The human machine interface in decision support systems: comparing the functional mappings of human subjects to those generated by feedforward neural networks

Feedforward neural networks have been used to perform classifications and to learn functional mappings. This paper compares human performance to feedforward neural networks using backpropagation in generating functional relationships from limited data. Many business judgments are made in situations where decision makers are required to infer relationships from partial, incomplete and conflicting information. Experiments conducted to compare the interpolations of humans to the behavior of the neural network models are presented. The experiments are described and the results and their implications in designing human-computer interfaces for decision support systems are discussed.