A New Application for the Goal Programming—The Target Decision Rule for Uncertain Problems
Bibliographic record
Abstract
The goal programming (GP) is a well-known approach applied to multi-criteria decision making (M-DM). It has been used in many domains and the literature offers diverse extensions of this procedure. On the other hand, so far, some evident analogies between M-DM under certainty and scenario-based one-criterion decision making under uncertainty (1-DMU) have not been revealed in the literature. These similarities give the possibility to adjust the goal programming to an entirely new domain. The purpose of the paper is to create a novel method for uncertain problems on the basis of the GP ideas. In order to achieve this aim we carefully examine the analogies occurring between the structures of both issues (M-DM and 1-DMU). We also analyze some differences resulting from a different interpretation of the data. By analogy to the goal programming, four hybrids for 1-DMU are formulated. They differ from each other in terms of the type of the decision maker considered (pessimist, optimist, moderate). The new decision rule may be helpful when solving uncertain problems since it is especially designed for neutral criteria, which are not taken into account in existing procedures developed for 1-DMU.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".