A Review of the Applicability of Classical and Extension of Fuzzy LogicApproaches to Project Decision-Making using Real Options
Bibliographic record
Abstract
Introduction: In this study, a review of fuzzy implementation to Real Options Approach (ROA) theory where the applicability of classical and extended theories of “fuzziness” studied. Background: ROA allows taking into account the value of some sources of managerial flexibility and therefore assessing a more accurately project value. The positive value of flexibility results from limiting the impacts of adverse events while taking advantage of positive ones. One of the main lessons is that uncertainty adds value in the presence of flexibility. Ambiguous parameters that have a significant effect on the project value are usually represented as fuzzy sets using Zadeh's classical theory of Fuzzy logic (also termed "type-1"). However, there have been so many derivatives, and expansions of the fuzzy set theories developed by different researchers. Dealing with uncertainty can be manifested in the different mechanism of fuzziness. Objective: The objective of this review is to identify the research gap as well as provide an elementary guide to the applicability of different varieties of classical and extended applicability of fuzziness to ROA when evaluating project investment. Methods: After a generic review of the progress of ROA theory and fuzzy approaches by researchers This paper reviews the applicability of ROA to fuzzy sets (classical and extended) implementation to decision-making for large projects where project timing and uncertainty are key parameters affecting the project value Results: After reviewing the applicability of each of the classical and extended theories of fuzzy logic to ROA, a tabular format shows the result of this study summarizing the scenario, showing the applicability of different techniques. Conclusion: Most of the reviewed techniques of fuzzy implementation to ROA approach, still based on the classical theory of fuzzy logic. Implementation of more extended techniques has a potential of enhancing the outcome of such research.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".