Irrigation systems are used across the world to produce food for human populations. This project focuses on self-organising irrigation systems. These systems are facing unprecedented change due to economic globalisation and global environmental change. They are a necessity for the people who manage them, for their livelihood and survival but also provide examples to the outside world of sustainable systems, which can persist for long periods with little or no outside input. International development strategies have so far had mixed success in intervening with such systems. By better understanding their evolution, improved site-specific adaptive management strategies can be implemented to allow the system to evolve in the face of change with limited negative consequences.
Many Indigenous Irrigation Systems (IIS) evolve over long periods to reach a state, which could be described as optimal for the given social and ecological conditions. Along this journey IIS encounter many perturbations, which affect their final make up. Using evolutionary computing methods this project seeks to evolve (and validate against real case studies) simplified irrigation systems for different scenarios with the hope of gaining insight into how a similar real-life system may evolve to a given state. Preliminary results show how different social structures can increase robustness to collapse when input into the system is reduced.
