminists might agree with me about the relative depictions of autonomy-vs.-embeddedness in a patriarchal society) engaged in deliberation and discernment looking for the right course of action. While the former might end up conforming to one or another moral code, only the latter can actually engage in “moral action” per se, because autonomous individuals would be choosing whether or not to act morally while embedded actors would be endeavoring to suss out the moral thing to do and then doing it.

2. The implications of this are morally objectionable twiceover, for being based on individuality and sexism

I still maintain that rational choice theory — and indeed, the broader decision-theoretical world of which rational choice theory constitutes just a particular, heavily-mathematized province — endorses and naturalizes a form of selfishness that is ultimately corrosive of human community and detrimental to the very idea of moral action.

3. Thus, rational choice research programs — and the communication of those programs are “basically corrosive and should be opposed whenever practicable.”

I think that things like Freakonomics [tdaxp excerpt] are basically corrosive and should be opposed whenever practicable. We owe it to the broader society not to simply tell stories that reaffirm the value-commitments and modes of person-hood prized by dominant social actors who want us to equate our happiness with the satisfaction of personal desires

Dr. Jackson’s collectivism idealism states (apparently) that scientists are immoral if they attempt to help control, predict, and improve variation in the world in a way that doesn’t fit with Jackson’s ideals, biases and sentimentalities.

At first glance, Dr. Jackson’s post is odd. It’s too dense and abstract to gain much popular traction. And his description of Rational Choice theory is ridiculous to anyone familiar with it. But such talking heads have wracked havoc in other ares, by attacking science for opposing their sentimentalities and prejudices.

At second glance, Jackson’s post is somewhat more understandable. Political science does not progress like a normal science, and many people who use terms like “Rational Choice” may themselves have no idea how science works. Few anti-scientists are driven by animosity towards humanity. Ignorance of science, and a love of their idealized and wished-for worlds, doubtless plays a larger part.

Anti-science is dangerous. Popular-elite anti-science most of all, but even popular-downtrodden (like the hapless Creationists) and small-elite (like Dr. Jackson’s arguments) should be recognized as the threats to human progress than they are. Human history is a record of one stagnation after another, with brief bursts of progress in between. I hope the anti-Scientists do not stop our current progress, and consign us all to castrated academia composed of ideologues and their pet biases.

Variation, Within and Between

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”!

european_races

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.

finding_your_roots

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.

Structural Equations — or — Translating Theories into Models

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:

examplesem_md

(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 Humanities, the Sciences, and Strategy

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.

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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.

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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.

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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.

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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.

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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.