sensitivity-analysis

sensitivity-analysis

Purpose

Quantify which parameters, assumptions, interactions, or uncertainties dominate a conclusion.

Input contract

mode_contracts:
  Morris: &sensitivity_input
    required: [model_or_reasoning_chain, uncertain_parameters, conclusion_metric]
    optional: [input_distributions, perturbation_bounds, decision_options]
    constraints: [ranges_and_dependencies_must_be_declared]
  Sobol: *sensitivity_input
  perturbation: *sensitivity_input
  Monte-Carlo: *sensitivity_input

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

1. You MUST load skill define-analysis-dimensions to define the analysis dimensions.
2. Apply the selected mode's perturbation, decomposition, or propagation operation.
3. You MUST load skill assess-sensitivity to estimate the declared sensitivity effects.
4. You MUST load skill identify-load-bearing-factors to identify load-bearing factors.
5. You MUST load skill quantify-information-value to quantify information value for unresolved drivers.

If the dominant sensitivity is caused by an unstable problem frame rather than an input value, consider problem-reframing as the next tactic. Deviation: local-perturbation, Morris, Sobol, Monte-Carlo, and decision-value modes select subsets, but mode and omitted analyses must be recorded.

Mode branches

• Morris: screen many uncertain inputs with elementary effects to identify influential factors and interactions before expensive global analysis. You MUST load skill apply-perturbation to generate elementary-effect trajectories. You MUST load skill decompose-global-sensitivity to separate influential factors and interactions.
• Sobol: decompose output variance into first-order and total-order contributions when the model can support global sampling. You MUST load skill decompose-global-sensitivity to compute the variance decomposition. You MUST load skill propagate-uncertainty to propagate the declared input distributions.
• perturbation: vary declared inputs around a baseline to expose local directional sensitivity and threshold crossings. You MUST load skill apply-perturbation to execute the bounded variations.
• Monte-Carlo: propagate input distributions through repeated draws to quantify outcome uncertainty rather than relying on a point estimate. You MUST load skill propagate-uncertainty to run and summarize the repeated draws.

Output contract

mode_contracts:
  Morris: &screening_sensitivity_output
    produces: [sensitivity_profile, interaction_effects]
    delta_fields: [findings, evidence_updates, uncertainties, decisions, recommended_jumps]
  Sobol:
    produces: [sensitivity_profile, interaction_effects, uncertainty_contributions]
    delta_fields: [findings, evidence_updates, uncertainties, decisions, recommended_jumps]
  perturbation: *screening_sensitivity_output
  Monte-Carlo:
    produces: [sensitivity_profile, uncertainty_contributions, information_value_ranking]
    delta_fields: [findings, evidence_updates, uncertainties, decisions, recommended_jumps]

Thresholds and quality gates

• B: dominant drivers, interactions, ranges, and uncertainty contributors are separated; local effects are not presented as global effects.

Failure and counterexamples

Reject rankings based on arbitrary ranges, invalid model runs, or point estimates that hide distributional uncertainty.

Provenance map

• sensitivity-analysis: intermediate (exact pool names are package-prefixed variants).
• parameter-screening, variance-decomposition, assumption-criticality, uncertainty-propagation, decision-sensitivity, screening-then-decomposition, uncertainty-cascade: resolved.

Preserved source criteria ledger

• Preserve perturbation, Morris/Sobol, uncertainty cascade, assumption criticality, and information-value modes.

Context checkpoint / Delta notes

Append dimensions, perturbation results, driver rankings, uncertainty updates, and next evidence decisions.