Hi everyone,
I have some problems with calibration optimization;
How do I know for which parameters I should define payoffs? Can I set it for all the parameters I have imported data?
Can I use 1/standard deviation for weights of the selected variables/parameters?
How do I further know which model parameters I use in the Optimization control set up?
Thank you in advance!
Keira
calibration optimization / weights
Re: calibration optimization / weights
Your payoff should compare the model to as much data as you have available.
I find it easier to change the distribution assumption for payoff items from Normal (using 1/sd weight) to Gaussian (which uses the sd directly - much clearer).
Parameter choices should be things that are (a) important, and (b) uncertain.
Before you start running automatic calibration, it's a good idea to set up a control panel view with custom graphs that show all of your model-data comparisons. Place all of your parameters of interest on the diagram as well. Then you can experiment with calibration manually in synthesim. You'll learn a lot about the possible issues. Later you can turn the optimizer loose on the problem.
I find it easier to change the distribution assumption for payoff items from Normal (using 1/sd weight) to Gaussian (which uses the sd directly - much clearer).
Parameter choices should be things that are (a) important, and (b) uncertain.
Before you start running automatic calibration, it's a good idea to set up a control panel view with custom graphs that show all of your model-data comparisons. Place all of your parameters of interest on the diagram as well. Then you can experiment with calibration manually in synthesim. You'll learn a lot about the possible issues. Later you can turn the optimizer loose on the problem.
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Blog: http://blog.metasd.com
Model library: http://models.metasd.com
Bookmarks: http://delicious.com/tomfid/SystemDynamics
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