Page 210 -

10 Understanding Simulation Results 207

such models and, for example, Markov models, is somewhat diffuse; however, as the
history of components becomes more involved, so the power of modern modelling
paradigms comes to the fore. What is lacking, however, are the techniques that are
predicated on these new architectures. Whilst models which are specified at the
level of individual entities or “agents” may also be analysed using conventional
mathematical techniques, in Sect. 10.3 of the chapter, we will discuss some more
novel approaches which are moving the direction of understanding the outputs of
these new, unaggregated, models on their own terms.
One of the reasons that simulation models are such a powerful methodology
for understanding complex systems is their ability to display aggregate behaviour
which goes beyond the simple extrapolation of the behaviour of the individual
component parts. In mathematical analysis, such as dynamical systems theory, this
behaviour tends to be linked to notions of equilibrium, oscillation and catastrophe
or bifurcation. Individual- and agent-based modelling approaches have veered
more strongly towards the notion of emergence, which can be defined as “an
unforeseen occurrence; a state of things unexpectedly arising” (OED 2010). The
concept of emergence is essentially a sign of our ignorance of the causal pathways
within a system. Nevertheless, emergence is our clearest hope for developing an
understanding of systems using models. We hope that emergence will give us
a perceptual shortcut to the most significant elements of a system’s behaviour.
When it comes to applications, however, emergence is a rather double-edged blade:
emergence happily allows us to see the consequence of behaviours without us
having to follow the logic ourselves; however it is problematic in relying upon
us to filter out which of the ramifications are important to us. As emergence
is essentially a sign of incomplete understanding, and therefore weakly relative,
there is no objective definition of what is “important”. one day classification of
the kinds of patterns that relate to different types of causal history, but there is
no objective manner of recognising a pattern as “important” as such. These two
problems, finding “important” patterns (in the absence of any objective way of
defining “important”) and then interpreting what they mean in terms of causality,
are the issues standing between the researcher and perfect knowledge of a modelled
system. In the fourth section of this chapter, we will discuss some of the problems
with using model results to enhance our understanding of the underlying social
systems which they represent, and we will assert that this is in large degree a
problem of isolating causal mechanisms within the model architecture. In particular,
we highlight the issues of equifinality and identifiability—that the same behaviour
may be induced within a simulation from a variety of different model representations
or parameter sets—and present recommendations for dealing with this problem.
Since recognising emergence and combating the problems of identifiability and
equifinality are amongst the most urgent challenges to effective modelling of
complex systems, this leads naturally to a discussion of future directions in the final
section of the chapter.