Trend Impact Analysis Event Strength Adjuster
Adjust a base trend-impact score by a nonnegative multiplier and the sum of selected caller-supplied adjuster weights.
Description
A trend-impact scenario combines a base value with selected positive adjusters.
A trend-impact scenario combines a base value with selected positive adjusters. The caller determines which adjusters are present.1
The returned values include Adjusted impact, Adjustment factor, Total adjuster weight. The How section below explains the calculation, input rules and interpretation.
When to use Trend Impact Analysis Event Strength Adjuster
Use Trend Impact Analysis Event Strength Adjuster when you need to evaluate the supplied records under the calculation and evidence rules below. No empirical impact calibration or interactions among adjusters are estimated.
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 Trend Impact Analysis Event Strength Adjuster 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 adjusterWeights, baseImpact, presentAdjusterFlags, trendMultiplier using the field table and calculation rules, then run the tool.
- Read adjustedImpact, adjustmentFactor, totalAdjusterWeight. 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 |
|---|---|---|---|
| adjusterWeights | Adjuster weights | list of number | Required |
| baseImpact | Base impact | number; minimum: 0 | Required |
| presentAdjusterFlags | Present adjuster flags | list of number | Required |
| trendMultiplier | Trend multiplier | number; minimum: 0 | Required |
Require nonnegative finite base/multiplier and aligned weight/binary-flag lists, possibly empty. Sum selected nonnegative finite weights with overflow checks and return factor, total and finite adjusted impact.
weight=sum(selectedWeights); factor=multiplier×(1+weight); adjusted=base×factor Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| adjustedImpact | Adjusted impact | number; minimum: 0 |
| adjustmentFactor | Adjustment factor | number; minimum: 0 |
| totalAdjusterWeight | Total adjuster weight | number; minimum: 0 |
{
"input": {
"adjusterWeights": [
0.2,
0.3,
0.1
],
"baseImpact": 100,
"presentAdjusterFlags": [
1,
0,
1
],
"trendMultiplier": 2
},
"output": {
"adjustedImpact": 260,
"adjustmentFactor": 2.6,
"totalAdjusterWeight": 0.30000000000000004
}
}Limitations and assumptions
No empirical impact calibration or interactions among adjusters are estimated.
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. 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 Wright Law Learning Curve Cost Doubling Lookup: Learning curves express a hypothetical unit-cost reduction per doubling of cumulative production. The chosen rate is an assumption, not an observed future trend. For a complementary task, use Extrapolation Horizon Limit Warning Checker: An extrapolation can assume uncertainty grows at a constant annual compound rate. Explicit thresholds provide an illustrative warning screen.
References
Similar or alternative tools
- Wright Law Learning Curve Cost Doubling Lookup
Learning curves express a hypothetical unit-cost reduction per doubling of cumulative production. The chosen rate is an assumption, not an observed future trend.
- Extrapolation Horizon Limit Warning Checker
An extrapolation can assume uncertainty grows at a constant annual compound rate. Explicit thresholds provide an illustrative warning screen.