Modelling Complexity for Policy: Opportunities and Challenges

TL;DR

This chapter explores the use of complex systems models in policy-making, discussing their opportunities, challenges, and limitations in understanding complex issues for informed decision-making.

Contribution

It provides a comprehensive review of various complexity science modelling approaches and their applicability, highlighting the trade-offs and pitfalls in policy contexts.

Findings

Different modelling approaches have unique strengths and limitations.

Complexity introduces significant compromises in model accuracy and usability.

Using models for policy involves careful consideration of their purpose and context.

Abstract

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.