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Student vs Machine: Comparing Artificial Neural Network Predictions with Student Estimates of Market Price Using Function Structure Models

Published online by Cambridge University Press:  26 May 2022

A. R. Patel  and
J. D. Summers
A. R. Patel*
Affiliation:
The University of Texas at Dallas, United States of America
J. D. Summers
Affiliation:
The University of Texas at Dallas, United States of America
*
apurva.patel@utdallas.edu

Abstract

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This paper investigates the use of ANNs to model human behaviour in design by comparing the predictive capability of ANNs and engineering students. Function structure models of 15 products are used as input for prediction. The type of information provided varied between topology and vocabulary. Analysis of prediction accuracy showed that ANNs perform comparably to students. However, students are more precise with their predictions. Finally, limitations and future work are discussed, with research questions presented for subsequent research.

Keywords

artificial intelligence (AI)functional modellingdesign education

Information

Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1669 - 1678
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

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