Using prediction to provide decision support for the elicitation of user preferences
Bibliographic record
Abstract
In multi-criteria decision making problems such as selecting development policies, selecting software products, or searching for commodities to purchase, it is necessary to have a precise model of the user preferences. Studies have revealed that often people are unable to state their preferences up front, and that they start to evaluate solution alternatives with a small set of high-value preferences; but change the value of those preferences as they discovery other solution features which they can incorporate into their preference models (B. Faltings et al., 2004). While, a variety of preference elicitation models have been proposed, limited or no effort has been made to utilize historical data to provide decision support for the elicitation of user preferences. In this paper, we discuss using neural net to take advantage of historical data, and provide decision support for developing user preference models, as well as preference value functions; from a set of high-value preferences. Moreover, we report results of using our technique to elicit the user preferences for evaluating and selecting a commercial-off-the-shelf software component
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How this classification was reachedexpand
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".