Decision tree, Monte Carlo analysis
8 important questions on Decision tree, Monte Carlo analysis
What are to methods to rationalise choice3 in an uncertain world?
- Decision Trees
- Monte Carlo simulation
- Analyze options and consequences
What is taken into account when performing a Monte Carlo simulation?
Variability at-risk level, uncertainty about claim level (Min ; ML ; Max)
What are important factors for a Monte Carlo simulation?
- Input parameters have a range of values often expressed in distributions
- For each model run, a random value is taken from this range
- Model outcome calculated for this value
- Repeated many times ( iterations)
- The outcome is also a range of values ( distribution)
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How does the distribution look like for an input parameter?
- Range values: histogram or distribution (fitted)
- Examples:
- Normal
- Lognormal
- Triangle
What is the influence of risk attitude?
What is the risk attitude: Risk neutral?
What is the risk attitude: Risk Seeker?
So what is the function of Decision trees & Monte Carlo simulation?
- Help to structure the decision problem
- Advise you of the preferred decision
However:
- The decision is up to the decision-maker
- And depending on his/her risk attitude
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