An Agent-Based Method to Investigate Customers’ Preference in Product Lifecycle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Consumer requirements for products vary dynamically based on the change of technologies, social influence, individual taste, etc. A sustainable product should meet customer requirements in its lifecycle. Different methods and techniques have been proposed to find possible changes of product needs or customers’ preferences. This paper introduces an agent-based technique to address the change of product requirements. Major contribution of the proposed method is to embed customers’ preference in the analysis of product performance using agent interactions. Using the combination of Quality Function Deployment (QFD), agent-based modeling and data mining methods, customers’ preference trends related to elements and functions of product are simulated. The prediction period is flexible based on estimated product lifecycle. The proposed method is compared with other techniques in a case study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it