Often in environmental science we find ourselves having to work with large, complex models that we didn't write ourselves, that we don't fully understand, and which may have known problems that would be difficult to fix. I wondered if anyone has considered appropriate and robust methods for working with such models, in order to get the results we want without having to start from scratch? One of my favourite papers is "Six (or so) things you can do with a bad model" by James S Hodges (Op. Res. 39(3) 355-365, 1991), which is along these lines. For example, even if a model is wrong, you can use it to generate ideas, for training, or to store data. Usually we pretend that our models are without flaws, but I would be interested in modelling approaches that are valid even when the model is not. Any ideas?

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