A computational framework for understanding hierarchical emergence in complex systems
This paper presents a novel computational framework for understanding emergence in complex systems. The authors develop a formalism based on how software works, establishing a hierarchy of nested self-contained processes that determines what computations occur at what level in a complex system. This approach goes beyond existing tools that only identify when emergence takes place, by also explaining how it does. The framework is illustrated on models from statistical physics and computational neuroscience, showing macroscopic processes akin to software in engineered systems. The work aims to enable better simulation, prediction, and control of multi-level complex systems.
Key quotes
Here we address this limitation by developing a computational approach to emergence, which characterises macroscopic processes in terms of their computational capabilities.
This framework establishes a hierarchy of nested self-contained processes that determines what computations take place at what level, which in turn delineates the functional architecture of a complex system.
This approach is illustrated on paradigmatic models from the statistical physics and computational neuroscience literature, which are shown to exhibit macroscopic processes that are akin to software in human-engineered systems.
Overall, this framework enables a deeper understanding of the multi-level structure of complex systems, revealing specific ways in which they can be efficiently simulated, predicted, and controlled.
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