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135
probabilities may lead to a novel way to understand the evolving nature of a team’s
preferences over the life of a project.
Is the preference information extracted in proposed method consistent with the
actual preferences? This research employs both qualitative reading of transcripts and
quantitative comparison with survey ratings to evaluate the preferential probabilities
extracted from the transcripts. Both results show the consistency for the case studies
and further validate the effectiveness of PPT. Two approaches including the Logit
Model (Chapter 5) and PPS (Chapter 6) are employed for graphical comparison and
quantitative comparison on geometric distances, cosine similarity and correlation
coefficients. The Logit Model is a direct and quick way to convert ratings to
preferential probabilities for comparison purposes, but because of its limitations on
design preferences, another approach (PPS) under the principle of maximum entropy
is established as a benchmark to quantitatively evaluate the probabilistic approach for
extracting the group’s preferences from the transcript. These two approaches
preliminarily try the links between the traditional preference ratings and the
preferential probabilities and may enlighten the research on converting the preferential
probabilities to the traditional preference ratings.
The approach proposed in this study for implicitly extracting the preference
information from the discussion transcript can be extended to other fields that include
a process of selecting among alternatives. It can be extended for use in economics to
extract consumers preferences from group discussion such as focus groups. However,
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 147 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 135 probabilities may lead to a novel way to understand the evolving nature of a team’s preferences over the life of a project. Is the preference information extracted in proposed method consistent with the actual preferences? This research employs both qualitative reading of transcripts and quantitative comparison with survey ratings to evaluate the preferential probabilities extracted from the transcripts. Both results show the consistency for the case studies and further validate the effectiveness of PPT. Two approaches including the Logit Model (Chapter 5) and PPS (Chapter 6) are employed for graphical comparison and quantitative comparison on geometric distances, cosine similarity and correlation coefficients. The Logit Model is a direct and quick way to convert ratings to preferential probabilities for comparison purposes, but because of its limitations on design preferences, another approach (PPS) under the principle of maximum entropy is established as a benchmark to quantitatively evaluate the probabilistic approach for extracting the group’s preferences from the transcript. These two approaches preliminarily try the links between the traditional preference ratings and the preferential probabilities and may enlighten the research on converting the preferential probabilities to the traditional preference ratings. The approach proposed in this study for implicitly extracting the preference information from the discussion transcript can be extended to other fields that include a process of selecting among alternatives. It can be extended for use in economics to extract consumers preferences from group discussion such as focus groups. However, |
