Innovative Evolutions in Multicriteria Decision-Making: Discovering Complex Challenges in Contemporary Decision-Making (2021–2023)
Bibliographic record
Abstract
This review systematically analyzes 317 selected papers from 2021 to 2023, focusing on their application in addressing complex contemporary decision-making challenges linked to innovation. The selection includes 180 studies identified through trend-focused keyword searches and 137 studies centered on novel methodologies in multicriteria decision-making (MCDM), obtained through a transparent and reproducible process. The breakdown of the novel MCDM papers emphasized “integration” methods (37.2%), “introduction” methods (26.3%), and “development” methods (36.5%). Moreover, the study highlights the dominance of mathematically based (96%) and deterministic (99%) approaches, underscoring the importance of precise and quantifiable decision frameworks. The emergence of hybrid methods (18.2%), artificial intelligence (AI) and machine learning (ML)-integrated methods (10.9%), and uncertainty-handling methods (10.2%) signifies evolving trends in MCDM. The paper further suggests eight critical future directions, including AI integration, interdisciplinary collaboration, and data analytics, among others, to pave the way for the effective application of MCDM in diverse industries.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".