  This chapter reviews the purpose and use of models from the field of complex
systems and, in particular, the implications of trying to use models to
understand or make decisions within complex situations, such as policy makers
usually face. A discussion of the different dimensions one can formalise
situations, the different purposes for models and the different kinds of
relationship they can have with the policy making process, is followed by an
examination of the compromises forced by the complexity of the target issues.
Several modelling approaches from complexity science are briefly described,
with notes as to their abilities and limitations. These approaches include
system dynamics, network theory, information theory, cellular automata, and
agent-based modelling. Some examples of policy models are presented and
discussed in the context of the previous analysis. Finally we conclude by
outlining some of the major pitfalls facing those wishing to use such models
for policy evaluation.
