Tier1and2Impacts

# Risk Impact Aggregation

This repository contains a simple R script for calculating the aggregation of risk impacts, taking into account both primary (tier 1) and secondary (tier 2) impacts. It uses a Monte Carlo simulation approach to estimate the combined impact of these risks.

## Parameters

The following parameters are set for the risk assessment:
- `Simulations`: Number of random simulations to run for estimation.
- `Loss1`: Estimated loss of the tier 1 impact (data regeneration costs).
- `StDev1`: Estimated standard deviation of the tier 1 impact.
- `Prob1`: Probability of occurrence of the tier 1 impact.
- `Loss2`: Estimated loss of the tier 2 impact (compensation costs).
- `StDev2`: Estimated standard deviation of the tier 2 impact.
- `Prob2`: Probability of occurrence of the tier 2 impact.

## Usage

The script uses the parameters to perform Monte Carlo simulations to estimate the combined impact of tier 1 and tier 2 risks. It generates random samples for both impacts and aggregates them. The results are visualized in a histogram to provide insights into the potential risk exposure.

Feel free to customize the parameters and use this script as a tool for risk assessment in your projects.

## How to Run

1. Clone or download this repository.
2. Open the R script in your preferred R environment.
3. Adjust the parameters as needed.
4. Run the script to perform the risk impact aggregation and visualize the results.


# Set the parameters for risk assessment
Simulations <- 10000    # Number of random simulations
Loss1 <- 20000          # Estimated loss of the tier 1 impact (data regeneration costs)
StDev1 <- 0.2           # Estimated standard deviation of the tier 1 impact
Prob1 <- 0.2            # Probability of occurrence of the tier 1 impact
Loss2 <- 50000          # Estimated loss of the tier 2 impact (compensation costs)
StDev2 <- 0.2           # Estimated standard deviation of the tier 2 impact
Prob2 <- 0.5            # Probability of occurrence of the tier 2 impact

# Calculate impacts
x1 <- rlnorm(Simulations, log(Loss1), StDev1) * rbinom(Simulations, 1, Prob1)
x2 <- rlnorm(Simulations, log(Loss2), StDev2) * rbinom(Simulations, 1, Prob2)
hist(x1 + x2, main="Risk Impact Aggregation", xlab="Combined Impact")