"[T]he purpose of a model is not to be realistic. After all, we already possess a model that is completely realistic—the world itself. The problem with that “model” is that it is too complicated to understand. A model’s purpose is to provide insights about particular features of the world. If a simplifying assumption causes a model to give incorrect answers to the questions it is being used to address, then that lack of realism may be a defect. . . . If the simplification does not cause the model to provide incorrect answers to the questions it is being used to address, however, then the lack of realism is a virtue: by isolating the effect of interest more clearly, the simplification makes it easier to understand."Models are never perfectly realistic. I don't think I've ever suggested that perfect realism is necessary or even good, so I should hope Bob doesn't think that's my standard. But realism of assumptions matters so I will never say something like "all that matters is whether your predictions are accurate". It's not all that matters. One can plausibly sacrifice predictive power for realistic assumptions just as one can plausibly sacrifice realism in assumptions for predictive power.
--David Romer, Advanced Macroeconomics (New York: McGraw-Hill, 1996), pp. 11–12.
The scientist's utility function is quite well behaved. When we are looking for assumption realism/empirical validity bundles, our utility functions are concave and we have diminishing marginal rates of substitution between the two. The real world and the critiques of our peers present us with the constraints that we're optimizing against.