The argument is sometimes made that we have no choice; without a model we will end up relying on biased opinions, guesswork, or even worse. Thus we must develop the best models possible, and use them to evaluate alternative policies. In other words, working with even a highly imperfect model is better than having no model at all. That might be true if we were honest and upfront about the limitations of the model. But often we are not.
Models sometimes convey the impression that we know much more than we really do. They create a veneer of scientific legitimacy that can be used to bolster the argument for a particular policy. This is particularly the case for integrated assessment models (IAMs) which tend to be large and complicated, and are sometimes poorly documented. IAMs are typically made up of many equations, and the equations are hard to evaluate individually (especially given that they are often ad hoc and without a theoretical or empirical foundation), and even harder to understand in terms of their interactions as a complete system. In effect, the model is just a black box: we put in some assumptions about GHG emissions, discount rates, etc., and we get out some results about temperature change, damages, etc. And because the black box is “scientific,” we are supposed to take those results seriously and use them for policy analysis.
“What mattered, however, was that these models required a computer to solve and simulate. The fact that some of the underlying relationships in the models were completely ad hoc and made little sense didn’t matter – the fact that they were computer models made them “scientific” and inspired a certain degree of trust.”
Read the original article THE USE AND MISUSE OF MODELS FOR CLIMATE POLICY.