Impact of Level of Detail and Information Content on Accuracy of Function Structure-Based Market Price Prediction Models

Abstract

This paper explores the amount of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional composition rules into vocabulary, grammatical, and topological classes and applying them to function structures available in an external design repository. The pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using graph complexity connectivity method. The accuracy is inversely with amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduce the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified through this research approach.