Change DT to reflect ever-smaller periods until the change in the critical variable is
within measuring tolerances. Start with a DT ¼ 1 and reduce it to 0.5, 0.25, 0.125. . .
for subsequent runs—each time cutting it into half of its previous value. We may
also change the numerical technique used to solve the model equations. Euler’s
method is chosen as a default. Two other methods, Runge–Kutta-2 and Runge–
Kutta-4, are available that update state variables in different ways. We will discuss
these methods later.
Start with a simple model and keep it simple, especially at first. Whenever possible,
compare you results against measured values. Complicate your model only when your
results do not predict the available experimental data with sufficient accuracy or when
your model does not yet include all the features of the real system that you wish to
capture. For example, as the owner of a pond, you may want to extract fish for sale.
What are the fish population dynamics if you wish to extract fish at a constant rate of
3 % per year? To find the answer to this question, define an click on the stock FISH.
Click on the converter, then click onto the stock to have the converter connected to
the stock, and then drag the flow from the stock to the right. Now fish disappear from
the stock into a “cloud.” We are not explicitly modeling where they go. Figure 1.13
shows what you should have developed thus far as your STELLA model.
Next, define a new converter called EXTRACTION RATE and set is to 0.03.
Specify the outflow as:
EXTRACTION ¼ EXTRACTION RATE Ã FISH
ð1:3Þ
after making the appropriate connections with information arrows. Your model
should now look like as in Fig. 1.14.
Fig. 1.13
Fig. 1.14
20
1 Modeling Dynamic Biological Systems
within measuring tolerances. Start with a DT ¼ 1 and reduce it to 0.5, 0.25, 0.125. . .
for subsequent runs—each time cutting it into half of its previous value. We may
also change the numerical technique used to solve the model equations. Euler’s
method is chosen as a default. Two other methods, Runge–Kutta-2 and Runge–
Kutta-4, are available that update state variables in different ways. We will discuss
these methods later.
Start with a simple model and keep it simple, especially at first. Whenever possible,
compare you results against measured values. Complicate your model only when your
results do not predict the available experimental data with sufficient accuracy or when
your model does not yet include all the features of the real system that you wish to
capture. For example, as the owner of a pond, you may want to extract fish for sale.
What are the fish population dynamics if you wish to extract fish at a constant rate of
3 % per year? To find the answer to this question, define an click on the stock FISH.
Click on the converter, then click onto the stock to have the converter connected to
the stock, and then drag the flow from the stock to the right. Now fish disappear from
the stock into a “cloud.” We are not explicitly modeling where they go. Figure 1.13
shows what you should have developed thus far as your STELLA model.
Next, define a new converter called EXTRACTION RATE and set is to 0.03.
Specify the outflow as:
EXTRACTION ¼ EXTRACTION RATE Ã FISH
ð1:3Þ
after making the appropriate connections with information arrows. Your model
should now look like as in Fig. 1.14.
Fig. 1.13
Fig. 1.14
20
1 Modeling Dynamic Biological Systems