Page 51 -

4 Different Modelling Purposes 45

• That information about the following are included: exactly what aspects it
predicts, guidelines on when the model can be used to predict and when not,
some guidelines as to the degree or kind of accuracy it predicts with and any
other caveats a user of the model should be aware of
• That the model code is distributed so others can explore when and how well it
predicts

4.3 Explanation

4.3.1 Motivation

Often, especially with complex social phenomena, one is particularly interested in
understanding why something occurs—in other words, explaining it. Even if one
cannot predict something before it is known, you still might be able to explain
it afterwards. This distinction mirrors that in the physical sciences where there
are both phenomenological and explanatory laws (Cartwright 1983)—the former
matches the data, whilst the latter explains why that came about. In mature science,
predictive and explanatory laws are linked in well-understood ways but with less
well-understood phenomena one might have one without the other. For example, the
gas laws that link measurements of temperature, pressure and volume were known
before the explanation in terms of molecules of gas bouncing randomly around and
the formal connection between both accounts only made much later. Understand-
ing is important for managing complex systems as well as understanding when
predictive models might work. Whilst generally with complex social phenomena
explanation is easier than prediction, sometimes prediction comes first (however, if
one can predict then this invites research to explain why the prediction works).
If one makes a simulation in which certain mechanisms or processes are
built in and the outcomes of the simulation match some (known) data, then this
simulation can support an explanation of the data using the built-in mechanisms. The
explanation itself is usually of a more general nature, and the traces of the simulation
runs are examples of that account. Simulations that involve complicated processes
can thus support complex explanations—that are beyond natural language reasoning
to follow. The simulations make the explanation explicit, even if we cannot fully
comprehend its detail. The formal nature of the simulation makes it possible to
test the conditions and cases under which the explanation works and to better its
assumptions.
Definition
By ‘explanation’ we mean establishing a possible causal chain from a set-up to its
consequences in terms of the mechanisms in a simulation.
Unpacking some parts of this:

•The possible causal chain is a set of inferences or computations made as part
of running the simulation—in simulations with random elements, each run will