Software in the natural world: A computational approach to hierarchical emergence
arxiv(2024)
摘要
Understanding the functional architecture of complex systems is crucial to
illuminate their inner workings and enable effective methods for their
prediction and control. Recent advances have introduced tools to characterise
emergent macroscopic levels; however, while these approaches are successful in
identifying when emergence takes place, they are limited in the extent they can
determine how it does. Here we address this limitation by developing a
computational approach to emergence, which characterises macroscopic processes
in terms of their computational capabilities. Concretely, we articulate a view
on emergence based on how software works, which is rooted on a mathematical
formalism that articulates how macroscopic processes can express self-contained
informational, interventional, and computational properties. 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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