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Experimental Evaluation of a Debiasing Method for Analysis in Engineering Design

Part of: Design Education

Published online by Cambridge University Press:  26 July 2019

Thomas Nelius  and
Sven Matthiesen

Abstract

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During analysis in engineering design, systematic thinking errors - so-called cognitive biases - can lead to inaccurate understanding of the design problem. With a simplified version of the Analysis of Competing Hypotheses - ACH method and a simplified decision matrix, the confirmation bias in particular can be minimized. To evaluate this method, it was taught to experienced design engineers and mechanical engineering students. During the experimental evaluation the participants analysed a real technical problem. The procedures and results were compared with a previously conducted study with the same task. The design engineers have not changed their approaches and could not further improve their analysis success. The students profited considerably from the training. They have mentioned twice as much supporting evidence and six times as much contradicting evidence through the training indicating a more extensive analysis. As a result, the students showed significantly fewer signs of confirmation bias than without training. The findings suggest that debiasing strategies should be introduced early in engineering design education.

Keywords

Decision makingDesign cognitionHuman behaviour in designConfirmation BiasDebiasing
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 489 - 498
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) 2019

References

Albers, A. and Braun, A. (2011), “A generalised framework to compass and to support complex product engineering processes”. International Journal of Product Development, Vol. 15 No. 1-3, pp. 6–25.Google Scholar
Albers, A., Bursac, N. and Rapp, S. (2017), “PGE–Produktgenerationsentwicklung am Beispiel des Zweimassenschwungrads”. Forschung im Ingenieurwesen, Vol. 81 No. 1, pp. 13–31.Google Scholar
Blessing, L. T. and Chakrabarti, A. (2009), “DRM, a design research methodology”. Springer Science & Business Media.Google Scholar
Booth, J. W., Reid, T. N., Eckert, C. and Ramani, K. (2015), “Comparing functional analysis methods for product dissection tasks”. Journal of Mechanical Design, Vol. 137 No. 8.Google Scholar
Eckert, C., Ruckpaul, A., Alink, T. and Albers, A. (2012), “Variations in functional decomposition for an existing product: Experimental results”. AI EDAM, Vol. 26 No. 2, pp. 107–128.Google Scholar
Ericsson, K. A. and Simon, H. A. (1993), Protocol analysis. Cambridge, MA: MIT press.Google Scholar
Hallihan, G. M., Cheong, H. and Shu, L. H. (2012, August), “Confirmation and cognitive bias in design cognition”. In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 913–924). American Society of Mechanical Engineers.Google Scholar
Hess, S., Lohmeyer, Q. and Meboldt, M. (2017), “Utilization of Mobile Eye Tracking Data to Improve Engineering Design Education”. In DS 88: Proceedings of the 19th International Conference on Engineering and Product Design Education (E&PDE17), Building Community: Design Education for a Sustainable Future, Oslo, Norway, 7 & 8 September 2017 (pp. 002–007).Google Scholar
Heuer, R. J. (1999), Psychology of intelligence analysis. United States Govt Printing OfficeGoogle Scholar
Lehner, P. E., Adelman, L., Cheikes, B. A. and Brown, M. J. (2008), “Confirmation bias in complex analyses”. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol. 38 No. 3, pp. 584–592.Google Scholar
Matthiesen, S. and Nelius, T. (2018a). “Eye Tracking Study On Successful Micro-Strategies By Design Engineers For The Synthesis-Driven Analysis Of Technical Systems”. In Proceedings of TMCE 2018, Las Palmas de Gran Canaria, Spain, 7-11 May, 2018Google Scholar
Matthiesen, S. and Nelius, T. (2018b). “Managing Assumptions during Analysis - Study on successful Approaches of Design Engineers”. DS 91: Proceedings of NordDesign 2018, Linköping, Sweden, 14th-17th August 2018.Google Scholar
Matthiesen, S., Nelius, T., Pflegler, B. and Gutmann, T. (2017), “Studiendesign zur Untersuchung der synthesegetriebenen Analyse von Konstrukteuren”. In DFX 2017: Proceedings of the 28th Symposium Design for X, 4-5 October 2017, Bamberg, Germany (pp. 299–310).Google Scholar
Meboldt, M., Matthiesen, S. and Lohmeyer, Q. (2012), “The dilemma of managing iterations in time-to-market development processes”. In Second International Workshop on the Modelling and Management of Engineering Processes (MMEP 2012). Eidgenössische Technische Hochschule Zürich.Google Scholar
Nickerson, R. S. (1998), “Confirmation bias: A ubiquitous phenomenon in many guises”. Review of general psychology, Vol. 2 No. 2, p. 175.Google Scholar
Rozenblit, L. and Keil, F. (2002), “The misunderstood limits of folk science: An illusion of explanatory depth”. Cognitive science, Vol. 26 No. 5, pp. 521–562.Google Scholar
Ruckpaul, A., Kriltz, A. and Matthiesen, S. (2014), “Using eye tracking to understand the engineering designers’ behavior in synthesis-driven analyzing processes: experiences in study design”. In International conference on human behavior in design HBiD. Proceedings of International conference on Human Behavior in Design, Ascona, Switzerland, 14–17 October, 2014Google Scholar
Ruckpaul, A., Nelius, T. and Matthiesen, S. (2015), “Differences in analysis and interpretation of technical systems by expert and novice engineering designers”. In DS 80-2 Proceedings of the 20th International Conference on Engineering Design (ICED 15) Vol 2: Design Theory and Research Methodology Design Processes, Milan, Italy, 27-30.07.15Google Scholar
Schweizer, M. (2005), Kognitive Täuschungen vor Gericht. Eine empirische Studie. Dissertation, Rechtswissenschaftlichen Fakultät der Universität ZürichGoogle Scholar
Smith, R. P. and Tjandra, P. (1998), “Experimental observation of iteration in engineering design”. Research in Engineering Design, Vol. 10 No. 2, pp. 107–117.Google Scholar
Wynn, D. C. and Eckert, C. M. (2017), “Perspectives on iteration in design and development”. Research in Engineering Design, Vol. 28 No. 2, pp. 153–184.Google Scholar