Examples of self-organising systems can be found practically everywhere: a heated fluid forms regular convection patterns of Bénard cells, neuronal ensembles self-organise into complex spike patterns, a swarm changes its shape in response to an approaching predator, ecosystems develop spatial structures in order to deal with diminishing resources, and so on.
Typically, self-organisation (SO) is defined as the evolution of a system into an organised form in the absence of external pressures. SO within a system brings about several attractive properties, in particular robustness, adaptability, and scalability. Consequently, a natural question to ask would be: Is it possible to guide the process of self-organisation towards some desirable patterns and outcomes? Over the last decades, it has become apparent that this question can be rigorously formalised across multiple domains, leading to the emergence of a new research field: Guided Self-Organisation (GSO). This has led to theoretical developments in information theory, network theory, dynamical systems, game theory, systems biology, and sociophysics, as well as practical applications in artificial intelligence, synthetic biology, unconventional computation, distributed robotics, and active matter.
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Complexity Digest 