1 / 11

Monte Carlo Simulation Managing uncertainty in complex environments.

Monte Carlo Simulation Managing uncertainty in complex environments. Module 8. EXTERNAL INPUTS. MODELS REFRESHER. OUTPUTS. MODEL. DECISION INPUTS. Models turn inputs into outputs. EXTERNAL INPUTS. MANAGING UNCERTAINTY IN MODELS. OUTPUTS. MODEL. DECISION INPUTS.

curran-peters
Télécharger la présentation

Monte Carlo Simulation Managing uncertainty in complex environments.

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Monte Carlo SimulationManaging uncertainty in complex environments. Module 8

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

  3. EXTERNAL INPUTS MANAGING UNCERTAINTY IN MODELS 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. ID INPUTS IDENTIFYING UNCERTAIN INPUTS Use sensitivity analysis to identify inputs in which uncertainty has the greatest effect.

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

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

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

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

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

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

More Related