M-MACBETH for Multicriteria Resource Allocation
Bibliographic record
Abstract
The M-MACBETH DSS (www.m-macbeth.com) implements the MACBETH approach to evaluate projects on multiple criteria base only on qualitative pairwise comparison judgements about difference of attractiveness. This multicriteria decision aid tool supports the selection of a good/best project. However, in a context of scarce resources, choosing a portfolio of projects is a more demanding problem, as it requires not only to balance benefits against costs and the risks of realising the benefits, but also to evaluate several projects together. There are several DSS for multicriteria portfolio analysis, that differ on the resource allocation procedure used: prioritizing projects by decreasing values of benefit-to-cost ratios or identifying the optimal portfolio by mathematical programming. It is well-known that the portfolios arising from the approaches do not always coincide, therefore it would be useful to combine both approaches, but few DSS do so. Within this framework, a new resource allocation component of the M-MACBETH DSS was developed, which implements the two approaches interactively. One distinctive feature is the ability to explicitly address the baseline problem, by sensitivity analysis of the stability of priority ranking and of the optimal portfolio. Besides, it is possible to deal with other constraints than the budget limitation, such as to force the inclusion or exclusion of projects from the portfolio or to model the mutually exclusion between projects.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.117 | 0.046 |
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".