The relative predictive accuracy of humans and statistical models has long been the subject of controversy even though models have demonstrated superior performance in many studies. We propose that relative performance depends on the amount of contextual information available and whether it is distributed symmetrically to humans and models. Given their different strengths, human and statistical predictions can be profitably combined to improve prediction.
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