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 messier problems in the “high level” special sciences--particularly those concerned with complex systems. Weisberg (2007) suggests a framework that may be more readily applicable to projects like climate modeling. Weisberg discusses a number of different senses of ‘idealization,’ but for our purposes the concept that he calls “multiple-model idealization” (MMI) is the most interesting. Weisberg defines MMI as ”the practice of building multiple related but incompatible models, each of which makes distinct claims about the nature and causal structure giving rise to a phenomenon.” He presents the model building practice of the United States’ National Weather Service (NWS) as a paradigmatic example of day-to-day MMI: the NWS employs a broad family of models that can incorporate radically different assumptions not just about the parameters of the system being modeled, but of the dynamical form being modeled as well.

This pluralistic approach to idealization sidesteps the puzzle we discussed at the close of Section 6.3.2. On Norton’s view, it’s hard to see how idealizations represent in the first place, since the discussion of representation can’t even get off the ground without an articulation of a “target system” and the novel idealized system cooked up to represent it. Weisberg-style pluralistic appeals like MMI are different in subtle but important ways. Weisberg’s own formulation makes reference to a “phenomenon” rather than a “target system:” a semantic difference with deep repercussions. Most importantly, MMI-style approaches to modeling and idealization let us start with a set of predictive and explanatory goals to be realized rather than some putative target system that we may model/approximate/idealize more-or-less perfectly.

By Norton’s own admission, his view of approximation and idealization is one that grounds the distinction firmly in representational content. While this approach to the philosophy of

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