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what our models tell us through their internal workings and logic, how we might
understand/interpret simulation results as results about an attempted simulation
of the real world, rather than as results we expect to compare directly with the
world. Here then, we tackle the third purpose of modelling: the exploration of
abstracted systems through simulation. In a sense, this is a purpose predicated
only on the limitations of the human mind. By common definition, simulation
modelling is concerned with abstract representations of entities within systems
and their interrelationships and with the exploration of the ramifications of these
abstracted behaviours at different temporal and geographical scales. In a world in
which we had larger brains, models would not be required to reveal anything—we
would instantly see the ramifications of abstracted behaviours in our heads. To a
degree, therefore, models may be seen as replacing the hard joined-up thinking that
is required to make statements about the way the world works. This chapter looks at
what this simplifying process tells us about the systems we are trying to replicate.
In part, the complications of simulation modelling are a product of the dimen-
sionality of the systems with which we are dealing. Let us imagine that we are
tackling a system of some spatio-temporal complexity, for example, the prices in
a retail market selling items A, B and C. Neighbouring retailers adjust their prices
based on local competition, but the price of raw materials keeps the price surface
out of equilibrium. In addition, customers will only buy one of the products at a
time, creating a link between the prices of the three items. Here, then, we have
three interdependent variables, each of which varies spatio-temporally, with strong
auto- and cross-correlations in both time and space. What kinds of techniques can
be used to tease apart such complex systems? In Sect. 10.2 of this chapter, we will
discuss some of the available methodologies broken down by the dimensionality
of the system in question and the demands of the analysis. Since the range of
such techniques is extremely sizable, we shall detail a few traditional techniques
that we believe might be helpful in simplifying model data that shows the traits of
complexity and some of the newer techniques of promise.
Until recently, most social science models represented social systems using
mathematical aggregations. We have over 2500 years’ worth of techniques to call
upon that are founded on the notion that we need to simplify systems as rapidly as
we can to the point at which the abstractions can be manipulated within a single
human head. As is clear, not least from earlier contributions in this volume, it
is becoming increasingly accepted that social scientists might reveal more about
systems by representing them in a less aggregate manner. More specifically, the
main difference between mathematics and the new modelling paradigm is that we
now aspire to work at a scale at which the components under consideration can be
represented as having their own discrete histories; mathematics actually works in a
very similar fashion to modern models, but at all the other scales. Naturally there are
knock-ons from this in terms of the more explicit representation of objects, states
and events, but these issues are less important than the additional simulation and
analytical power that having a history for each component of a system gives us. Of
course, such a “history” may just be the discrete position of an object at a single
historical moment, and plainly at this level of complication, the boundary between