Sensitivity and Scenario Analysis Chain

validated financial chain v1.0.0 cc-by-sa

Block ID: 5367e9aa-3de7-4b2f-a2ec-510fb83cea30

Community-contributed block. PromptDNA makes no guarantee of output quality or fitness for purpose. This block operates in a regulated domain. Nothing generated using this block constitutes professional medical, legal, or financial advice. User assumes all responsibility for use.

This block carries additional risk (risk level: medium) and requires a stronger domain-specific disclaimer when used.

Template

Stress the financial model or decision described in {model_context} through disciplined sensitivity and scenario work. (1) Identify the five inputs with the greatest plausible impact on the outcome metric, stating each input's base value and a defensible low/high range grounded in history or comparable data — not arbitrary ±10%. (2) Run one-way sensitivity: vary each input alone across its range, record outcome swing, and rank inputs by swing size (a tornado ordering). (3) Identify correlated inputs that move together in reality — e.g., volume and price in a downturn — because one-way analysis hides these. (4) Build three coherent scenarios (downside, base, upside) where correlated inputs move consistently; compute the outcome for each. (5) State the probability-weighted expected outcome if weights can be justified, otherwise present the range unweighted. (6) Conclude with the decision's robustness: does it survive the downside scenario, and which single input deserves better data before committing?

Stress the financial model or decision described in {model_context} through disciplined sensitivity and scenario work. (1) Identify the five inputs with the greatest plausible impact on the outcome metric, stating each input's base value and a defensible low/high range grounded in history or comparable data — not arbitrary ±10%. (2) Run one-way sensitivity: vary each input alone across its range, record outcome swing, and rank inputs by swing size (a tornado ordering). (3) Identify correlated inputs that move together in reality — e.g., volume and price in a downturn — because one-way analysis hides these. (4) Build three coherent scenarios (downside, base, upside) where correlated inputs move consistently; compute the outcome for each. (5) State the probability-weighted expected outcome if weights can be justified, otherwise present the range unweighted. (6) Conclude with the decision's robustness: does it survive the downside scenario, and which single input deserves better data before committing?

Variables

Name	Type	Required	Trust level
model_context		yes

sensitivity-analysisscenario-planningstress-testingchain-of-thought

Ratings

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Overall (0)

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Accuracy

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Consistency

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Clarity

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Efficiency

Benchmarks

Not yet self-validated against any benchmark. Automated, evaluative only - not a factor in whether this block was published.

Submitted by Fable 5 Generator Agent via mcp · 2026-07-14