Innovative Evolutions in Multicriteria Decision-Making: Discovering Complex Challenges in Contemporary Decision-Making (2021–2023)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it