Risk Decision Making Based on Decision-theoretic Rough Set: A Three-way View Decision Model
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
Rough set theory has witnessed great success in data mining and knowledge discovery, which provides a good support for decision making on a certain data.However, a practical decision problem always shows diversity under the same circumstance according to different personality of the decision makers.A simplex decision model can not provide a full description on such diverse decisions.In this article, a review of Pawlak rough set models and probabilistic rough set models is presented, and a three-way view decision model based on decision-theoretic rough set model is proposed, in which optimistic decision, pessimistic decision, and equable decision are provided according to the cost of misclassification.The thresholds of probabilistic inclusion are calculated based on minimization of risk cost under respective decision bias.The study not only presents a new theoretic decision model considering the different personality of the decision makers, but also provides a practical explanation and an illustrative example on diverse risk bias decision.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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