Ventana software support forum

Hi,

I am calibrating my model through the optimization tool of Vensim. My question is related with the optimization parameters choice. I selected 5 constant variables to be calibatred which range of values is indifferent as long as the sum of these variables equals to zero. How can I define these restrictions in the optmization tool?

Thanks!
G

Three options:

- Define the 5th parameter to be 1-SUM(other 4) - this will only work in some cases; you could wind up with a negative value.

- Optimize a set of weights that get normalized to determine the shares:

x : (x1-x5) ~ dimension for your parameter vector
weight[x] = 1 ~ optimization parameters, with range 0 to 1
fraction[x] = weight[x]/SUM(weight[x!]) ~ the fraction that gets used in the model

- Add a penalty function to enforce the constraint.

Hi,

I defined a model variable that computes the sum of the 5 constant variables:

Total utilities=sum(utilities(i!)
where,
Utilities(i) represent the constant variables that I want to calibrate (one for each vehicle type: BEV, PHEV, HEV, Gas and Diesel)

How the penalty function works? I never heard about it before

G

The basic idea is to add a term to the payoff that penalizes the difference between 1 and SUM(utilities[i!]).

I'd try the normalization approach first.

I am following your suggestion and trying the normalization approach.
If I understood correctly, in the equations you presented above, you are saying to multiply the fraction[x] with the utility[x]. If I do that I am defining that, in the base run, the utility computed is 1/5 if its initial value?

G

I just realized that I misread your original - I thought you wanted to enforce sum=1, not sum=0.

So:

utility[x] = 0 ~ opt parameter, with some reasonable bounds like -10 to 10
norm utility[x] = utility[x] - SUM(utility[x!])/ELMCOUNT(x) ~ this enforces the constraint

I am not understanding how the norm utility[x] equation enforces the sum of utilities to be zero. Can you explain the reasoning behind it?

Thanks

Try it!

This is just the same trick as subtracting the sample mean from a set of data points. The mean of the resulting adjusted data is 0.

I already tried it but the sum of the calibrated utilities was not equal to 0...

Sum is 0 within numerical precision tolerances.