1. Monte Carlo Simulation Managing uncertainty in complex environments.
Module 8

2. Models turn inputs into outputs.
MODELS REFRESHER
EXTERNAL
INPUTS
OUTPUTS
MODEL
DECISION
INPUTS
Models turn inputs into outputs.

3. MANAGING UNCERTAINTY IN MODELS
EXTERNAL
INPUTS
OUTPUTS
MODEL
DECISION
INPUTS
Uncertainty in inputs translates into uncertainty in outputs.

4. THE STEPS OF MODELING UNCERTAINTY
IDENTIFY UNCERTAIN INPUTS
MODEL UNCERTAIN INPUTS
RUN SIMULATION
Monte Carlo simulation allows you to determine probabilities of possible outcomes by running thousands of automated scenario analyses.

5. IDENTIFYING UNCERTAIN INPUTS
ID INPUTS
Use sensitivity analysis to identify inputs in which uncertainty has the greatest effect.

6. MODELING UNCERTAIN INPUTS
MODEL INPUTS
Use probability distributions to model possible values of inputs. Most variables fit into one of four common distributions.

7. Calculate mean, standard deviation. For “give or take” variables.
NORMAL DISTRIBUTION
MODEL INPUTS
Calculate mean, standard deviation.
For “give or take” variables.

8. TRIANGLE DISTRIBUTION
MODEL INPUTS
For quick estimates or situations with little data.
Estimate Worst Case, Expected, and Best Case.

9. Equal probability for all values. For “anywhere between” situations.
UNIFORM DISTRIBUTION
MODEL INPUTS
Equal probability for all values.
For “anywhere between” situations.

10. DISCRETE DISTRIBUTION
MODEL INPUTS
For variables that fit no discernable trend.
Read probabilities directly from histogram.

11. Use software to simulate thousands of scenarios.
RUN SIMULATION
RUN SIMULATION
Use software to simulate thousands of scenarios.