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reasons. First, it is an implicit method that does not involve direct questioning of
designers to determine their preferences but instead relies on extraction. This
minimizes intrusion on design activities, and therefore increases its usability. Second,
this approach provides a representation of the overall group’s preferential probabilities
without requiring explicit aggregation of group opinion. Group aggregation of
preference has long been a subject of discussion in engineering design and in other
fields, and this approach sidesteps aggregation by treating a group as a single entity
rather than as a collection of individuals. Third, this approach results in a time-based
profile of preferential probabilities that can offer insights into a team’s design choice
evolution. Preferences are generally assessed at only a single point in time, but in fact
a team’s preferences may change throughout the course of a project, and the ability to
chart these changes is a step towards better understanding how teams make
engineering design decisions in early stage engineering design. Fourth, this study
focuses on extraction of preference information and design selection from the raw
discussion data. It provides a powerful tool to harvest information about how team
works on design selection process. Fifth, the quantitative evaluation in this research
provides ways to convert the traditional preference ratings or utilities to preferential
probabilities which are comparable with those from the transcript. The evaluation
ways could enlighten the research on the conversions between these two forms of
preference information.
In this research, the following four questions are explored:
Object Description
| Title | Extraction of preferential probabilities from early stage engineering design team discussion |
| Author | Ji, Haifeng |
| Author email | haifengj@usc.edu; haifeng.ji@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Industrial & Systems Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2008-08-19 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-10-07 |
| Advisor (committee chair) | Yang, Maria C. |
| Advisor (committee member) |
Lu, Stephen Jin, Yan |
| Abstract | Activities in the early stage of engineering design typically include the generation of design choices and selection among these design choices. A key notion in design alternative selection is that of preference in which a designer or design team assigns priorities to a set of design choices. However, preferences become more challenging to assign on both a practical and theoretical level when done by a group of individuals. Preferences may also be explicitly obtained via surveys or questionnaires in which designers are asked to rank the choices, rate choice with values, or select a "most-preferred" choice. However, these methods are typically employed at a single point of time; therefore, it may not be practical to use surveys to elicit a team’s preference change and evolution throughout the process.; This research explores the text analysis on the design discussion transcripts and presents a probabilistic approach for implicitly extracting a projection of aggregated preference-related information from the transcripts. The approach in this research graphically represents how likely a choice is to be "most preferred" by a design team over time. For evaluation purpose, two approaches are established for approximating a team's "most preferred" choice in a probabilistic way from surveys of individual team members. A design selection experiment was conducted to determine possible correlations between the preferential probabilities estimated from the team's discussion and survey ratings explicitly stated by team members. Results suggest that there are strong correlations between extracted preferential probabilities and team intents that are stated explicitly, and that the proposed methods can provide a quantitative way to understand and represent qualitative design information using a low overhead information extraction method. |
| Keyword | preferences; probabilities; concept selection; design process; design decision-making |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m1635 |
| Contributing entity | University of Southern California |
| Rights | Ji, Haifeng |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Ji-2413 |
| Archival file | uscthesesreloadpub_Volume14/etd-Ji-2413.pdf |
Description
| Title | Page 15 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 3 reasons. First, it is an implicit method that does not involve direct questioning of designers to determine their preferences but instead relies on extraction. This minimizes intrusion on design activities, and therefore increases its usability. Second, this approach provides a representation of the overall group’s preferential probabilities without requiring explicit aggregation of group opinion. Group aggregation of preference has long been a subject of discussion in engineering design and in other fields, and this approach sidesteps aggregation by treating a group as a single entity rather than as a collection of individuals. Third, this approach results in a time-based profile of preferential probabilities that can offer insights into a team’s design choice evolution. Preferences are generally assessed at only a single point in time, but in fact a team’s preferences may change throughout the course of a project, and the ability to chart these changes is a step towards better understanding how teams make engineering design decisions in early stage engineering design. Fourth, this study focuses on extraction of preference information and design selection from the raw discussion data. It provides a powerful tool to harvest information about how team works on design selection process. Fifth, the quantitative evaluation in this research provides ways to convert the traditional preference ratings or utilities to preferential probabilities which are comparable with those from the transcript. The evaluation ways could enlighten the research on the conversions between these two forms of preference information. In this research, the following four questions are explored: |
