Increasing Model Precision Can Reduce Accuracy

In the field of logistics, a variable that is to be predicted (e.g., cost) often varies in a nonsmooth, irregular, but known manner, with various factors (e.g., distances, quantity, and density of material to be carried, etc.). This paper identifies conditions, where given approximate input factors, a prediction of the variable is less error prone if one uses a smooth approximation to the exact function of the factors. This phenomenon, which is quite prevalent, may enhance the appeal of continuous approximation models in some instances.