Monte Carlo Simulation Managing uncertainty in complex environments. Module 8

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

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

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.

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

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

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

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

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

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

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