Next, we set the temporal (time) parameters of the model. These are DT
(the time step over which the stock variables are updated) and the total time length
of a model run. Go to the RUN pull-down menu on the menu bar and select Time
Specs. . . A dialogue box will appear in which you can specify, among other things,
the length of the simulation, the DT, and the units of time. We arbitrarily choose
DT ¼ 1, length of time ¼ 20, and units of time ¼ years.
To display the results of our model, click on the graph icon (the left symbol in
Fig. 1.7) and drag it to the diagram. If we wanted to, we could display these results
in a table by choosing the table icon instead (the right symbol in Fig. 1.7).
When you create a new graph pad it will open automatically. To open a pad that
had been created previously, just double-click on it to display the list of stocks,
flows, and parameters for our model. Each one can be plotted. Select FISH to be
plotted and, with the ) arrow, add it to the list of selected items. Then set the scale
from 0 to 600 and check OK. You can set the scale by clicking once on the variable
whose scale you wish to set and then on the arrow next to it (Fig. 1.8). Now you can
select the minimum on the graph and the maximum value defines the highest point
on the graph. Rerunning the model under alternative parameter settings will lead to
graphs that are plotted over different ranges. Sometimes these are a bit difficult to
compare with previous runs, because the scaling has changed, unless, of course, you
have fixed the scale, as we suggest here.
Would you like to see the results of our model so far? We can run the model by
selecting RUN from the pull-down menu. The results are shown in Fig. 1.9.
We see a graph of exponential growth of the fish population in your pond. This is
what we should have expected. It is important to state beforehand what results you
expect from running a model. Such speculation builds your insight into system
behavior and helps you anticipate (and correct) programming errors. When the
results do not meet your expectations, something is wrong and you must fix it. The
error may be either in your STELLA program or your understanding of the system
that you wish to model, or both.
Fig. 1.6
Fig. 1.7
1.5 Modeling in STELLA
15
(the time step over which the stock variables are updated) and the total time length
of a model run. Go to the RUN pull-down menu on the menu bar and select Time
Specs. . . A dialogue box will appear in which you can specify, among other things,
the length of the simulation, the DT, and the units of time. We arbitrarily choose
DT ¼ 1, length of time ¼ 20, and units of time ¼ years.
To display the results of our model, click on the graph icon (the left symbol in
Fig. 1.7) and drag it to the diagram. If we wanted to, we could display these results
in a table by choosing the table icon instead (the right symbol in Fig. 1.7).
When you create a new graph pad it will open automatically. To open a pad that
had been created previously, just double-click on it to display the list of stocks,
flows, and parameters for our model. Each one can be plotted. Select FISH to be
plotted and, with the ) arrow, add it to the list of selected items. Then set the scale
from 0 to 600 and check OK. You can set the scale by clicking once on the variable
whose scale you wish to set and then on the arrow next to it (Fig. 1.8). Now you can
select the minimum on the graph and the maximum value defines the highest point
on the graph. Rerunning the model under alternative parameter settings will lead to
graphs that are plotted over different ranges. Sometimes these are a bit difficult to
compare with previous runs, because the scaling has changed, unless, of course, you
have fixed the scale, as we suggest here.
Would you like to see the results of our model so far? We can run the model by
selecting RUN from the pull-down menu. The results are shown in Fig. 1.9.
We see a graph of exponential growth of the fish population in your pond. This is
what we should have expected. It is important to state beforehand what results you
expect from running a model. Such speculation builds your insight into system
behavior and helps you anticipate (and correct) programming errors. When the
results do not meet your expectations, something is wrong and you must fix it. The
error may be either in your STELLA program or your understanding of the system
that you wish to model, or both.
Fig. 1.6
Fig. 1.7
1.5 Modeling in STELLA
15