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Sensitivity Analysis II

Sensitivity Analysis II. Presented by Ignacio J. Martinez and Peter Otto. PAD824. Today‘s Presentation. Clarification Traditional Process for Conducting Sensitivity Analysis Parameters in Vensim Doing in Class Exercises . Clarification.

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Sensitivity Analysis II

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  1. Sensitivity Analysis II Presented by Ignacio J. Martinez and Peter Otto PAD824

  2. Today‘s Presentation • Clarification • Traditional Process for Conducting Sensitivity Analysis • Parameters in Vensim • Doing in Class Exercises

  3. Clarification • Models often include exogenous parameters or relations among variables about which our knowledge is inadequate, thus we make guesses • SA is the process of determining which of these guesses really matter, that is, we try to determine whether a slight different guess would make a significant difference in the behavior of the model

  4. Types • Numerical Sensitivity.- Changes the numbers of the output of the simulation but not the behavioral pattern. • Behavioral Sensitivity.- Changes the numbers and the behavioral pattern of the output of the simulation. • Structural Sensitivity.- Changes the output when changing the structure.

  5. Process for Conducting a Sensitivity Analysis • List the exogenous parameters and relations about which we are making guesses • Determine the possible range for each parameter • Pick the parameter that seems most likely to be important, while holding everything else constant, run the model under a full range of different values for that parameter

  6. Outcome • If model behavior changes significantly, the model is sensitive to the selected parameter, and we must reformulate the model to eliminate the parameter, learn what the real value for the parameter is, or lose confidence in the model.

  7. Setting up a Sensitivity Analysis in Vensim®

  8. Settings • Default: Multivariate, causes all selected constants to be changed together • Univariate, if selected, causes the first constant to be changed and then the next, with the first constant set back to its normal simulation value (useful option for doing a series of Vector searches across parameters) Note: If you enter only one parameter, Univariate and Multivariate searches are the same

  9. Settings • Latin Hypercube (change together exhausting axes ranges), if selected will cause a Latin Hypercube search to occur. A Latin Hypercube search is simply a mechanism to ensure that the full range of each parameter being varied is explored in the number of simulations specified. This is desirable for big models where each simulation takes a long time.

  10. Distributions • Default: Random Uniform (min, max), draws from a uniform random distribution • Random Normal (min, max, mean, standard dev), draws a number from a normal distribution with the specified mean and standard deviation • Vector (min, max, increment), generates a sequence of numbers from min to max by increment. This sequence is not random, but uniformly increasing

  11. Running Sensitivity in Vensim®

  12. Changing Settings and Results

  13. Multivariate with Random Uniform Distribution Multivariate with Random Normal Distribution Sensitivity Run: BCN = 0.01 – 0.2 (value in the model 0.07) Sensitivity Run: BCN = 0.01 – 0.2, Mean = 0.1, Standard Deviation = 0.01

  14. Latin Hypercube with Random Uniform Distribution Latin Hypercube with Random Normal Distribution Sensitivity Run: BCN = 0.01 – 0.2 (value in the model 0.07) Sensitivity Run: BCN = 0.01 – 0.2, Mean = 0.1, Standard Deviation = 0.01

  15. Univariate with Vector Distribution Multivariate with Random Normal Distribution Sensitivity Run: BCN = 0.01 – 0.2, Increment = 0.01 (value in the model 0.07) Sensitivity Run: BCN = 0.01 – 0.2, Mean = 0.1, Standard Deviation = 0.01

  16. Sensitivity to a Table Function (A kind of Structural Sensitivity)

  17. Business labor force multiplier ("<BLFM A>"(Labor to job ratio)*Weight on A) +("<BLFM B>"(Labor to job ratio)*(1-Weight on A)) =

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