Reynal-Querol Polarization Index Calculator
Reynal–Querolpolarization4Σp²(1−p), actual complete exclusive aggregate category shares, no infer ethnicity/religion from records or individual/civil conflict risk.
Description
The Reynal–Querol index summarizes polarization in an aggregate share distribution.
The Reynal–Querol index summarizes polarization in an aggregate share distribution.1
It differs mathematically from fractionalization and does not predict conflict. The calculation and input requirements are explained below.
When to use Reynal-Querol Polarization Index Calculator
Enter nonempty aggregate shares from 0 to 1 forming a complete partition within a sum tolerance of 1e-9.
How to use Reynal-Querol Polarization Index Calculator and how it works
- Prepare the measurements or independently established model inputs described below, with matching units and observation scope.
- Enter the required fields and run Reynal-Querol Polarization Index Calculator.
- Read the result fields using the formula, output units, signs and ordering explained below.
- Correct any rejected inputs and rerun; interpret the result within the stated model and its assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| actualQualifiedAggregateShares | Actual Qualified Aggregate Shares. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
Enter nonempty aggregate shares from 0 to 1 forming a complete partition within a sum tolerance of 1e-9. Return four times the sum of p squared times (1-p). This polarization metric is not a forecast of conflict.
polarization=4*sum(p^2*(1-p)) Apply the input ordering, units and branch conditions stated above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the calculated or selected fields below. | object |
| result.value | Numeric result under the formula and output units explained above. | number |
{
"input": {
"actualQualifiedAggregateShares": [
0.5,
0.5
]
},
"output": {
"result": {
"value": 1
}
}
}Limitations and assumptions
The calculation uses the supplied observations and model inputs. It does not collect measurements, select a reference population or coefficient, or establish that the model applies to the actual case. The stated branch conditions and output units remain part of the result.
All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.
Alternative or Complementary methods
Use Religious Fractionalization Index Calculator for a complementary operation: Religious fractionalization uses shares of mutually exclusive aggregate categories.
Use Linguistic Fractionalization Index Calculator for a complementary operation: Linguistic fractionalization describes an aggregate partition of language categories.
References
Similar or alternative tools
- Religious Fractionalization Index Calculator
Religious fractionalization uses shares of mutually exclusive aggregate categories.
- Linguistic Fractionalization Index Calculator
Linguistic fractionalization describes an aggregate partition of language categories.