This report assesses the relevance and applications of 'complexity science' — a term that encompasses inter-relationships between different disciplines and objectives in international development projects. It provides a definition, gives examples of actual and potential applications, and identifies future possibilities and challenges.

The report focuses on how to apply the methodologies of complexity science — such as nonlinear dynamics, stochastic processes, agent-based models and machine learning — to study complex systems such as climate change and economic forecasts. It examines several areas of complexity science in detail, showing how they are likely to be beneficial for a range of international development scenarios, and offers an example of success in the automated use of data to improve the rate of correctly classifying soybean disease. It concludes that increasing the availability of data will make complexity science increasingly important, raising questions about how to best use this data and improve their availability and reliability.


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