If we think of our problem of understanding the phenomena as one of seeking the real causes of the outcome in question, and perhaps we also would like a means of predicting behavior or even intervening so as to change the outcomes, this kind of abstraction is potentially valuable, but only if one can manipulate the situation in such a way that behavior changes. Changing a teenager’s future earnings prospects is not feasible. Nor is this a very good predictor: in the case of adolescent pregnancy, such non-explanatory facts as whether the adolescent smokes turns out to predict better.
This kind of problem, in which prediction, rational decisions, and the kind of information available in the thick description (Geertz, 1973) of actual events pull us in different directions, is normal for behavioral science subjects. And they lead to different approaches to the problem of complexity.
One of the standard methods solves the problem in a different way, which compromises between fidelity to the actual thinking and beliefs of the people who are acting and the larger picture of social differences within a society. The method also begins, as the rational choice model does, with some typical, well-proven starting points, but the starting points are “standard demographics” rather than an abstract economic model.
Rates, for example, of smoking, political affiliations, suicide, or adolescent pregnancy, vary between demographic groupings, often dramatically.
At the same time, demographic (and geographic) groupings correspond, loosely, to different social worlds. The analyst’s knowledge of the specifics of the social life of these social worlds may supply other informative categorizations, for example between informal social groupings that can be identified on the basis of members’ knowledge of these social worlds, such as membership in cliques. But there is also a mass of additional concepts, such as “network,” that also enable the analyst to search for categorical distinctions that are possibly relevant to the outcomes of interest, and to test this relevance by comparing rates or degrees of the outcome in question.