There are hundreds of different types of performance measures for numerical models, many of which are applicable to many different types of models. But a good model doesn't just perform well, it performs well while being as simple as possible, and no simpler. Are there any general-ish measures of a model's parsimony?
In some fields you can build in some kind of ensured parsimony, like in evolutionary neural-network model generation, where you can punish complexity. I'm thinking more along the lines of physics-based models, where the model is to some extent defined by theory.