On Modeling, 2

We build models because we want to highlight the most relevant factors of the circumstances. In a broad sense, language, music, painting are all different forms of modeling: they stress certain aspects of reality by ignoring the rest.

Models must be useful. Unlike simplicity and generality, usefulness is trickier to assess. The challenge arises from the parallel of two worlds: the real and the model world; so there is a gap one must be able to cross.

The main idea of modeling is that, if we isolate the substantive factors of a system into a model, then the outcomes generated by the model should also find counterparts in the real world. Or, if we observes certain outcomes in the real world, and the model produces similar outcomes, then these substantive factors should be determinants in the real world, too.

Both arguments rely on inductive inference. In general, the model and real world should share similarities in  structure, dynamics, and outcomes. Yet these similarities are no guarantee that two worlds move in lock step. Cases abound of otherwise. Thus, relevance has to be taken by faith, not by the logic reasoning. There are gaps that one must be willing to cross.

On Modeling, 1

All disciplines use models. So what is a model? It is a useful abstraction of reality. It is an abstraction because it does not intend to recreate the real world in a one-to-one scale. Rather, it distills the essence of a situation so that it can be readily deployed in other similar situations.

One reason we need models is because we have limited cognitive capacity—you cannot keep track of all the details all the time. Modeling is a way to filter out irrelevance and to focus on what matters. So models are the lens we use to see the world we want to see. And simplicity is the first criterion.

The second criterion is generality. We don’t want to build a new model for every single situation; that would defeat the very purpose of modeling. Rather, we would like our models to have sufficient generality, so that we can apply the same models to different situations with limited modifications.