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4 Different Modelling Purposes 49

• A use of theoretical exposition can be to refute a hypothesis, by exhibiting a
concrete counterexample, or to establish a hypothesis.
• Although any simulation has to have some meaning for it to be a model
(otherwise it would just be some arbitrary code), this does not involve any other
relationship with the observed world in terms of data or evidence.
Example Schelling developed his famous model for a theoretical purpose. He was
advising the Chicago district on what might be done about the high levels of
segregation there. The assumption was that the sharp segregation observed must be a
result of strong racial discrimination by its inhabitants. Schelling’s model (Schelling
1969, 1971) showed that segregation could result from just weak preferences of
inhabitants for their own kind—that even, a wish for 30% of people of the same
trait living in the neighbourhood could result in segregation. This was not obvious
without building a model, and Schelling did not rely on the results of his model
alone but did extensive mathematical analysis to back up its conclusions.
What the model did not do is say anything about what actually caused the
segregation in Chicago—it might well be the result of strong racial prejudice. The
model did not predict anything about the level of segregation nor did it explain it.
All it did was provide a counterexample to the current theories as to the cause of the
segregation, showing that this was not necessarily the case.

4.4.2 Risks

In theoretical exposition, one is not relating simulations to the observed world, so it
9
is fundamentally an easier and ‘safer’ activity. Since a near-complete understanding
of the simulation behaviour is desired, this activity is usually concerned with
relatively simple models. However, there are still risks—it is still easy to fool oneself
with one’s own model. Thus, the main risk is that there is a bug in the code, so that
what one thinks one is establishing about a set of mechanisms is really about a
different set of mechanisms (i.e. those including the bug).
A second area of risk lies in a potential lack of generality or ‘brittleness’ of
what is established. If the hypothesis is true but only holds under very special
circumstances, then this reduces the usefulness of the hypothesis in terms of
understanding the simulation behaviour.
Lastly, there is the risk of over-interpreting the results in terms of saying anything
about the observed world. The model might suggest a hypothesis about the observed
world, but it does not provide any level of empirical support for this.

9 In the sense of not being vulnerable to being shown to be wrong later