Human Terrain & Anthropology (merged thread)

[marct]

Hi M.L.,

Well, I'll let Tom handle the "hard" (hah! Stats is hard?!?!) side but, from what I remember, R-squared is a CYA fudge factor applied to an apparent (presumed?) [pseudo-]causal relationship. You know the type "X causes Y with .27% rsq validity; of course, Y causes X with .23% rsq validity" .

If memory serves, R-square values are a numeric expression of the probability that a given model will predict future behavior. Perhaps the problem is social scientists are trying to predict behavior. This seems somewhat difficult to accomplish in a social system where the agents have a choice, emotions, subjective rationalities, cultural forces, etc...

It this belief that in order to be a "science" something must be quantified using the simplest form of mathematics (statistics). Sure, we're trying to predict behaviour, but the people who rely on simplistic models a la Quettelet are committing an ID10T error: Markov chains, probability "sprays", Chaos and Catastrophe theory are better languages for some of what we study for exactly the reasons you list. Then again, most of us got into the social sciences to escape from math....

By way of contrast, in the "hard" sciences atoms (above the quantum level), molecules, etc... obey predictable laws. Thus, it would seem models which predict the behavior of agents (that themselves must follow predictable laws) would result in very high R-square values.

Well, yeah. Then again, almost everyone seems to forget that "prediction" is based on probability, and it can't account for a "new" event (Taleb's Black Swans). I've always suspected that this is one of the reasons why people who get heavily involved in the philosophy of science and, especially, cosmology get heavily into some very "odd" head spaces that are right outside of the common understanding of causation.

I don't mean to assert that there isn't a significant amount of stupidity in the social sciences (there is in every discipline). Rather, I would suggest less that reliable predictive models in social systems says more about the system in question and the approach to understanding it than it does about the scientists.

Often, the answer you get depends on the question you ask. Perhaps we are asking the wrong questions? I would argue the failings in social science are related to our attempt to study it as if it were a hard science; that is to say reductionist, analytical, linear thinking.

Totally agree with that ! It's one of the reasons I use both music and dance to try to grok what I study. That, BTW, is one of the lesser advertised / discussed components of Anthropology ("groking" I mean). There's little written about it, barring a chapter by Rhoda Metreaux from the '50's, and we only seem to talk about it after the third drink.

So, what happens if you don't "ask questions" but, rather, set your mind in "neutral" and just "perceive"? That's what a good fieldworker does (or should do) when confronted with something which they have no good predictive model for. When I was doing my grad work, we used to have a joke (well several...) about the differences between Anthropologists and Sociologist:

An Anthropologist and a Sociologist walk into a bar and see a good looking women at the bar. The Sociologist walks up to the bar next to one of the women, orders a beer and, looking out the side of his eye, carefully slides a paper in front of the woman which reads "Would you like to XXXX? Yes ___ No ___"; gets slapped and slinks off to watch the game on TV. The Anthropologist shakes his head, goes over to the other side of the woman, orders a Scotch and mumbles "Men!". Five minutes later, he and the woman leave the bar.

For example, if you are doing any type of research you must state your independent and dependent variables. However, social systems are not composed of independent and dependent variables, and applying such a construct is doomed to fail. The construct asks the wrong question, i.e. "What are the cause and effect relationships?" There are few cause and effect relationships in social systems because people have choices.

Social systems are composed of interdependent variables. Therefore, we cannot study one or two in isolation, but we must study the system as a whole to understand the interdependency of the variables and the emergent properties of the system.

Well, now here's an interesting question: why do you assume variables exist ? I would argue that patterns and forms exist in people's minds and exert a sense of "rightness" on individuals, but "variables"? That, I suspect, is highly debatable. Now, I could stop playing silly semantics, but I think that this is, really, an important semantic distinction. All too often, "variables" are proxy variables - my favorite one has always been church attendance as a proxy for religious belief: it fails, in Canada at least, because church attendance or, rather, the spike in the late 1980's - early '90's, was related to a general pattern expectation that it was good / safe for the children. It also fails in a whole slew of other areas as well....

So, I've always held that what we should be looking at is a) a pattern of behaviour and b) the "explanation" or "meaning structure" ascribed to that behaviour by those who perform it is a much better, and more useful, unit of analysis and theory construction.

In short, social systems can't be studied like physical or chemical systems, yet this is what we are doing. As long as we continue to do so, we are unlikely to have much success.

Totally agree.

Cheers,

Marc