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56 B. Edmonds

what is essential for the prediction and the ‘noise’ (what cannot be predicted).
Establishing that a simulation is good for prediction requires its testing against
unknown data several times—this goes way beyond what is needed to establish
a candidate explanation for some phenomena. This is especially true for social
systems, where we often cannot predict events, but we can explain them after
they have occurred.
4. Illustration ! Theoretical exposition. A neat illustration of an idea suggests a
mechanism. Thus, the temptation is to use a model designed as an illustration or
playful exploration as being sufficient for the purpose of a theoretical exposition.
A theoretical exposition involves the extensive testing of code to check the
behaviour and the assumptions therein; an illustration, however suggestive, is
not that rigorous. For example, it may be that an illustrated process is a very
special case and only appears under very particular circumstances, or it may be
that the outcomes were due to aspects of the simulation that were thought to be
unimportant (such as the nature of a random number generator). The work to
rule out these kinds of possibility is what differentiates using a simulation as an
illustration from a theoretical exposition.
There is a natural progression in terms of purpose attempted as understanding
develops: from illustration to description or theoretical exposition, from description
to explanations and from explanations to prediction. However, each stage requires
its own justification and probably a complete reworking of the simulation code for
this new purpose. It is the lazy assumption that one purpose naturally follows from
another that is the danger.

4.8 Conclusion

In Table 4.1, we summarise the most important points of the above discussion. This
does not include all the risks of each kind of model but simply picks the most
pertinent ones.
As should be clear from the above discussion, being clear about one’s purpose in
modelling is central to how one goes about developing, checking and presenting the
results. Different modelling purposes imply different risks and hence activities to
avoid these. If one is intending the simulation to have a public function (in terms of
application or publication), then one should not model with unspecified or conflated
purposes. 13 Confused, conflated or unclear modelling purpose leads to unreliable
models that are hard to check, can create deeply misleading results and is hard for
readers to judge—in short, it is a recipe for bad science.

13
This does not include private modelling, whose purpose maybe playful or exploratory; however,
in this case one should not present the results or model as if they have achieved anything more than
illustration (to oneself). If one finds something of value in the exploration, it should then be redone
properly for a particular purpose to be sure it is worth public attention.