Wind Tunnelling Policy Test Scenarios Method
Cross every caller-supplied policy mechanism with every stressor to generate wind-tunnelling tests for a policy goal.
Description
Policy wind tunnelling tests mechanisms under several stressors.
Policy wind tunnelling tests mechanisms under several stressors. A generated question is a test plan rather than a result.1
The returned values include Policy stress tests, Test count, Test sequence. The How section below explains the calculation, input rules and interpretation.
When to use Wind Tunnelling Policy Test Scenarios Method
Use Wind Tunnelling Policy Test Scenarios Method when you need to evaluate the supplied records under the calculation and evidence rules below. No policy effectiveness or stress probability is assessed.
Define the question, evidence and scenario assumptions first. Supply the chosen score scales, time horizons or reference selection explicitly. Interpret generated text as a discussion template and screen labels as the stated rules.
How to use Wind Tunnelling Policy Test Scenarios Method and understand its results
- Define the question, evidence and scenario assumptions first. Supply the chosen score scales, time horizons or reference selection explicitly. Interpret generated text as a discussion template and screen labels as the stated rules.
- Enter policyGoal, policyMechanisms, stressors using the field table and calculation rules, then run the tool.
- Read testCases, testCount, testSequence. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| policyGoal | Policy goal | string | Required |
| policyMechanisms | Policy mechanisms | list of string | Required |
| stressors | Stressors | list of string | Required |
Trim a nonblank policy goal and nonempty lists of nonblank mechanisms and stressors. Generate every pair in stressor-major then mechanism order, preserve duplicates, and return prompts, count and one-based sequence.
tests=stressors×mechanisms Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| testCases | Policy stress tests | list of string |
| testCount | Test count | integer; minimum: 1 |
| testSequence | Test sequence | list of number |
{
"input": {
"policyGoal": "reliable low-carbon power",
"policyMechanisms": [
"Performance standard",
"Capacity support"
],
"stressors": [
"Demand surge",
"Supply disruption"
]
},
"output": {
"testCases": [
"Test Performance standard against Demand surge: can it still advance reliable low-carbon power?",
"Test Capacity support against Demand surge: can it still advance reliable low-carbon power?",
"Test Performance standard against Supply disruption: can it still advance reliable low-carbon power?",
"Test Capacity support against Supply disruption: can it still advance reliable low-carbon power?"
],
"testCount": 4,
"testSequence": [
1,
2,
3,
4
]
}
}Limitations and assumptions
No policy effectiveness or stress probability is assessed.
Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Core numeric and string lists are limited to 100,000 entries; text entries are subject to the general large-text limit. Follow any smaller calculation limits above. Extremely large text inputs are rejected. Missing fields without a declared default, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Red Team Devils Advocate Assumption Attack Protocol: An assumption attack protocol prompts reviewers to challenge a scenario in several ways. Pair generation does not itself falsify an assumption. For a complementary task, use Strategy Robustness Across Quadrants Score: Robustness across scenarios requires every supplied quadrant to satisfy chosen payoff and risk criteria. Good averages can conceal a weak quadrant.
References
Similar or alternative tools
- Red Team Devils Advocate Assumption Attack Protocol
An assumption attack protocol prompts reviewers to challenge a scenario in several ways. Pair generation does not itself falsify an assumption.
- Strategy Robustness Across Quadrants Score
Robustness across scenarios requires every supplied quadrant to satisfy chosen payoff and risk criteria. Good averages can conceal a weak quadrant.