Auto-generated Relative Importance for Multi-agent Inducing Variable in Uncertain and Preference Involved Evaluation
Bibliographic record
Abstract
Abstract Inducing information and bi-polar preference-based weights allocation and relevant decision-making are one important branch of Yager’s decision theory. In the context of basic uncertain information environment, there exist more than one inducing factor and the relative importance between them should be determined. Some subjective methods require decision makers to indicate the bi-polar preference extents for each inducing factor as well as the relative importance between all the involved inducing factors. However, although the bi-polar preference extents for inducing factors can often be elicited, sometimes decision makers cannot provide the required relative importance. This work presents some approaches to address such problem in basic uncertain information environment. From the mere bi-polar preference extents offered by decision makers, we propose three methods, statistic method, distance method and linguistic variable method, to derive relative importance between different inducing factors, respectively. Each of them has advantages and disadvantages, and the third method serves as a trade-off between the first two methods. The rationale of preference and uncertainty involved evaluation is analyzed, detailed evaluation procedure is presented, and numerical example is given to illustrate the proposals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".