Page 124 |
Save page Remove page | Previous | 124 of 171 | Next |
|
small (250x250 max)
medium (500x500 max)
Large (1000x1000 max)
Extra Large
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
|
112
0
1
2
3
4
5
0 0.2 0.4 0.6 0.8 1
Rating
Probability Distribution Function
Figure 6.5 Rating Distribution with Stated Rating=1
However, in some survey cases, the ratings are given in a relative way rather than
an absolute way. For example, the sampling from the distribution function may have
an implicit constraint that the sampling ratings from the probability distribution should
sum up to a certain value, such as when designers have 10 points to allocate among the
alternatives.
Suppose an individual designer is given W points among N alternatives. The
relative ratings the designer assigns are r1, r2, … rN. The possible relative values for
Alternative i (1 ≤ i ≤ N ) are in the range [li, ui]. Let x1, x2, … xN be the sampling
variable for the relative ratings for each alternative. The joint distribution function can
be represented by
f(x1, x2, … xN)
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 124 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 112 0 1 2 3 4 5 0 0.2 0.4 0.6 0.8 1 Rating Probability Distribution Function Figure 6.5 Rating Distribution with Stated Rating=1 However, in some survey cases, the ratings are given in a relative way rather than an absolute way. For example, the sampling from the distribution function may have an implicit constraint that the sampling ratings from the probability distribution should sum up to a certain value, such as when designers have 10 points to allocate among the alternatives. Suppose an individual designer is given W points among N alternatives. The relative ratings the designer assigns are r1, r2, … rN. The possible relative values for Alternative i (1 ≤ i ≤ N ) are in the range [li, ui]. Let x1, x2, … xN be the sampling variable for the relative ratings for each alternative. The joint distribution function can be represented by f(x1, x2, … xN) |
