Predictive Global Sensitivity Analysis: Foundational Concepts, Tools, and Applications
Bibliographic record
Abstract
Modern managers must sift through huge data overload to make quick decisions in dynamic environments. Predictive Global Sensitivity Analysis (PGSA) represents a statistical approach to simplifying a complicated mathematical optimization model into a straightforward set of predictive equations by summarizing numerous complexities into a few highly explanatory variables. Managers can use such equations to make swift decisions with colleagues or customers in real time, or the equations can be used as a monitoring tool to verify current decisions as external conditions change. In this monograph, the authors review the published applications of PGSA that have emerged over the past two decades. Differences in the published works illustrate the underlying flexible nature of the method. Modelers get to practice significant judgement all throughout the process, from application selection through model validation. Section 3 provides a step-by-step tutorial of the full PGSA process. The authors describe how each step has been addressed in the literature to date, and they illustrate each step in detail using two new applications of classic problems in operations research. Section 4 introduces a brand-new application of PGSA that predicts which among three centralized purchasing scenarios that a newly introduced product purchased at a local site should adopt.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".