Page 61 -

4 Different Modelling Purposes 55

model justified for one purpose might be used as part of the development of
a simulation model for another purpose—this can be how science progresses.
However, just because a model for one purpose suggests a model for another does
not mean it is a good model for the new purpose. If it is being suggested that a
model can be used for a new purpose, it has to be justified for this new purpose. To
drive home this point further, we look at some common confusions of purpose to
underline this danger. Each time some code is mistakenly relied upon for a purpose
other than has been established for it.
1. Theoretical exposition ! Explanation. Once one has immersed oneself in a
model, there is a danger that the world looks like this model to its author.
This is a strong kind of Kuhn’s ‘theoretical spectacles’ 12 and results from the
intimate relationship that simulation developers have with their model. Here, the
temptation is to jump from a theoretical exposition, which has no empirical basis,
to an explanation of something in the world. A simulation can provide a way of
looking at some phenomena, but just because one can view some phenomena in
a particular way does not make it a good explanation. Of course, one can form a
hypothesis from anywhere, including from a theoretical exposition, but it remains
only a hypothesis until it is established as a good explanation as discussed above
(which would almost certainly involve changing the model).
2. Description ! Explanation. In constructing a simulation for the purpose of
describing a small set of observed cases, one has deliberately made many
connections between aspects of the simulation and evidence of various kinds.
Thus, one can be fairly certain that, at least, some of its aspects are realistic. Some
of this fitting to evidence might be in the form of comparing the outcomes of the
simulation to data, in which case it is tempting to suggest that the simulation
supports an explanation of those outcomes. The trouble with this is twofold: (a)
theworktotest which aspects of that simulation are relevant to the aspects being
explained has not been done; and (b) the simulation has not been established
against a range of cases—it is not general enough to make a good explanation. An
explanation that only explains aspects of a small number of cases using a complex
simulation is a bad explanation since there will be many other potentialities in
the simulation that are not used for these few cases.
3. Explanation ! Prediction. A simulation that establishes an explanation traces
a (complex) set of causal steps from the simulation set-up to outcomes that
compare well with observed data. It is thus tempting to suggest that one can
use this simulation to predict this observed data. However, the process of using
a simulation to establish and understand an explanation inevitably involves
iteration between the data being explained and the model specification—that is,
the model is fitted to that particular set of data. Model fitting is not a good way
to construct a model useful for prediction, since it does not distinguish between

12
Kuhn (1962) pointed out the tendency of scientists to only see the evidence that is coherent
with an existing theory—it is as if they have ‘theoretical spectacles’ that filter out other kinds of
evidence.