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106
Chapter 6 Comparative Study between Transcripts and Surveys (2)
– Evaluation with PPS: Preferential Probabilities Translated from
Surveys under Principle of Maximum Entropy
This chapter develops a new simulation approach for translating the surveys to
preferential probabilities under the principle of maximum entropy [65, 66] so that
these preferential probabilities can be compared with those found using PPT. The
principle of maximum entropy is chosen because it gives the least biased distribution
with the given information. In this method, it does not assume a distribution a priori.
The distribution and the parameters are calculated while maximizing the information
entropy so that it does not have any unknown parameters such as λ in the Logit model.
This approach also considers the boundary constraint while applying the principle of
maximum entropy, which generates distinctive distributions when the stated ratings
are at the different positions in the range.
For simplicity’s sake, the approach established in Chapter 4 for extracting the
preferential probabilities from the transcript is called “PPT” (Preferential Probabilities
from Transcript), and the approach proposed in this chapter for translating survey
rating preferences into preferential probabilities under maximum entropy principle is
called “PPS” (Preferential Probabilities from Surveys), while the approach drawn from
the Logit model for converting ratings to preferential probabilities established in
Chapter 5 is called “Logit” in order for distinctions.
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 118 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 106 Chapter 6 Comparative Study between Transcripts and Surveys (2) – Evaluation with PPS: Preferential Probabilities Translated from Surveys under Principle of Maximum Entropy This chapter develops a new simulation approach for translating the surveys to preferential probabilities under the principle of maximum entropy [65, 66] so that these preferential probabilities can be compared with those found using PPT. The principle of maximum entropy is chosen because it gives the least biased distribution with the given information. In this method, it does not assume a distribution a priori. The distribution and the parameters are calculated while maximizing the information entropy so that it does not have any unknown parameters such as λ in the Logit model. This approach also considers the boundary constraint while applying the principle of maximum entropy, which generates distinctive distributions when the stated ratings are at the different positions in the range. For simplicity’s sake, the approach established in Chapter 4 for extracting the preferential probabilities from the transcript is called “PPT” (Preferential Probabilities from Transcript), and the approach proposed in this chapter for translating survey rating preferences into preferential probabilities under maximum entropy principle is called “PPS” (Preferential Probabilities from Surveys), while the approach drawn from the Logit model for converting ratings to preferential probabilities established in Chapter 5 is called “Logit” in order for distinctions. |
