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One typical use of the optimizer is to fit a model to historical data. Imagine we had historical revenue data in addition to the simulated revenue values in the business model. Rather than trying to maximize revenue, we would like to adjust the parameter values in the model in order to make the simulated data best match the historical data. We can do this using the optimizer by first creating a variable in the model that measures the error for the fit of the simulated to historical data. We could use, for example, classic squared error:

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