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Towards a Context- and Scope-Sensitive Analysis
for Specifying Agent Behaviour
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University, Manchester, United Kingdom
bruce@edmonds.name
Abstract.
A structure for analysing narrative data is suggested, one that
distinguishes three parts: context, scope and narrative elements. This structure
is first motivated and then illustrated with some simple examples taken from
Sukaina Bhawani's thesis. It is hypothesised that such a structure might be
helpful in preserving more of the natural meaning of such data, as well as being
a good match to a context-dependent computational architecture. This structure
could clearly be combined and improved by other methods, such as Grounded
Theory. Finally some criteria for judging any such method are suggested.
1
Introduction
Agent-based modellers have always used a variety of sources to inform the design of
their simulations. These have included: existing theories, tradition, expert opinion,
intuition, experimental results, talking to stakeholders, summaries of other research,
and narrative data. This paper considers the later of these, namely narrative data. This
involves analysing transcribed accounts obtained from stakeholders and using these to
inform the specification of the behavioural rules of corresponding agents within a
simulation. This method goes beyond simply talking to stakeholders and using the
understanding gained to inform ones modelling. It divides the process into stages: (a)
conducting interviews, (b) transcribing these into text (the “narrative data”), (c)
analysing this data, and finally (d) specifying the behavioural rules for a social
simulation. As far as I am aware, Richard Taylor [17] and Sukaina Bharwani [4]
were the first to do this.
The advantages of doing this analysis in this staged and formalised ways should be
obvious. The data can be made available and examined by subsequent researchers
who may spot aspects that the original observer has missed as well as seeing how the
sense of the stakeholder might have been changed during the process. Unlike an
informal approach, which does not work from transcribed text, a subsequent
researcher can follow the chain of analysis and understand it better. This mirrors
attempts to improve the documentation of simulation code and to make simulations
easier to replicate and investigate. Although the process of analysing natural
language data is never going to be a completely formal process and relies upon the
understanding of the interviewer and/or analyst, formalising the process makes it
more transparent and replicable.
