Exploring the dynamics of complex systems from a fundamental perspective is crucial for further reasoning about the effects of our intervention strategies on complex challenges, whether climate change, global health or financial markets. By using novel mathematical and computational methods, we aim to better understand and predict complex processes.
Complex adaptive systems can be defined as systems with many parts that interact (often non-linearly) to produce emerging behaviour that cannot easily be explained in terms of the individual constituent elements (e.g. ecosystems, brains, cities). Characteristics of complex systems, such as tipping points, emergence, intractability and resilience (to name a few examples) are found in many of today’s most pressing scientific and societal challenges. Exploring the dynamics of complex systems from a fundamental perspective is crucial for further reasoning about the effects of our intervention strategies, and the controllability of the system itself.
This research theme explores new ways to reason about complexity using information theory and statistical mechanics and experimental methods such as agent-based models and complex networks.