Approaches for mapping between preferential probabilities and relative design preference ratings

Assigning preferences to a set of design choices is an important activity in the design process. Previous research proposed a probabilistic approach to extracting preference information from transcripts of design team discussion in a low overhead, implicit way. However, the preference information that was extracted took the form of a "preferential probability," rather than a more traditional preference rating. Preference ratings describe the strength of how much a design team prefers a design choice, and several formal design techniques require such preference ratings. This paper examines the underlying theoretical mappings between preferential probabilities and relative preference ratings, and explores the feasibility of converting preferential probabilities into to relative preference ratings. The paper presents an algorithm for performing this conversion, and then illustrates the use of the algorithm by applying it to a case example. The method proposed in this research has the potential to link implicit preference information generated by real world design teams with formal design decision-making tools.

[1]  J. Martel,et al.  A Distance-Based Collective Preorder Integrating the Relative Importance of the Group's Members , 2004 .

[2]  Reid Hastie,et al.  The impact of information on small group choice. , 1997 .

[3]  George A. Hazelrigg,et al.  A Framework for Decision-Based Engineering Design , 1998 .

[4]  T. Saaty.  Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process , 2000 .

[5]  R. Keeney.  A Group Preference Axiomatization with Cardinal Utility , 1976 .

[6]  Deborah L Thurston,et al.  A formal method for subjective design evaluation with multiple attributes , 1991 .

[7]  Maria C. Yang,et al.  An approach to the extraction of preference-related information from design team language , 2011, Research in Engineering Design.

[8]  Neetu Sharma,et al.  THE NEW LEADERSHIP: MANAGING PARTICIPATION IN ORGANIZATIONS , 2013 .

[9]  Ralph L. Keeney,et al.  Decisions with multiple objectives: preferences and value tradeoffs , 1976 .

[10]  Erik K. Antonsson,et al.  Aggregation functions for engineering design trade-offs , 1995, Fuzzy Sets Syst..

[11]  C. Dym,et al.  Rank ordering engineering designs: pairwise comparison charts and Borda counts , 2002 .

[12]  J. Wang.  (International Journal of Production Research, 35(4):995-1010)A Fuzzy Outranking Method for Conceptual Design Evaluation , 1997 .

[13]  Maria C. Yang,et al.  A Comparison of Formal Methods for Evaluating the Language of Preference in Engineering Design , 2010 .

[14]  Maria C. Yang,et al.  Granularity Enhancement of Extracted Preferential Probabilities From Design Team Discussion , 2011, DAC 2011.

[15]  Susan Carlson Skalak.  House of Quality , 2002 .

[16]  Kemper Lewis,et al.  A Formal Approach to Handling Conflicts in Multiattribute Group Decision Making , 2006 .

[17]  Paul E. Green,et al.  Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice , 1990 .

[18]  David A. Hensher,et al.  Applied discrete-choice modelling , 1980 .

[19]  W. Press,et al.  Numerical Recipes: The Art of Scientific Computing , 1987 .

[20]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[21]  G. Box,et al.  On the Experimental Attainment of Optimum Conditions , 1951 .

[22]  Edwin T. Jaynes,et al.  Prior Probabilities , 1968, Encyclopedia of Machine Learning.

[23]  Kemper Lewis,et al.  Preference Consistency in Multiattribute Decision Making , 2005 .

[24]  Hiroshi Akima,et al.  A Method of Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points , 1978, TOMS.

[25]  M. Ben-Akiva,et al.  Discrete choice analysis , 1989 .

[26]  Erik K. Antonsson,et al.  Trade-off strategies in engineering design , 1991 .

[27]  Kristin L. Wood,et al.  Computations with Imprecise Parameters in Engineering Design: Background and Theory , 1989 .