ABSTRACT
A model is an abstraction of an object, system, or process that permits knowledge to be gained about reality by conducting experiments on the model. Constraining modeling to that of interest to the geographer, a model should abstract geographic space and the dynamic processes that take place within it, and be able to simulate spatial processes
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usefully. At the absurd extreme, a model could be more complicated than the data chosen to represent the spatio-temporal dynamics in question. Occam’s razor tells us, of course, that when two models are of equal explanatory power, the simpler one is preferred. Einstein stated that “a model should be as simple as possible and yet no simpler.” Some earth systems are so predictable that they are indeed amenable to modeling with the simplest of models. For example, the drop in atmospheric pressure as a function of elevation is so predictable as to be suitable for building a measuring instrument for the latter using the former as a proxy. Of course few human systems, or coupled human/environmental systems are this simple. Geography’s past is laced with attempts to use overly simple predictive or explanatory models for human phenomena, the gravity model being a good example. In some cases, the simplicity these models offer serves to produce insight, or at least fill textbooks, in the absence of geographic laws. In this chapter, I raise the issue of model simplicity, and discuss Einstein’s question of how much simplicity is enough.
