Jared Diamond on the legitimacy of social sciences

[Dermezel]

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As a biologist practicing laboratory experimental science, I'm aware that some scientists may be inclined to dismiss these historical interpretations as unprovable speculation, because they're not founded on replicated laboratory experiments. The same objection can be raised against any of the historical sciences, including astronomy, evolutionary biology, geology, and paleontology. The objection can of course be raised against the whole field of history, and most of the other social sciences. That's the reason why we're uncomfortable about considering history as a science. It's classified as a social science, which is considered not quite scientific.

But remember that the word "science" isn't derived from the Latin word for "replicated laboratory experiment," but instead from the Latin word "scientia" for "knowledge." In science, we seek knowledge by whatever methodologies are available and appropriate. There are many fields that no one hesitates to consider sciences even though replicated laboratory experiments in those fields would be immoral, illegal, or impossible. We can't manipulate some stars while maintaining other stars as controls; we can't start and stop ice ages, and we can't experiment with designing and evolving dinosaurs. Nevertheless, we can still gain considerable insight into these historical fields by other means. Then we should surely be able to understand human history, because introspection and preserved writings give us far more insight into the ways of past humans than we have into the ways of past dinosaurs. For that reason I'm optimistic that we can eventually arrive at convincing explanations for these broadest patterns of human history.

http://www.edge.org/3rd_culture/diamond/diamond_p6.html

He makes a good point, and as a practicing scientist I think he would know.

In any case, he is right, if you are just defining science as a lab experiment, then you have to throw out astronomy and a great deal of biology since much of that is based on natural observation.

Likewise much of the distinction between "soft" and "hard" sciences is actually made by journalists, not scientists themselves or philosophers of science:

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ALTHOUGH THE SOCIAL SCIENCES are integral to news reporting, experts say, the public generally doesn't consider these sciences truly scientific. Laypeople and academicians alike tend to judge fields such as sociology, psychology, and political science as "soft" because they are presumed to be understandable, devoid of mathematical rigor, and concerned with everyday concepts such as interpersonal relationships. On the other hand, astronomy, physics, and biology are more "scientific" because they are deemed difficult, demand exactitude, and concern discoveries far removed from routine human experience, such as atomic forces or DNA.

Journalists help maintain this conceptual dualism, say leading Columbia social scientists. It happens, they say, because reporters tend to rely on social scientists as sources for commentary about current events such as crime, politics, or catastrophes. The media doesn't give much ink or air time to new knowledge generated by social science research activity, as it does in the hard sciences. As a result, the public image of social science research is more fluff than tough. This perception doesn't just strike at the self-esteem of social scientists; it potentially affects research funding.

....

That responsibility, explains James W. Carey, professor of journalism at Columbia, stems from the fact that new findings in social research are often volatile and speak directly to social policies. He says that reporters, when they do cover social science subjects, portray them as controversies to take sides on, while depicting the physical sciences as a mystery to explore.

http://www.columbia.edu/cu/21stC/issue-1.1/soft.htm

In other words, "soft" sciences are treated as less certain because it is more sensational, and because people find the idea that social sciences can be certain to be uncomfortable since it implies a sort of determinism.

But the fact of the matter is so-called "soft" science can actually be more rigorous then so called "hard" science since they deal with more difficult phenomenon.

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-- '"The overall correlation between frustration and instability (in 62 countries of the world) was 0.50.'' --Samuel Huntington, professor of government, Harvard

-- ''This is utter nonsense. How does Huntington measure things like social frustration? Does he have a social-frustration meter? I object to the academy's certifying as science what are merely political opinions.'' -- Serge Lang, professor of mathematics, Yale

-- ''What does it say about Lang's scientific standards that he would base his case on twenty-year-old gossip?'' . . . ''a bizarre vendetta'' . . . ''a madman . . .'' -- Other scholars, commenting on Lang's attack

For those who love to watch a dogfight among intellectuals supposedly above such things, it's been a fine dogfight, well publicized in Time and elsewhere. In one corner, political scientist and co-author of The Crisis of Democracy, Samuel Huntington. In the other corner, mathematician and author of Diophantine Approximation on Abelian Varieties with Complex Multiplication, Serge Lang. The issue: whether Huntington should be admitted, over Lang's opposition, to an academy of which Lang is a member. The score after two rounds: Lang 2, Huntington 0, with Huntington still out.

Lang vs. Huntington might seem like just another silly blood-letting in the back alleys of academia, hardly worth anyone's attention. But this particular dogfight is an important one. Beneath the name calling, it has to do with a central question in science: Do the so-called soft sciences, like political science and psychology, really constitute science at all, and do they deserve to stand beside ''hard sciences,'' like chemistry and physics?

The arena is the normally dignified and secretive National Academy of Sciences (NAS), an honor society of more than 1,500 leading American scientists drawn from almost every discipline. NAS's annual election of about 60 new members begins long before each year's spring meeting, with a multi- stage evaluation of every prospective candidate by members expert in the candidate's field. Challenges of candidates by the membership assembled at the annual meeting are rare, because candidates have already been so thoroughly scrutinized by the appropriate experts. In my eight years in NAS, I can recall only a couple of challenges before the Lang-Huntington episode, and not a word about those battles appeared in the press.

At first glance, Huntington's nomination in 1986 seemed a very unlikely one to be challenged. His credentials were impressive: president of the American Political Science Association; holder of a named professorship at Harvard; author of many widely read books, of which one, American Politics: The Promise of Disharmony, got an award from the Association of American Publishers as the best book in the social and behavioral sciences in 1981; and many other distinctions. His studies of developing countries, American politics, and civilian-military relationships received the highest marks from social and political scientists inside and outside NAS. Backers of Huntington's candidacy included NAS members whose qualifications to judge him were beyond question, like Nobel Prize winning computer scientist and psychologist Herbert Simon.

If Huntington seemed unlikely to be challenged, Lang was an even more unlikely person to do the challenging. He had been elected to the academy only a year before, and his own specialty of pure mathematics was as remote as possible from Huntington's specialty of comparative political development. However, as Science magazine described it, Lang had previously assumed for himself ''the role of a sheriff of scholarship, leading a posse of academics on a hunt for error,'' especially in the political and social sciences. Disturbed by what he saw as the use of ''pseudo mathematics'' by Huntington, Lang sent all NAS members several thick mailings attacking Huntington, enclosing photocopies of letters describing what scholar A said in response to scholar B's attack on scholar C, and asking members for money to help pay the postage and copying bills. Under NAS rules, a candidate challenged at an annual meeting is dropped unless his candidacy is sustained by two-thirds of the members present and voting. After bitter debates at both the 1986 and 1987 meetings, Huntington failed to achieve the necessary two-thirds support.

Much impassioned verbiage has to be stripped away from this debate to discern the underlying issue. Regrettably, a good deal of the verbiage had to do with politics. Huntington had done several things that are now anathema in U.S. academia: he received CIA support for some research; he did a study for the State Department in 1967 on political stability in South Vietnam; and he's said to have been an early supporter of the Vietnam war. None of this should have affected his candidacy. Election to NAS is supposed to be based solely on scholarly qualifications; political views are irrelevant. American academics are virtually unanimous in rushing to defend academic freedom whenever a university president or an outsider criticizes a scholar because of his politics. Lang vehemently denied that his opposition was motivated by Huntington's politics. Despite all those things, the question of Huntington's role with respect to Vietnam arose repeatedly in the NAS debates. Evidently, academic freedom means that outsiders can't raise the issue of a scholar's politics but other scholars can.

It's all the more surprising that Huntington's consulting for the CIA and other government agencies was an issue, when one recalls why NAS exists. Congress established the academy in 1863 to act as official adviser to the U.S. government on questions of science and technology. NAS in turn established the National Research Council (NRC), and NAS and NRC committees continue to provide reports about a wide range of matters, from nutrition to future army materials. As is clear from any day's newspaper, our government desperately needs professionally competent advice, particularly about unstable countries, which are one of Huntington's specialties. So Huntington's willingness to do exactly what NAS was founded to do -- advise the government -- was held against him by some NAS members. How much of a role his politics played in each member's vote will never be known, but I find it unfortunate that they played any role at all.

I accept, however, that a more decisive issue in the debates involved perceptions of the soft sciences -- e.g., Lang's perception that Huntington used pseudo mathematics. To understand the terms soft and hard science, just ask any educated person what science is. The answer you get will probably involve several stereotypes: science is something done in a laboratory, possibly by people wearing white coats and holding test tubes; it involves making measurements with instruments, accurate to several decimal places; and it involves controlled, repeatable experiments in which you keep everything fixed except for one or a few things that you allow to vary. Areas of science that often conform well to these stereotypes include much of chemistry, physics, and molecular biology. These areas are given the flattering name of hard science, because they use the firm evidence that controlled experiments and highly accurate measurements can provide.

We often view hard science as the only type of science. But science (from the Latin scientia -- knowledge) is something much more general, which isn't defined by decimal places and controlled experiments. It means the enterprise of explaining and predicting -- gaining knowledge of -- natural phenomena, by continually testing one's theories against empirical evidence. The world is full of phenomena that are intellectually challenging and important to understand, but that can't be measured to several decimal places in labs. They constitute much of ecology, evolution, and animal behavior; much of psychology and human behavior; and all the phenomena of human societies, including cultural anthropology, economics, history, and government.

These soft sciences, as they're pejoratively termed, are more difficult to study, for obvious reasons. A lion hunt or revolution in the Third World doesn't fit inside a test tube. You can't start it and stop it whenever you choose. You can't control all the variables; perhaps you can't control any variable. You may even find it hard to decide what a variable is. You can still use empirical tests to gain knowledge, but the types of tests used in the hard sciences must be modified. Such differences between the hard and soft sciences are regularly misunderstood by hard scientists, who tend to scorn soft sciences and reserve special contempt for the social sciences. Indeed, it was only in the early 1970s that NAS, confronted with the need to offer the government competent advice about social problems, began to admit social scientists at all. Huntington had the misfortune to become a touchstone of this widespread misunderstanding and contempt.

While I know neither Lang nor Huntington, the broader debate over soft versus hard science is one that has long fascinated me, because I'm among the minority of scientists who work in both areas. I began my career at the hard pole of chemistry and physics, then took my Ph.D. in membrane physiology, at the hard end of biology. Today I divide my time equally between physiology and ecology, which lies at the soft end of biology. My wife, Marie Cohen, works in yet a softer field, clinical psychology. Hence I find myself forced every day to confront the differences between hard and soft science. Although I don't agree with some of Lang's conclusions, I feel he has correctly identified a key problem in soft science when he asks, ''How does Huntington measure things like social frustration? Does he have a social-frustration meter?'' Indeed, unless one has thought seriously about research in the social sciences, the idea that anyone could measure social frustration seems completely absurd.

The issue that Lang raises is central to any science, hard or soft. It may be termed the problem of how to ''operationalize'' a concept. (Normally I hate such neologistic jargon, but it's a suitable term in this case.) To compare evidence with theory requires that you measure the ingredients of your theory. For ingredients like weight or speed it's clear what to measure, but what would you measure if you wanted to understand political instability? Somehow, you would have to design a series of actual operations that yield a suitable measurement -- i.e., you must operationalize the ingredients of theory.

Scientists do this all the time, whether or not they think about it. I shall illustrate operationalizing with four examples from my and Marie's research, progressing from hard science to softer science.

Let's start with mathematics, often described as the queen of the sciences. I'd guess that mathematics arose long ago when two cave women couldn't operationalize their intuitive concept of ''many.'' One cave woman said, ''Let's pick this tree over here, because it has many bananas.'' The other cave woman argued, ''No, let's pick that tree over there, because it has more bananas.'' Without a number system to operationalize their concept of ''many,'' the two cave women could never prove to each other which tree offered better pickings.

There are still tribes today with number systems too rudimentary to settle the argument. For example, some Gimi villagers with whom I worked in New Guinea have only two root numbers, iya = 1 and rarido = 2, which they combine to operationalize somewhat larger numbers: 4 = rarido-rarido, 7 = rarido-rarido-rarido-iya, etc. You can imagine what it would be like to hear two Gimi women arguing about whether to climb a tree with 27 bananas or one with 18 bananas.

Now let's move to chemistry, less queenly and more difficult to operationalize than mathematics but still a hard science. Ancient philosophers speculated about the ingredients of matter, but not until the eighteenth century did the first modern chemists figure out how to measure these ingredients. Analytical chemistry now proceeds by identifying some property of a substance of interest, or of a related substance into which the first can be converted. The property must be one that can be measured, like weight, or the light the substance absorbs, or the amount of neutralizing agent it consumes.

For example, when my colleagues and I were studying the physiology of hummingbirds, we knew that the little guys liked to drink sweet nectar, but we would have argued indefinitely about how sweet sweet was if we hadn't operationalized the concept by measuring sugar concentrations. The method we used was to treat a glucose solution with an enzyme that liberates hydrogen peroxide, which reacts (with the help of another enzyme) with another substance called dianisidine to make it turn brown, whereupon we measured the brown color's intensity with an instrument called a spectrophotometer. A pointer's deflection on the spectrophotometer dial let us read off a number that provided an operational definition of sweet. Chemists use that sort of indirect reasoning all the time, without anyone considering it absurd.

My next-to-last example is from ecology, one of the softer of the biological sciences, and certainly more difficult to operationalize than chemistry. As a bird watcher, I'm accustomed to finding more species of birds in a rain forest than in a marsh. I suspect intuitively that this has something to do with a marsh being a simply structured habitat, while a rain forest has a complex structure that includes shrubs, lianas, trees of all heights, and crowns of big trees. More complexity means more niches for different types of birds. But how do I operationalize the idea of habitat complexity, so that I can measure it and test my intuition?

Obviously, nothing I do will yield as exact an answer as in the case where I read sugar concentrations off a spectrophotometer dial. However, a pretty good approximation was devised by one of my teachers, the ecologist Robert MacArthur, who measured how far a board at a certain height above the ground had to be moved in a random direction away from an observer standing in the forest (or marsh) before it became half obscured by the foliage. That distance is inversely proportional to the density of the foliage at that height. By repeating the measurement at different heights, MacArthur could calculate how the foliage was distributed over various heights.

In a marsh all the foliage is concentrated within a few feet of the ground, whereas in a rain forest it's spread fairly equally from the ground to the canopy. Thus the intuitive idea of habitat complexity is operationalized as what's called a foliage height diversity index, a single number. MacArthur's simple operationalization of these foliage differences among habitats, which at first seemed to resist having a number put on them, proved to explain a big part of the habitats' differences in numbers of bird species. It was a significant advance in ecology.

For the last example let's take one of the softest sciences, one that physicists love to deride: clinical psychology. Marie works with cancer patients and their families. Anyone with personal experience of cancer knows the terror that a diagnosis of cancer brings. Some doctors are more frank with their patients than others, and doctors appear to withhold more information from some patients than from others. Why?

Marie guessed that these differences might be related to differences in doctors' attitudes toward things like death, cancer, and medical treatment. But how on earth was she to operationalize and measure such attitudes, convert them to numbers, and test her guesses? I can imagine Lang sneering ''Does she have a cancer-attitude meter?''

Part of Marie's solution was to use a questionnaire that other scientists had developed by extracting statements from sources like tape-recorded doctors' meetings and then asking other doctors to express their degree of agreement with each statement. It turned out that each doctor's responses tended to cluster in several groups, in such a way that his responses to one statement in a cluster were correlated with his responses to other statements in the same cluster. One cluster proved to consist of expressions of attitudes toward death, a second cluster consisted of expressions of attitudes toward treatment and diagnosis, and a third cluster consisted of statements about patients' ability to cope with cancer. The responses were then employed to define attitude scales, which were further validated in other ways, like testing the scales on doctors at different stages in their careers (hence likely to have different attitudes). By thus operationalizing doctors' attitudes, Marie discovered (among other things) that doctors most convinced about the value of early diagnosis and aggressive treatment of cancer are the ones most likely to be frank with their patients.

In short, all scientists, from mathematicians to social scientists, have to solve the task of operationalizing their intuitive concepts. The book by Huntington that provoked Lang's wrath discussed such operationalized concepts as economic well-being, political instability, and social and economic modernization. Physicists have to resort to very indirect (albeit accurate) operationalizing in order to ''measure'' electrons. But the task of operationalizing is inevitably more difficult and less exact in the soft sciences, because there are so many uncontrolled variables. In the four examples I've given, number of bananas and concentration of sugar can be measured to more decimal places than can habitat complexity and attitudes toward cancer.

Unfortunately, operationalizing lends itself to ridicule in the social sciences, because the concepts being studied tend to be familiar ones that all of us fancy we're experts on. Anybody, scientist or no, feels entitled to spout forth on politics or psychology, and to heap scorn on what scholars in those fields write. In contrast, consider the opening sentences of Lang's paper Diophantine Approximation on Abelian Varieties with Complex Multiplication: ''Let A be an abelian variety defined over a number field K. We suppose that A is embedded in projective space. Let AK be the group of points on A rational over K.'' How many people feel entitled to ridicule these statements while touting their own opinions about abelian varieties?

No political scientist at NAS has challenged a mathematical candidate by asking ''How does he measure things like 'many'? Does he have a many-meter?'' Such questions would bring gales of laughter over the questioner's utter ignorance of mathematics. It seems to me that Lang's question ''How does Huntington measure things like social frustration?'' betrays an equal ignorance of how the social sciences make measurements.

The ingrained labels ''soft science'' and ''hard science'' could be replaced by hard (i.e., difficult) science and easy science, respectively. Ecology and psychology and the social sciences are much more difficult and, to some of us, intellectually more challenging than mathematics and chemistry. Even if NAS were just an honorary society, the intellectual challenge of the soft sciences would by itself make them central to NAS.

But NAS is more than an honorary society; it's a conduit for advice to our government. As to the relative importance of soft and hard science for humanity's future, there can be no comparison. It matters little whether we progress with understanding the diophantine approximation. Our survival depends on whether we progress with understanding how people behave, why some societies become frustrated, whether their governments tend to become unstable, and how political leaders make decisions like whether to press a red button. Our National Academy of Sciences will cut itself out of intellectually challenging areas of science, and out of the areas where NAS can provide the most needed scientific advice, if it continues to judge social scientists from a posture of ignorance.

http://bama.ua.edu/~sprentic/607 Diamond 1987.htm

As Diamond notes, dismissing the social science, on the basis of nothing more then gossip, is a "posture of ignorance" and I would add bias, as well as presumption regarding what science is from people who have taken neither the time, nor put forth the effort to learn the actual history and philosophy of science, or simply disagree with the idea that sociology and psychology could be scientific for philosophical or religious or "spiritual" reasons.

[Layra-chan]

Although my first instinct is to point out that mathematics is not a science, and my second is to point out that the notions of more and fewer, and the ability to distinguish such, are completely independent of linguistic formulation, and my third is to point out that a lot of mathematicians are assholes who don't even respect other mathematicians, I'll have to admit that I liked the article.
I do wish he hadn't brought up the bit about the etymology of "science". I've never seen such an action actually benefit the argument without giving leave to people to completely misinterpret the intent. "Oh, so since science is just knowledge I can say whatever I 'know' and it'll be science".
Maybe he goes into it in another part, but I wish he'd spend more time on the epistemological implications of operationalization. It's not just a way to measure things, but a means by which two people, armed with only the operationalization, can look at the same phenomenon and agree upon what they've observed. It's this definitional rigor that gets attacked, not just the empirical aspect, at least in my experience. The worry isn't so much about quantification but rather about even knowing what you're talking about.

The other problem is one of statistics; compared to the "hard" sciences, sample size in the social sciences tends to be low. You can't control the stars, but there are a lot of them, enough so that you can usually find two that are identical in all but the relevant parameter. A person might not be able to reproduce the experiment at whim, but it is reproduced. In the social sciences, there usually isn't enough data to be able to say "I can find two instances such that the only difference between them is ___, and hence ____ is the relevant factor". He mentions the difficulty with this, and the difficulty with even identifying what possible ___s could be, but he only mentions the solution to the second problem.
Of course, this wouldn't cause something to not be a science, but it would lead to difficulty accepting that a thesis is a sure conclusion from the data.

Also, his statement about the importance of the social sciences versus the importance of mathematics is exactly the kind of ignorant drivel that he is supposedly fighting against. The social sciences are simply easier to care about, just as the hard sciences are easier to operationalize. Talking about importance just makes him a Lang with a softer tongue.

[Dermezel]

Layra-chan

Although my first instinct is to point out that mathematics is not a science, and my second is to point out that the notions of more and fewer, and the ability to distinguish such, are completely independent of linguistic formulation, and my third is to point out that a lot of mathematicians are assholes who don't even respect other mathematicians, I'll have to admit that I liked the article.
I do wish he hadn't brought up the bit about the etymology of "science". I've never seen such an action actually benefit the argument without giving leave to people to completely misinterpret the intent. "Oh, so since science is just knowledge I can say whatever I 'know' and it'll be science".
Maybe he goes into it in another part, but I wish he'd spend more time on the epistemological implications of operationalization. It's not just a way to measure things, but a means by which two people, armed with only the operationalization, can look at the same phenomenon and agree upon what they've observed. It's this definitional rigor that gets attacked, not just the empirical aspect, at least in my experience. The worry isn't so much about quantification but rather about even knowing what you're talking about.

The other problem is one of statistics; compared to the "hard" sciences, sample size in the social sciences tends to be low. You can't control the stars, but there are a lot of them, enough so that you can usually find two that are identical in all but the relevant parameter. A person might not be able to reproduce the experiment at whim, but it is reproduced. In the social sciences, there usually isn't enough data to be able to say "I can find two instances such that the only difference between them is ___, and hence ____ is the relevant factor". He mentions the difficulty with this, and the difficulty with even identifying what possible ___s could be, but he only mentions the solution to the second problem.
Of course, this wouldn't cause something to not be a science, but it would lead to difficulty accepting that a thesis is a sure conclusion from the data.

Also, his statement about the importance of the social sciences versus the importance of mathematics is exactly the kind of ignorant drivel that he is supposedly fighting against. The social sciences are simply easier to care about, just as the hard sciences are easier to operationalize. Talking about importance just makes him a Lang with a softer tongue.

Diamond's point concerning operationalization was that any field of expertise, be it math, physics, or sociology has a set of measurements which have been developed by practice and social conventions. For example, many definitions used in math were not carved in stone but were debated over many decades and centuries. Diamond's main argument in this regards is that when a mathematician argues that a social scientist cannot take measurements because they are determined by convention, he or she is using a double standard and ignoring the fact that mathematics itself has simihttp://www.gaiaonline.com/forum/compose/entry/new/55881109/?quote=2lar conventional, or operational definitions.

Sample size might be lower on average in the social sciences (I am not sure if this has ever been tested, or measured) but there are exceptions (for example particle accelerators, or observations of stars going supernova, or the Big Bang of which we only have one example). Likewise sample size is usually determined by statistics and what is acceptable as a margin of error. This is something universally applied across many sciences, not just social sciences.

With respect to Diamond's assertion about the importance of social sciences vs. the physical I think he is right on the mark. Social sciences deal with things that effect us now, as a society, and the way our society will evolve. Issues like political violence, global warming effects, world hunger, economics, and nuclear war effect whether or not we even live. Studies like whether or not we can identify a Quasar, or whether or not we prove or disprove string theory are not as relevant to our welfare or survival as a species.

Last, I would like to address the issue of certainty, since it seems implicit in this debate. I will acknowledge that intuitively it seems as if social sciences are less certain then physical or biological on average, but again I think there are important exceptions.

For example, a new idea that seems to be superseding String-theory in physics is loop quantum gravity:

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Loop quantum gravity (LQG), also known as loop gravity and quantum geometry, is a proposed quantum theory of spacetime which attempts to reconcile the theories of quantum mechanics and general relativity. Loop quantum gravity suggests that space can be viewed as an extremely fine fabric or network "weaved" of finite quantised loops of excited gravitational fields called spin networks. When viewed over time, these spin networks are called spin foam, which should not be confused with quantum foam. A major contender with string theory, loop quantum gravity incorporates general relativity and does not require string theory's higher dimensions.

Now Loop Quantum Gravity and String Theory are to my knowledge the primary contenders for a unified theory in physics (which means physics itself is engaged in a fundamental contradiction, at least at present).

And I may not know exactly how certain either of those theories are, but I am pretty sure none of them are as established or certain as say, Darwin's theory of natural selection, which we know to be a fact, or to take a social phenomenon, decreasing rates of violence since the tribal days, or the general centralization of capital. This is a case where a biological theory, and sociological theories/observations have achieved greater certainty then physics.

I do not believe this is common, or always the case, but it does undermine the presumed hierarchy of "hard" and "soft" sciences.