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53
Similarly, from Equation (4.2), P(εi = ak | πi = ak ) is independent of i and k. It
means that in a certain time interval, designers have a fixed probability to utter the
same alternative as the one they prefer the most.
By the maximum likelihood, P(εi = ak | πi = ak ) can be estimated as
P(εi = ak | πi = ak ) = 1 1
1 1 1
( , )
( , )
i m i m
i T m N
i n i m
i T n N m N
C a a
C a a
ε π
ε π
≤ ≤ ≤ ≤
≤ ≤ ≤ ≤ ≤ ≤
= =
= =
Σ Σ
Σ Σ Σ
(4.12)
where C(εi = an ,πi = am ) is also a fractional count, which counts the number of cases
that an is uttered while am is most-preferred in the same time interval. It can be
calculated as follows.
C(εi =an,πi =am)=C(εi =an)P(πi =am | σi ,σi−1,...,σ1) (4.13)
where C(εi = an ) is the number of utterances of Alternative an in the time interval i.
Equations (4.12) and (4.13) calculate q based on the samples of alternative
utterances and preferences. When using the above procedure to calculate q, it should
be noted that the value of q should be more than 1/N.
Because the EM algorithm is guaranteed to improve the probability of the sample
of alternative occurrences at each iteration, p and q will converge to values which try
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 65 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 53 Similarly, from Equation (4.2), P(εi = ak | πi = ak ) is independent of i and k. It means that in a certain time interval, designers have a fixed probability to utter the same alternative as the one they prefer the most. By the maximum likelihood, P(εi = ak | πi = ak ) can be estimated as P(εi = ak | πi = ak ) = 1 1 1 1 1 ( , ) ( , ) i m i m i T m N i n i m i T n N m N C a a C a a ε π ε π ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤ = = = = Σ Σ Σ Σ Σ (4.12) where C(εi = an ,πi = am ) is also a fractional count, which counts the number of cases that an is uttered while am is most-preferred in the same time interval. It can be calculated as follows. C(εi =an,πi =am)=C(εi =an)P(πi =am | σi ,σi−1,...,σ1) (4.13) where C(εi = an ) is the number of utterances of Alternative an in the time interval i. Equations (4.12) and (4.13) calculate q based on the samples of alternative utterances and preferences. When using the above procedure to calculate q, it should be noted that the value of q should be more than 1/N. Because the EM algorithm is guaranteed to improve the probability of the sample of alternative occurrences at each iteration, p and q will converge to values which try |
