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10 Understanding Simulation Results 213

10.3 Individual Patterns, Novel Approaches
and Visualisation

Plainly aggregate statistics like those above are a useful way of simplifying
individual-level data, both in terms of complexity and dimensionality. However,
they are the result of over 2500 years of mathematical development in a research
environment unsuited to the mass of detail associated with individual-level data.
Now, computers place us in the position of being able to cope with populations
of individual-level data at a much smaller scale. We still tend to place our own
understanding at the end of an analytical trail, constraining the trail to pass through
some kind of simplification and higher level of aggregation for the purposes of
model analysis. Despite this, it is increasingly true that individual-level data is dealt
with at the individual level for the body of the analysis, and this is especially true in
the case of individual-level modelling, in which experimentation is almost always
enacted at the individual level. Whether it is really necessary to simplify for human
understanding at the end of an analysis is not especially clear. It may well be that
better techniques might be developed to do this than those built on an assumption of
the necessity of aggregation.
At the individual level, we are interested in recognising patterns in space and
time, seeing how patterns at different scales affect each other, and then using this
to say something about the behaviour of the system/individuals. Patterns are often
indicators of the attractors to which individuals are drawn in any given system and
present a shortcut to understanding the mass of system interactions. However, it is
almost as problematic to go through this process to understand a model as it is, for
example, to derive individual-level behaviours from real large-size spatio-temporal
datasets of socio-economic attributes. The one advantage we have in understanding
a model is that we do have some grip on the foundation rules at the individual scale.
Nonetheless, understanding a rule and determining how it plays out in a system
of multiple interactions are very different things. Table 10.2 outlines some of the
problems.
Despite the above, our chief tool for individual-level understanding without
aggregation is, and always has been, the human ability to recognise patterns in

Table 10.2 Issues related to understanding a model at different levels of complexity
Complexity Issues
Spatial What is the impact of space (with whom do individuals initiate transactions
and to what degree)?
Temporal How does the system evolve?
Individuals How do we recognise which individual behaviours are playing out in the
morass of interactions?
Relationships How do we recognise and track relationships?
Scale How can we reveal the manner in which individual actions affect the
large-scale system and vice versa?