Increasing Model Precision Can Reduce Accuracy

Carlos F. Daganzo
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Carlos F. Daganzo: University of California, Berkeley, California

Transportation Science, 1987, vol. 21, issue 2, 100-105

Abstract: 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.

Date: 1987
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:21:y:1987:i:2:p:100-105

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