2.4. Sources of Model Errors
37
tion, your table will report the results with an accuracy of more than the
two decimal places that would otherwise be listed. However, irrespective of
the level of precision at which you report the results, the computation itself
is not affected by that choice. Click OK and run the model. Note the results
(perhaps lock the results of this page of the table much as you would lock
results on a page of a graph), and then proceed to change the DT to a
smaller value. Compare the results from one model run to the next. Repeat
this process for different solution methods, and observe changes in model
errors . Other sources of errors are discussed in more detail in the following
section.
2.4. Sources of Model Errors
Error is associated with virtually every aspect of a model. As we have discussed, there are errors involved in the algorithms-and sometimes even at
the hardware level of the computer-used to numerically solve the model.
There are also a set of errors associated with the conceptualization of the
model. These are the topic of this section'.
Any model is an abstraction. In the process of making this abstraction, a
limited set of system components and their interactions are considered.
Those system features which are assumed to not influence the system's dynamics over the relevant temporal and spatial range to which the model applies, and which are considered not pertinent in answering the questions
that the model addresses are not taken into account. Errors of exclusion
come from not noticing that a particular system feature does have a relevant
influence on the dynamics of the system components that are explicitly
modeled, and thus on the results of the model. A model of the impacts of
fishing activities on fish population dynamics may assume constancy of
physical features of the habitat within which fishing takes place. However,
the choice of fishing gear may affect habitat. For example, otter trawls
may reduce vertical relief and complexity of the sea floor. As a consequence, fewer hiding spaces may exist for juveniles. Fewer hiding spaces
may mean increased exposure to predators and therefore elevated juvenile
mortality. Fishing may thus not only have immediate impacts on adult populations, but also time-lagged impacts via the destruction of sea floor habitat, and consequent higher juvenile mortality and reduced recruitment into
the fishery.
Errors of inclusion are associated with explicitly modeling aspects of the
system that are not relevant for an understanding of its dynamics, and have
no bearing on model result. Unnecessary effort goes into those parts without
corresponding gain. At a given modeling budget or time frame available for
"The discussion of sources of errors presented in this section follows Westervelt
(2001) .
37
tion, your table will report the results with an accuracy of more than the
two decimal places that would otherwise be listed. However, irrespective of
the level of precision at which you report the results, the computation itself
is not affected by that choice. Click OK and run the model. Note the results
(perhaps lock the results of this page of the table much as you would lock
results on a page of a graph), and then proceed to change the DT to a
smaller value. Compare the results from one model run to the next. Repeat
this process for different solution methods, and observe changes in model
errors . Other sources of errors are discussed in more detail in the following
section.
2.4. Sources of Model Errors
Error is associated with virtually every aspect of a model. As we have discussed, there are errors involved in the algorithms-and sometimes even at
the hardware level of the computer-used to numerically solve the model.
There are also a set of errors associated with the conceptualization of the
model. These are the topic of this section'.
Any model is an abstraction. In the process of making this abstraction, a
limited set of system components and their interactions are considered.
Those system features which are assumed to not influence the system's dynamics over the relevant temporal and spatial range to which the model applies, and which are considered not pertinent in answering the questions
that the model addresses are not taken into account. Errors of exclusion
come from not noticing that a particular system feature does have a relevant
influence on the dynamics of the system components that are explicitly
modeled, and thus on the results of the model. A model of the impacts of
fishing activities on fish population dynamics may assume constancy of
physical features of the habitat within which fishing takes place. However,
the choice of fishing gear may affect habitat. For example, otter trawls
may reduce vertical relief and complexity of the sea floor. As a consequence, fewer hiding spaces may exist for juveniles. Fewer hiding spaces
may mean increased exposure to predators and therefore elevated juvenile
mortality. Fishing may thus not only have immediate impacts on adult populations, but also time-lagged impacts via the destruction of sea floor habitat, and consequent higher juvenile mortality and reduced recruitment into
the fishery.
Errors of inclusion are associated with explicitly modeling aspects of the
system that are not relevant for an understanding of its dynamics, and have
no bearing on model result. Unnecessary effort goes into those parts without
corresponding gain. At a given modeling budget or time frame available for
"The discussion of sources of errors presented in this section follows Westervelt
(2001) .