Conditional Preference Networks with User's Genuine Decisions
Why this work is in the frame
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Bibliographic record
Abstract
Abstract User's choices involve habitual behavior and genuine decision. Habitual behavior is often expressed using preferences. In a multiattribute case, the Conditional Preference Network (CP‐net) is a graphical model to represent user's conditional ceteris paribus (all else being equal) preference statements. Indeed, the CP‐net induces a strict partial order over the outcomes. By contrast, we argue that genuine decisions are environmentally influenced and introduce the notion of “comfort” to represent this type of choices. In this article, we propose an extension of the CP‐net model that we call the CP‐net with Comfort (CPC‐net) to represent a user's comfort with preferences. Given that preference and comfort might be two conflicting objectives, we define the Pareto optimality of outcomes when achieving outcome optimization with respect to a given CPC‐net. Then, we propose a backtrack search algorithm to find the Pareto optimal outcomes. On the other hand, two outcomes can stand in one of six possible relations with respect to a CPC‐net. The exact relation can be obtained by performing dominance testing in the corresponding CP‐net and comparing the numeric comforts.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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