Global Sensitivity Analysis¶
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., et al. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons.
Cited by¶
12 citations across 12 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Ceteris Paribus
- In operations research it is one-at-a-time sensitivity analysis.
This sourceDefines one-at-a-time sensitivity analysis (varying one input with the others clamped) and contrasts it with global methods.
- In operations research it is one-at-a-time sensitivity analysis.
- Sensitivity Analysis (in Operations Research)
- The optimization output itself is often taken as authoritative without recognition of its parameter-conditionality, leading to over-confident decisions, misdirected information-gathering, and missed robustness concerns.
This sourceStandard primer covering screening, variance-based, and Monte Carlo methods for sensitivity analysis.
- The optimization output itself is often taken as authoritative without recognition of its parameter-conditionality, leading to over-confident decisions, misdirected information-gathering, and missed robustness concerns.
Mechanisms¶
- Feasibility Sensitivity Probe
- Its strength is that it distinguishes a robust choice from a lucky one: it shows which parts of the decision are settled across the plausible range and which rest on a number no one has actually pinned down — the core value of sensitivity analysis in any decision model.
This sourceUses global sensitivity analysis to map outcomes across uncertain input ranges and identify which unpinned factors materially control model results.
- Its strength is that it distinguishes a robust choice from a lucky one: it shows which parts of the decision are settled across the plausible range and which rest on a number no one has actually pinned down — the core value of sensitivity analysis in any decision model.
- Inflation & Exchange-Rate Scenario Table
- Its strength is honesty about uncertainty: it replaces a single, over-confident figure with a defensible range and names the assumption the decision actually hinges on, which is exactly what basis uncertainty demands.
This sourceExplains how uncertainty analysis yields output ranges across plausible assumptions and sensitivity analysis identifies the inputs or assumptions that drive the result.
- Its strength is honesty about uncertainty: it replaces a single, over-confident figure with a defensible range and names the assumption the decision actually hinges on, which is exactly what basis uncertainty demands.
- Kernel Response Sensitivity Sweep
- Its failure mode is that a one-factor-at-a-time sweep can pronounce a kernel "robust" while missing fragilities that only emerge when parameters move together,
This sourceShows that one-at-a-time analysis can miss interactions and that sensitivity findings depend on the chosen input ranges and output of interest.
- Its failure mode is that a one-factor-at-a-time sweep can pronounce a kernel "robust" while missing fragilities that only emerge when parameters move together,
- One-way Sensitivity Analysis
- Its failure mode is structural — because it varies inputs one at a time, it is systematically blind to interaction and correlation, so when two inputs move together or amplify each other, the one-way ranking can put the wrong driver first.
This sourceShows that varying inputs one at a time fails to explore interaction effects and dependent joint variation among inputs.
- Its failure mode is structural — because it varies inputs one at a time, it is systematically blind to interaction and correlation, so when two inputs move together or amplify each other, the one-way ranking can put the wrong driver first.
- Scenario Sensitivity Sweep
- Its strength is that it separates a robust incompatibility — one worth the pain of resolving — from a fragile one that a single soft assumption is manufacturing, and it names exactly which assumptions the resolution must be hardened against.
This sourceUses sensitivity analysis to distinguish conclusions robust across input assumptions from conclusions driven by a small or uncertain assumption set.
- Its strength is that it separates a robust incompatibility — one worth the pain of resolving — from a fragile one that a single soft assumption is manufacturing, and it names exactly which assumptions the resolution must be hardened against.
- Sensitivity Analysis
- The classic error is a one-at-a-time sweep that nudges each parameter around a single base case and declares the model robust — while missing interactions where two parameters are dangerous only in combination, exactly the regime a global, variance-based analysis is built to catch
This sourceShows that local one-factor perturbations around a base point can miss interaction effects that global variance-based sensitivity methods are designed to identify.
- The classic error is a one-at-a-time sweep that nudges each parameter around a single base case and declares the model robust — while missing interactions where two parameters are dangerous only in combination, exactly the regime a global, variance-based analysis is built to catch
- Sensitivity Analysis Workshop
- Its strength is precision about leverage: it tells you, with numbers, exactly which few quantitative premises deserve real evidence and which are safe to leave roughly guessed — and its paired-variation step catches the compounding interactions a naive check misses.
This sourceUses quantitative factor prioritization and fixing to identify influential and safely fixable premises, while global designs account for interactions missed by one-factor-at-a-time checks.
- Its strength is precision about leverage: it tells you, with numbers, exactly which few quantitative premises deserve real evidence and which are safe to leave roughly guessed — and its paired-variation step catches the compounding interactions a naive check misses.
- Sensitivity Check
- One-at-a-time vs. joint — varying variables singly is cheap and readable; varying them together catches interactions but explodes the number of runs.
This sourceCompares sensitivity methods by their ability to capture interactions and by the number of model runs they require.
- One-at-a-time vs. joint — varying variables singly is cheap and readable; varying them together catches interactions but explodes the number of runs.
- Sensitivity-to-Mapping-Change Review
- Its strength is exposing knife-edge preimages before they are trusted: a one-at-a-time sensitivity analysis
This sourceProvides one-at-a-time parameter-variation methods as part of sensitivity analysis.
- Its strength is exposing knife-edge preimages before they are trusted: a one-at-a-time sensitivity analysis
- Weight-Sweep Sensitivity Table
- Recompute and log outcomes. One-at-a-time moves show single-weight fragility; joint moves catch interactions the one-at-a-time view misses.
This sourceExplains how one-at-a-time analysis isolates individual-input effects and how joint or total-effect analysis reveals interactions it can miss.
- Recompute and log outcomes. One-at-a-time moves show single-weight fragility; joint moves catch interactions the one-at-a-time view misses.
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