Monte Carlo Simulation Managing uncertainty in complex environments'

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Monte Carlo SimulationManaging uncertainty in
complex environments.
Module 8
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MODELS REFRESHER
EXTERNAL INPUTS
OUTPUTS
MODEL
DECISION INPUTS
Models turn inputs into outputs.
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MANAGING UNCERTAINTY IN MODELS
EXTERNAL INPUTS
OUTPUTS
MODEL
DECISION INPUTS
Uncertainty in inputs translates into uncertainty
in outputs.
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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.
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IDENTIFYING UNCERTAIN INPUTS
ID INPUTS
Use sensitivity analysis to identify inputs in
which uncertainty has the greatest effect.
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MODELING UNCERTAIN INPUTS
MODEL INPUTS
Use probability distributions to model possible
values of inputs. Most variables fit into one of
four common distributions.
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NORMAL DISTRIBUTION
MODEL INPUTS
Calculate mean, standard deviation. For give or
take variables.
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TRIANGLE DISTRIBUTION
MODEL INPUTS
For quick estimates or situations with little
data. Estimate Worst Case, Expected, and Best
Case.
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UNIFORM DISTRIBUTION
MODEL INPUTS
Equal probability for all values. For anywhere
between situations.
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DISCRETE DISTRIBUTION
MODEL INPUTS
For variables that fit no discernable trend. Read
probabilities directly from histogram.
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RUN SIMULATION
RUN SIMULATION
Use software to simulate thousands of scenarios.