Abstract
The process of selecting among design alternatives is an important activity in the early stages of design. A designer is said to express design preferences when assigning priorities to a set of possible design choices. However, the assignment of preferences becomes more challenging on both a practical and theoretical level when performed by a group. This paper presents a probabilistic approach for estimating a team’s overall preference-related information known as preferential probabilities that extracts information from the natural language used in team discussion transcripts without aggregation of individual team member opinions. Assessment of the method is conducted by surveying a design team to obtain quantitative ratings of alternatives. Two different approaches are applied to convert these ratings into values that may be compared to the results of transcript analysis: the application of a modified Logit model and simulation based on the principle of maximum entropy. The probabilistic approach proposed in the paper represents how likely a choice is to be “most preferred” by a design team over a given period of time. A preliminary design selection experiment was conducted as an illustrative case example of the method. Correlations were found between the preferential probabilities estimated from transcripts and those computed from the surveyed preferences. The proposed methods may provide a formal way to understand and represent informal, unstructured design information using a low overhead information extraction method.
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References
Arnold K (2001) Making team decision. In: Biech E (ed) The Pfeiffer book of successful team building tools. Jossey-Bass/Pfeiffer, San Francisco
Arrow KJ (1970) Social choice and individual values. Yale University Press, New Haven
Arrow KJ, Raynaud H (1986) Social choice and multicriterion decision-making. MIT, Cambridge, MA
Ben-Akiva M, Lerman SR (1985) Discrete choice analysis. MIT, Cambridge, MA
Bertrand M, Mullainathan S (2001) Do people mean what they say? Implications for subjective survey data. Am Econ Rev 91(2):67–72
Bilmes JA (1998) A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical report. International Computer Science Institute, Berkeley, CA, USA
Bockenholt U (2002) A Thurstonian analysis of preference change. J Math Psychol 46(3):300–314
Brans JP, Vincke P (1985) A preference ranking organisation method: (The PROMETHEE method for multiple criteria decision-making). Manag Sci 31(6):647–656
Brockman JB (1996) Evaluation of student design processes. The 26th annual frontiers in education conference, Salt Lake City, UT
Busemeyer JR, Diederich A (2002) Survey of decision field theory. Math Soc Sci 43(3):345–370
Cross N, Christiaans H, Dorst K (1996) Analysing design activity. Wiley, Chichester
Dempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39(1):1–38
Dong A (2005) The latent semantic approach to studying design team communication. Des Stud 26(5):445–461
Dong A (2006a) Concept formation as knowledge accumulation: a computational linguistics study. Artif Intell Eng Des Anal Manuf 20(1):35–53
Dong A (2006b) How am I doing? The language of appraisal in design. Design computing and cognition ‘06 (DCC06). J S Gero. Kluwer, Dordrecht, The Netherlands, pp 385–404
Dym CL, Wood WH, Scott MJ (2002) Rank ordering engineering designs: pairwise comparison charts and Borda counts. Res Eng Des 13(4):236–242
Fishburn PC (1978) Choice probabilities and choice functions. J Math Psychol 18:205–219
Fisher RA (1922) On the mathematical foundations of theoretical statistics. Philos Trans R Soc 222:309–368
Geslin MM (2006) An argumentation-based approach to negotiation in collaborative engineering design. Department of Aerospace And Mechanical Engineering, University of Southern California, Los Angeles
Giffin M, De Weck O, Bounova G, Keller R, Eckert C, Clarkson PJ (2009) Change propagation analysis in complex technical systems. J Mech Des 131(8):081001
Gigone D, Hastie R (1997) The impact of information on small group choice. J Pers Soc Psychol 72(1):132–140
Green PE, Srinivasan V (1990) Conjoint analysis in marketing: new developments with implications for research and practice. J Market 54(4):3–19
Grefenstette G (1993) Automatic thesaurus generation from raw text using knowledge-poor techniques. The 9th annual conference of the UW centre for the new OED and text research, Oxford, England
Hanley N, Mourato S, Wright RE (2001) Choice modelling approaches: A superior alternative for environmental valuation? J Econ Surv 15(3):435–462
Hauser JR, Clausing D (1988) The house of quality. Harv Bus Rev 66(3):63–73
Hazelrigg GA (1998) A framework for decision-based engineering design. J Mech Des 120(4):653–658
Hensher DA, Johnson LW (1981) Applied discrete choice modeling. Halsted Press, New York
Hey JD (1998) Do rational people make mistakes? Game theory, experience, rationality. In: Leinfellner W, Kohler E (eds) Kluwer, The Netherlands, pp 55–66
Honda T, Yang MC, Dong A, Ji H (2010) A comparison of formal methods for evaluating the language of preference in engineering design. ASME design engineering technical conferences. Montreal, Canada
Jabeur K, Martel J-M, Khelifa SB (2004) A distance-based collective preorder integrating the relative importance of the group’s members. Group Decis Negot 13(4):327–349
Jain VK, Sobek DK II (2006) Linking design process to customer satisfaction through virtual design of experiments. Res Eng Des 17(2):59–71
Jaynes ET (1957) Information theory and statistical mechanics. Phys Rev 106(4):620–630
Jaynes ET (1968) Prior probabilities. IEEE Trans Syst Sci Cybern 4(3):227–241
Ji H, Yang MC, Honda T (2007) A probabilistic approach for extracting design preferences from design team discussion. In: Proceedings of ASME 2007 international design engineering technical conferences and computers and information in engineering conference
Keeney RL, Raiffa H (1976) Decisions with multiple objectives: preferences and value tradeoffs. Wiley, New York
Kelley CT (2003) Solving nonlinear equations with Newton’s method. Society for Industrial and Applied Mathematics, Philadelphia
Kohrs A, Merialdo B (2000) Using category-based collaborative filtering in the ActiveWebMuseum. The 2000 IEEE international conference on multimedia and expo, vol 1, pp 351–354
Krantz DH, Luce RD, Suppes P, Tversky A (1971) Foundations of measurement volume 1. Academic Press, New York, NY
Kulok M, Lewis K (2005) Preference consistency in multiattribute decision making. ASME conference proceedings 2005 (4742Xa), pp 291–300
Li W, Jin Y (2006) Fuzzy preference evaluation for hierarchical co-evolutionary design concept generation. ASME conference proceedings (4255X), pp 31–41
Luce RD (1959) Individual choice behavior. Wiley, New York
Mabogunje A, Leifer LJ (1996) 210-NP: measuring the mechanical engineering design process. Frontiers in Education Conference. FIE ‘96. In: Proceedings of 26th annual conference, vol 3, pp 1322–1328
Manski CF (1977) The structure of random utility models. Theory Decis 8:229–254
Miller GA, Beckwith R, Fellbaum C, Gross D, Miller K (1990) WordNet: an on-line lexical database. Int J Lexicogr 3(4):235–244
Otto KN, Antonsson EK (1991) Trade-off strategies in engineering design. Res Eng Des 3(2):87–104
Otto KN, Antonsson EK (1993) The method of imprecision compared to utility theory for design selection problems. ASME 1993 design theory and methodology conference
Packard DJ (1979) Preference relations. J Math Psychol 19(3):295–306
Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) Numerical recipes: the art of scientific computing. Cambridge University Press, New York
Pugh S (1991) Total design: integrated methods for successful product engineering. Addison-Wesley, Wokingham
Reich Y (2010) My method is better!. Res Eng Des 21(3):137–142
Ross SM (2006) Simulation. Academic Press, Burlington, MA
Saaty TL (2000) Fundamentals of decision making and priority theory with the analytic hierarchy process. RWS Publications, Pittsburgh
Scott MJ, Antonsson EK (1998) Aggregation functions for engineering design trade-offs. Fuzzy Sets Syst 99(3):253–264
Scott MJ, Antonsson EK (1999) Arrow’s theorem and engineering design decision making. Res Eng Des 11(4):218–228
Scott MJ, Antonsson EK (2005) Compensation and weights for trade-offs in engineering design: beyond the weighted sum. J Mech Des 127(6):1045–1055
See T-K, Lewis K (2006) A formal approach to handling conflicts in multiattribute group decision making. J Mech Des 128(4):678–688
Shah JJ, Vargas-Hernandez N, Summers JD, Kulkarni S (2001) Collaborative sketching (C-Sketch)—an idea generation technique for engineering design. J Creat Behav 35(3):168–198
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423, 623–656
Song S, Dong A, Agogino AM (2003) Time variation of design “story telling” in engineering design teams. In: Proceedings of the 14th international conference on engineering design (ICED 03), Stockholm, Sweden
Thompson LL (2003) Making the team: a guide for managers. Prentice Hall, Upper Saddle River
Thurston D (1991) A formal method for subjective design evaluation with multiple attributes. Res Eng Des 3(2):105–122
Tribus M (1969) Rational descriptions, decisions, and designs. Pergamon Press, New York
Ueda N, Nakano R (1998) Deterministic annealing EM algorithm. Neural Netw 11(2):271–282
Ueda N, Nakano R, Ghahramani Z, Hinton GE (2000) SMEM algorithm for mixture models. Neural Comput 12(9):2109–2128
von Neumann J, Morgenstern O (1947) Theory of games and economic behaviour. Princeton University Press, Princeton
Wang J (1997) A fuzzy outranking method for conceptual design evaluation. Int J Prod Res 35(4):995–1010
Wassenaar HJ, Chen W (2003) An approach to decision based design with discrete choice analysis for demand modeling. J Mech Des 125(3):490–497
Wassenaar HJ, Chen W, Cheng J, Sudjianto A (2005) Enhancing discrete choice demand modeling for decision-based design. J Mech Des 127(4):514–523
Wood KL, Antonsson EK (1989) Computations with imprecise parameters in engineering design: background and theory. ASME J Mech Transm Autom Des 111(4):616–625
Yang MC (2003) Concept generation and sketching: correlations with design outcome. ASME Conf Proc 37017b:829–834
Yang MC (2005) A study of prototypes, design activity, and design outcome. Des Stud 26(6):649–669
Yang MC, Ji H (2007) A text-based analysis approach to representing the design selection process. In: Proceedings of the 16th international conference on engineering design (ICED 07)
Yang MC, Wood WH, Cutkosky MR (2005) Design information retrieval: a thesauri-based approach for reuse of informal design information. Eng Comput 21(2):177–192
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The work described in this paper was supported in part by the National Science Foundation under Awards CMMI-0830134 and CMMI-0900255. The opinions, findings, conclusions, and recommendations expressed are those of the authors and do not necessarily reflect the views of the sponsors.
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Ji, H., Yang, M.C. & Honda, T. An approach to the extraction of preference-related information from design team language. Res Eng Design 23, 85–103 (2012). https://doi.org/10.1007/s00163-011-0116-7
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DOI: https://doi.org/10.1007/s00163-011-0116-7