More and more, leaders of every sort of enterprise – from corporations to federal, state and local governments – are using mathematical models to help guide them in decision-making. Clearly, the US and UK governments’ approaches to dealing with the Covid-19 pandemic were greatly influenced by the model developed by Neil Ferguson of the Imperial College in London, and his co-workers. The calls for the Green New Deal stand (or fall) in part on the accuracy (or not) of the predictions of numerous global climate models. Many companies rely on weather models to guide important operating decisions. Most financial institutions (e.g., banks and esp. the Federal Reserve) rely on models to develop strategies for dealing with the future.
Leaders are increasingly relying on models because they are a convenient way to harmonize the cacophony of data that assails all of us daily. But as Mae West once said, “A model’s just an imitation of the real thing.” (For those of you who don’t remember Mae West, think of Dolly Parton smirking Nikki Glazer’s innuendo.). Like a Monet landscape, a model accentuates certain facets of reality, ignores others and, sometimes, fills in blank spaces that can’t be seen. Thus, though produced by scientists, there is a certain art in crafting a model – what to include, what to ignore, how to bridge regions where data may not be available.