A Biobjective Optimization Model for Expert Opinions Aggregation and Its Application in Group Decision Making
Bibliographic record
Abstract
Expert opinions aggregation is a generic part of the group decision making (GDM) problem. The challenge of expert opinions aggregation is to reduce the subjectivity in the process as much as possible and improve the reliability of the aggregated opinion. Most of the existing literature try to eliminate the subjectivity but seldom consider the reliability of the aggregation result. In this article, we propose a new criterion that contains consensus level and confidence level to improve both objectivity (i.e., consensus) and reliability (i.e., no absurd result) with the experts' opinions being represented as probability density functions. Subsequently, the expert opinion aggregation problem is formulated as a biobjective optimization model. The Survey of Professional Forecasters is used as an example to examine the feasibility and accuracy of the proposed approach and the result shows that the new approach can provide a better estimation than that of the single objective model in the literature. To our best knowledge, the proposed criterion is new in the literature of GDM along with relevant problems. The proposed criterion is actually a pilot work to probe the problem of the quality of a GDM process, which is largely ignored in the field of GDM.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".