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58
The preferential probabilities in later time intervals can be calculated recursively
by Equations (4.6) and (4.8). The probability values in different time intervals are
listed in Table 4.2, in the first iteration with p1=0.5 and q1=0.4.
Table 4.2 Preferential Probabilities of Design Alternatives (the First Iteration)
Alternative
Interval
a1 a2 a3
1 0.7798 0.0663 0.1539
2 0.6829 0.0574 0.2597
3 0.4878 0.2328 0.2795
4 0.2179 0.0579 0.7242
5 0.112 0.0934 0.7946
6 0.4419 0.2481 0.3099
7 0.3514 0.2309 0.4177
8 0.2508 0.2993 0.4498
9 0.1058 0.0835 0.8107
10 0.2418 0.2367 0.5215
11 0.2267 0.2971 0.4763
12 0.1497 0.1595 0.6909
Parameters pr and qr would be updated by Equations (4.10) and (4.12) with the
fractional counts calculated from Equations (4.11) and (4.13) in the previous iteration.
After updating the parameters, the new preferential probabilities of alternatives
are re-calculated according to Equations (4.6), (4.7) and (4.8). The above procure is
iterated until converged.
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 70 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 58 The preferential probabilities in later time intervals can be calculated recursively by Equations (4.6) and (4.8). The probability values in different time intervals are listed in Table 4.2, in the first iteration with p1=0.5 and q1=0.4. Table 4.2 Preferential Probabilities of Design Alternatives (the First Iteration) Alternative Interval a1 a2 a3 1 0.7798 0.0663 0.1539 2 0.6829 0.0574 0.2597 3 0.4878 0.2328 0.2795 4 0.2179 0.0579 0.7242 5 0.112 0.0934 0.7946 6 0.4419 0.2481 0.3099 7 0.3514 0.2309 0.4177 8 0.2508 0.2993 0.4498 9 0.1058 0.0835 0.8107 10 0.2418 0.2367 0.5215 11 0.2267 0.2971 0.4763 12 0.1497 0.1595 0.6909 Parameters pr and qr would be updated by Equations (4.10) and (4.12) with the fractional counts calculated from Equations (4.11) and (4.13) in the previous iteration. After updating the parameters, the new preferential probabilities of alternatives are re-calculated according to Equations (4.6), (4.7) and (4.8). The above procure is iterated until converged. |
