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 may need to rely on models to allow us to explore the consequences of some proposed intervention before we try out a new policy in socio vivo; that was the intended application of SimHealth, but the model in question need not be so explicit as a computer simulation. If we disagree about which model to use, what the model implies, or how to tune the model parameters, then it may be difficult (or even impossible) to come to a policy agreement. In many cases, the lack of scientific consensus on a single model to be used (or at least on relatively small family of models to be used) when working with a particular system is a sign that more work needs to be done: we may not agree, for instance, about whether or not the Standard Model of particle physics is the one we ought to work with in perpetuity, but this disagreement is widely appreciated to be an artifact of some epistemic shortcoming on our part. As we learn more about the world around us, the scientific community will converge on a single model for the behavior of sub-atomic systems.

However, this is not always the case. Suppose we have a pressing public policy decision to make, and that the decision needs to be informed by the best science of the day. Suppose further that we have good reason to think that the sort of singular consensus trajectory that (say) sub-atomic particle models seem to be on is unlikely to appear in this case. Suppose, that is, that we’re facing a policy decision that must be informed by science, but that the science seems to be generating a plethora of indispensable (but distinct) models rather than converging on a single one. If we have good reason to think that this trend is one that is unlikely to disappear with time—or, even more strongly, that it is a trend that is an ineliminable part of the science in question—then we will be forced to confront the problem of how to reform the relationship between science and policy in light of this new kind of science. Wright’s pronouncement that 226