An advanced tool for dynamic risk modeling and analysis in projects management
Bibliographic record
Abstract
Risk is inherently present in all projects. Quite often, many projects fail to achieve their time, quality, and budget goals. Despite its high relevance to the success of megaprojects, risk management remains one of the least developed research issues. Therefore, advanced risk assessment is essential in minimizing losses and enhancing profitability. This paper proposes an advanced decision support tool using Fuzzy Cognitive Maps (FCMs) for dynamic risk assessment in project management. The proposed tool is able to predict the impact of each risk on the other risks or the outcomes of projects by considering uncertainties and complex interdependencies among risk factors. This tool could help project managers to manage the risks in a more effective and precise way and offer better risk mitigation solutions. The proposed tool could be undertaken by all organizations with the highest level of risk management maturity in the largest and most complex projects. In addition, it can be applied as an advanced decision support tool in variety of problems such as prioritization, failure analysis, etc. An academic numerical example related to outsourcing illustrates the applicability and simplicity of the proposed method.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".