Why Model

Why Model? Joshua M. Epstein∗
Based on the author's 2008 Bastille Day keynote address to the Second World Congress on Social Simulation, George Mason University, and earlier addresses at the Institute of Medicine, the University of Michigan, and the Santa Fe Institute.

The modeling enterprise extends as far back as Archimedes; and so does its misunderstanding. I have been invited to share mythoughts on some enduring misconceptions about modeling. I hope that by doing so, I will give heart to aspiring modelers, and give pause to misguided critics. Why Model? The first question that arises frequently--sometimes innocently and sometimes not--is simply, "Why model?" Imagining a rhetorical (non-innocent) inquisitor, my favorite retort is, "You are a modeler." Anyone who ventures aprojection, or imagines how a social dynamic--an epidemic, war, or migration--would unfold is running some model. But typically, it is an implicit model in which the assumptions are hidden, their internal consistency is untested, their logical consequences are unknown, and their relation to data is unknown. But, when you close your eyes and imagine an epidemic spreading, or any other social dynamic, youare running some model or other. It is just an implicit model that you haven't written down. This being the case, I am always amused when these same people challenge me with the question, "Can you validate your model?" The appropriate retort, of course, is, "Can you validate yours?" At least I can write mine down so that it can, in principle, be calibrated to data, if that is what you mean by"validate," a term I assiduously avoid (good Popperian that I am). The choice, then, is not whether to build models; it's whether to build explicit ones. In explicit models, assumptions are laid out in detail, so we can study exactly what they entail. On these assumptions, this sort of thing happens. When you alter the assumptions that is what happens. By writing explicit models, you let othersreplicate your results. You can in fact calibrate to historical cases if there are data, and can test against current data to the extent that exists. And, importantly, you can incorporate the best domain (e.g., biomedical, ethnographic) expertise in a rigorous way. Indeed, models can be the focal points of teams involving experts from many disciplines.

Senior Fellow in Economic Studies and Directorof the Center on Social and Economic Dynamics, the Brookings Institution, and External Professor, The Santa Fe Institute. I thank Ross A. Hammond for insightful comments.
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Another advantage of explicit models is the feasibility of sensitivity analysis. One can sweep a huge range of parameters over a vast range of possible scenarios to identify the most salient uncertainties, regions ofrobustness, and important thresholds. I don't see how to do that with an implicit mental model. It is important to note that in the policy sphere (if not in particle physics) models do not obviate the need for judgment. However, by revealing tradeoffs, uncertainties, and sensitivities, models can discipline the dialogue about options and make unavoidable judgments more considered. Can You Predict? Nosooner are these points granted than the next question inevitably arises: "But can you predict?" For some reason, the moment you posit a model, prediction--as in a crystal ball that can tell the future--is reflexively presumed to be your goal. Of course, prediction might be a goal, and it might well be feasible, particularly if one admits statistical prediction in which stationary distributions (ofwealth or epidemic sizes, for instance) are the regularities of interest. I'm sure that before Newton, people would have said "the orbits of the planets will never be predicted." I don't see how macroscopic prediction-pacem Heisenberg--can be definitively and eternally precluded. Sixteen Reasons Other Than Prediction to Build Models But, more to the point, I can quickly think of 16 reasons other...