Representing Human Decision-Making in Environmental Modelling - Environmental Modelling: Finding Simplicity in Complexity - page 293

2000). In this case the modeller is more interested in
understanding and representing system structure (and
its change) correctly - by considering what is essential
to system or agent behaviour versus what is the result of
historical conditions or path dependency - than making
accurate forecasts of the future. Indeed, if we are to
get anywhere near developing 'universal laws' for the
complex, adaptive systems that result from the behaviour
of humans and their decision-making, a focus on initial,
antecedent and exogenous conditions may be particularly
important (e.g. Ballinger, 2008).
The representation of a broader range of human
behaviours and decision-making contexts than has been
considered to date should be welcomed in future models
of human decision-making. However, as environmental
modellers we should remember that the ultimate reason
to represent human activity in our models is to improve
decision-making about environmental systems, not hin-
der it. Consequently, it may often be more appropriate
to aim for a model and model-building process that
can practically contribute to the decision-making pro-
cess rather than one that provides a mimetically accurate
reproduction of observed system behaviour (in the vain
hope that it will therefore do a good job of forecasting
the future). The development of a simple model that
focuses on facilitating communication between disparate
groups of experts or publics (like in companion modelling
presented above), may be far more useful for decision-
making purposes than a complex 'would-be world' that
proves incomprehensible. Uncertainty is central to the
human condition and to the outcomes of models of the
types we have discussed above and decision-makers are
used to making decisions the outcomes of which are
uncertain. Whilst models and representations of human-
decision making will inevitably be 'wrong' (see Chapter 2),
whether they are useful depends on how we apply them
and how well we communicate their assumptions and
inherent uncertainties.

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