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.
- Pub Date:
- October 2013
- Computer Science - Multiagent Systems;
- Computer Science - Computers and Society;
- Nonlinear Sciences - Adaptation and Self-Organizing Systems;
- Physics - Physics and Society
- Draft for the Handbook on Complexity and Public Policy, edited by Robert Geyer and Paul Cairney