Prediction machines are better than humans at factoring in complex interactions among different indicators, especially in settings with rich data. As the number of dimensions for such interactions grows, the ability of humans to form accurate predictions diminishes, especially relative to machines. However, humans are often better than machines when understanding the data generation process confers a prediction advantage, especially in settings with thin data. We describe a taxonomy of prediction settings (i.e., known knowns, known unknowns, unknown knowns, and unknown unknowns) that is useful
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