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Pugh Controlled Convergence and Social Choice Theory

Part of: Design Management

Published online by Cambridge University Press:  26 July 2019

John Morgan Nicholson  and
Paul Collopy

Abstract

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The Pugh Method of Controlled Convergence is evaluated based on social choice theory, both from an axiomatic basis, and by examining all possible cases of attribute ranks for a range of numbers of alternatives and numbers of attributes. The evaluation shows that, for a typical Pugh application, concept selection varies with the arbitrary choice of datum or is simply incorrect in about one-third of the cases. While there are merits to the iteration steps and creation of new alternatives within the Pugh method, a simpler and more expressive concept ranking procedure can give far superior results.

Keywords

Collaborative designDesign theoryDesign methodsSystems Engineering (SE)
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1245 - 1254
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

Arrow, K. (1951), Social Choice and Individual Values, John Wiley and Sons Inc., New York, NY.Google Scholar
Bui, T. (1999), “A parallel, finite-volume algorithm for large-eddy simulation of turbulent flows”, NASA Technical Memorandum 1999-206570, NASA Armstrong Flight Research Center, Edwards, CA.Google Scholar
Clausing, D. (1994), Total Quality Development: A Step-By-Step Guide to World-Class Concurrent Engineering, ASME Press, New York, NY.Google Scholar
Collopy, P. and Mesmer, B. (2015), “Report on the science of systems engineering workshop”, AIAA Paper 2015-1865. American Institute of Aeronautics and Astronautics, Reston, VA.Google Scholar
Condorcet, J. (1785), An Essay on the Application of Analysis to the Probability of Decisions Rendered by A Plurality of Votes, Reprinted by Chelsea Publishing, New York, NY.Google Scholar
de Borda, M. (1784), “On elections by ballot”, Histoire de l'Academie Royale des Sciences, pp. 31–34.Google Scholar
Franssen, M. (2005), “Arrow's theorem, multi-criteria decision problems and multi-attribute preferences in engineering design”, Res. in Eng. Des., Vol. 16, pp. 42–56.Google Scholar
Frey, D, Herder, P., Wijnia, Y., Subrahmanian, E., Katsikopoulos, K. and Clausing, D. (Mar. 2009), “The pugh controlled convergence method: Model-based evaluation and implications for design theory”, Research in Engineering Design, Vol. 20, pp. 41–58.Google Scholar
Haunsperger, D. and Saari, D. (1992), “Dictionaries of paradoxes for statistical tests on k-samples”, Jour. Amer. Statistical Assoc., Vol. 87, pp. 249–272.Google Scholar
Hazelrigg, G. (1996), “The implications of arrow's impossibility theorem on approaches to optimal engineering design”, ASME J. Mech Des., Vol. 118, pp. 161–164.Google Scholar
Pareto, V. (1927), Manual of Political Economy, Augustus M. Kelley Publishing, New York, NY.Google Scholar
Peterson, M. (2017), An Introduction to Decision Theory, Cambridge University Press, New York, NY.Google Scholar
Pugh, S. (1991), Total Design: Integrated Methods for Successful Product Engineering, Addison Wesley Publishing Company, Reading, MA.Google Scholar
Saari, D. (2001), Decisions and Election: Explaining then Unexplained, Cambridge University Press, New York, NY.Google Scholar
Saari, D. (2010), “Aggregation and multilevel design for systems: Finding guidelines”, ASME J. Mech Des., Vol. 132.Google Scholar
Saari, D. and Seiberg, K. (2001), “Are pairwise comparisons reliable?”, Res. Eng. Des., Vol. 15, pp. 62–71.Google Scholar
Saaty, T. (2008), Group Decision Making: Drawing Out and Reconciling Differences, RWS Publications, Pittsburgh, PA.Google Scholar
Sen, A. (1970), “The impossibility of the paretian liberal”, Journal of Political Economy, Vol. 78, pp. 152–157.Google Scholar
Thurston, D. (2006), “Multi-attribute Utility Analysis of Conflicting Preferences”, In: Lewis, K.E., et al. (Ed.), Decision Making in Engineering Design, ASME Press, New York, New York. pp. 125–133.Google Scholar
von Neumann, J. and Morgenstern, O. (1947), Theory of Games and Economic Behavior, Princeton University Press, Princeton, pp. 16–31.Google Scholar