science systems

schelling on the purposes of modelling

Simplified models of artificial situations can be offered for either of two purposes. One is ambitious: these are “basic models” – first approximations that can be elaborated to simulate with higher fidelity the real situations we want to examine. The second is modest: whether or not these models constitute a “starting set” on which better approximations can be built, they illustrate the kind of analysis that is needed, some of the phenomena to be anticipated, and some of the questions worth asking.

The second, more modest, accomplishment is my only aim in the preceding demonstrations. The models were selected for easy description, easy visualization, and easy algebraic treatment. But even these artificial models invite elaboration. In the closed model [of self-sorting of a fixed population across two sub-groups (‘rooms’) according to individual’s preferences for a group mean age closest to their own], for example, we could invoke a new variable, perhaps “density”, and get a new division between the two rooms at a point where the greater attractiveness of the age level is balanced by the greater crowding. To do this requires interpreting “room” concretely rather than abstractly, with some physical dimension of some facility in short supply. (A child may prefer to be on the baseball squad which has older children, but not if he gets to play less frequently; a person may prefer to travel with an older group, but not if it reduces his chances of a window seat; a person may prefer the older discussion group, but not if it means a more crowded room, more noise, fewer turns at talking, and less chance of being elected chairman.) As we add dimensions to the model, and the model becomes more particular, we can be less confident that our model is of something we shall ever want to examine. And after a certain amount of heuristic experiments with building blocks, it becomes more productive to identify the actual characteristics of the phenomena we want to study, rather than to explore general properties of self-sorting on a continuous variable. Nursing homes, tennis clubs, bridge tournaments, social groupings, law firms, apartment buildings, undergraduate colleges, and dancing classes may display a number of similar phenomena in their membership; and there be a number of respects in which age, I.Q., walking speed, driving speed, income, seniority, body size, and social distinction motivate similar behaviours. But the success of analysis eventually depends as much on identifying what is peculiar to one of them as on the insight developed by studying what is common to them.

Schelling, T. (1978/2006). Micromotives and Macrobehaviour, pp183-184.

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