Terror Recruitment Index: An extended version moves beyond a simple linear model and integrates:
- Weighted Multi-Domain Factors
- Nonlinear Interactions
- Risk Elasticities
- Bayesian Probabilistic Scoring (optional)
TRI 2.0 (Extended Composite Risk Index Model)
TRI= 1/Z ∑i=1n βi⋅ fi(Xi)
Where:
- TRI∈[0,1]: Terror Recruitment Index (risk score)
- βi: Empirical or expert-assigned weight for indicator iii
- fi(Xi): Normalized transformation function of raw indicator Xi
- Z=∑βi: Normalization factor to keep TRI in [0, 1]
Included Domains and Variables
| Domain | Variable | Symbol | Transform Function fif_i |
|---|---|---|---|
| Environment | Water Scarcity Index | WS | WS0.5(nonlinear) |
| Climate | Drought Severity Index | DS | log(1+DS) |
| Economy | Youth Unemployment Rate | UR | UR |
| Society | Education Deprivation Index (inverted) | 1−EI | square root (1−EI) |
| Governance | State Fragility Index | FI | FI1.2 |
| Migration | Internally Displaced People per capita | IDP | IDP/Population |
| Media | Disinformation Index | DI | log(1+DI) |
| Inequality | Gini Coefficient | G | G |
Sample equation:
TRI=1/Z [0.20 ⋅ WS0.5 + 0.15 ⋅ log(1+DS) + 0.15 ⋅ UR + 0.10 ⋅ (1 − EI)1/2 + 0.15 ⋅ FI1.2 + 0.10 ⋅ (IDP/Pop) + 0.10 ⋅ log(1+DI) + 0.05 ⋅ G]
Where Z=1.00
Optional Extension: Bayesian Risk Model
Incorporate a probabilistic layer:
P(Terrorism)=σ(α+β1⋅WS+β2⋅UR+β3⋅FI+…)
Where σ is the sigmoid function → gives output as probability of terrorism incidents based on inputs.
__END OF NOTE___
Realated Article:
Climate-Terror Risk Simulator | Creating an Economic Dashboard – PRIYADARSHI KIRTI GOURAV
