Handbook on Complexity and Public Policy
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Handbook on Complexity and Public Policy

Edited by Robert Geyer and Paul Cairney

Though its roots in the natural sciences go back to the early 20th century, complexity theory as a scientific framework has developed most rapidly since the 1970s. Increasingly, complexity theory has been integrated into the social sciences, and this groundbreaking Handbook on Complexity and Public Policy has brought together top thinkers in complexity and policy from around the world. With contributions from Europe, North America, Brazil and China this comprehensive Handbook splits the topic into three cohesive parts: Theory and Tools, Methods and Modeling, and Application.
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Chapter 13: Modelling complexity for policy: opportunities and challenges

Bruce Edmonds and Carlos Gershenson


For policy and decision-making, models can be an essential component, as models allow the description of a situation, the exploration of future scenarios, the valuation of different outcomes and the establishment of possible explanations for what is observed. The principle problem with this is the sheer complexity of what is being modelled. A response to this is to use more expressive modelling approaches, drawn from the ‘sciences of complexity’– to use more complex models to try and get a hold on the complexity we face. However, this approach has potential pitfalls as well as opportunities, and it is these that this chapter will attempt to make clear. Thus, we hope to show that more complex modelling approaches can be useful, but also to help people avoid ‘fooling themselves’ in the process. The chapter is basically in three parts: a general discussion about models and their characteristics that will inform the subsequent decision and help the reader understand their potential and difficulties, then a brief review of some of the available techniques, and ending with a review of some models used in a policy context. It thus starts with an examination of the different kinds of model that exist, so that these kinds might be clearly distinguished and not confused. In particular it looks at what it means for a model to be formal. A section follows on the kinds of uses to which such models can be put.

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