Three goals for computational models

The idea of computing machines was born to develop an algorithmic theory of thought — to learn if we could always decide the validity of sentences in axiomatic systems — but some of the first physical computing machines were build to calculate physics. In particular, they were tools of war, used to predict ballistic trajectories and the effects of not-yet-constructed hydrogen bombs. War time scientists had enough confidence in these computational models — Fermi’s bet notwithstanding — that they were willing to trust the computations’ conclusion that the Trinity test would not incinerate the atmosphere. Now computational modeling is so common, that we hear model predictions of the state of our (unincinerated) atmosphere every morning on the local weather report. Much progress has been made in modeling, yet although I will heed the anchor’s advice to pack an umbrella, I can’t say that I trust most computational models in domains outside of physics and chemistry. Actually, my trust in computational models has only gone down with exposure. Fortunately, modeling can have many goals and I can think of models as tools for (at least) three things: (1) predicting future outcomes of an external reality; (2) clarifying and formalizing (more verbal) theories; or (3) communication and rhetoric.