Fuzzy Real Options for Risky Project Evaluation Using Least Squares Monte-Carlo Simulation
Bibliographic record
Abstract
A numerical technique for evaluating risky projects with fuzzy real options is developed. Fuzzy real options are based on hybrid variables that represent the market risk of a project, which is derived from data, and the private risk, which is usually estimated by experts. These hybrid variables can be evaluated using an extension of Least Squares Monte-Carlo simulation that produces numerical evaluations of fuzzy real options based on the generation and backward induction of sample paths. A major advantage of this methodology is its ability to determine values regardless of whether or not an analytic solution exists. To illustrate, two fuzzy real options models are evaluated using the proposed algorithm: one, on brownfields, for comparison with analytic outputs for fuzzy real options; the other, on oil development, for comparison to the results of the Integrated Valuation Procedure (IVP), another algorithm to assess private risk. The results indicate that the generalized Least Squares Monte-Carlo simulation produces similar results to the analytic valuation of fuzzy real options, when this is possible. Moreover, the use of fuzzy real options can overcome the private risk problem without invoking IVP, which is preferable because expert linguistic estimates are easier to use in a fuzzy environment.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".