Bibliographic record
Abstract
Big data, Internet, Internet of things, and cloud computing have profoundly affected decision paradigms and methods in various disciplines and application areas, ranging from business and management to finance and economics, decision sciences, system evaluation, forecasting, psychology, sociology, tourism, health, safety, engineering, smart city management, and environmental management.Intelligent computing technology and intelligent decision models are developed to meet the needs of academia and practitioners.This special issue of International Journal of Computational Intelligence Systems (IJCIS) entitled "Intelligent Decision Analysis and Applications" aims to provide a forum for some state-of-the-art research in this emerging field and outline new and important developments in fundamentals, approaches, models, and intelligent decision support systems with applications to different areas.Twelve papers have been selected for publication in this special issue.Below is a brief summary of these twelve papers.The paper "A Bargaining Solution with Level Structure" (https:// doi.org/ 10. 2991/ ijcis.d.191016.002), by Yan Xiao and Deng-Feng Li, develops a solution to n-person bargaining problems in which external cooperation among coalitions is permitted.This type of problem is referred to as bargaining games with level structure, which allow more cooperative types than those with coalition structure.
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.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".