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4 Different Modelling Purposes 51

Unpacking some of this:
• This is not an attempt to produce a one-one representation of what is being
observed but only of the features thought to be relevant for the intended kind
of study. It will leave out some features; in particular, it may leave out some of
the interactions between processes.
• It is not in any sense general, but it seeks to capture a restricted set of cases—it
is specific to these, and no kind of generality beyond these can be assumed.
• The simulation has to relate in an explicit and well-documented way to a set
of evidence, experiences and data. This is the opposite of theoretical exposition
and should have a direct and immediate connection with observation, data or
experience.
Example In Moss (1998), Scott Moss describes a model that captures some of the
interactions in a water pumping station during crises. This came about through
extensive discussions with stakeholders within a UK water company about what
happens in particular situations during such crises. The model sought to directly
reflect this evidence within the dynamic form of a simulation, including cognitive
agents who interact to resolve the crisis. This simulation captured aspects of the
physical situation but also tackled some of the cognitive and communicative aspects.
To do this, he had represented the problem solving and learning of key actors,
so he inevitably had to use some existing theories and structures—namely, Alan
Newell and Herbert Simon’s ‘general problem solving architecture’ (Newell and
Simon 1972) and Cohen’s ‘endorsement mechanism’ (Cohen 1984a, b). However,
this is all made admirably explicit in the paper. The paper is suitably cautious in
terms of any conclusions, saying that the simulation ‘indicate[s] a clear need for an
investigation of appropriate organizational structures and procedures to deal with
full-blown crises’.

4.5.2 Risks

Any system for representation will have its own affordances—it will be able to
capture some kinds of aspect much more easily than others will. This inevitably
biases the representations produced, as those elements that are easy to represent are
more likely to be captured than those which are more difficult. Thus, the medium
will influence what is captured and what is not.
Since agent-based simulation is not theoretically constrained, 10 there are a large
number of ways in which any observed phenomena could be expressed in terms
of simulation code. Thus, it is almost inevitable that any modeller will use some

10
To be precise, it does assume there are discrete entities or objects and that there are processes
within these that can be represented in terms of computations, but these are not very restrictive
assumptions.