On the Cross-Disciplinary Nature of Guided Self-Organisation

Abstract

Self-organisation is pervasive: neuronal ensembles self-organise into complex spatio-temporal spike patterns which facilitate synaptic plasticity and long-term consolidation of information; large-scale natural or social systems, as diverse as forest fires, landslides, or epidemics, produce spontaneous scale-invariant behaviour; robotic modules self-organise into coordinated motion patterns; individuals within a swarm achieve collective coherence out of isolated actions; and so on. Selforganisation is also valuable: the resultant increase in an internal organisation brings benefits to the (collective) organism, be it a learning brain, a co-evolving ecosystem, an adapting modular robot, or a re-configuring swarm. These benefits are typically realised in increased resilience to external disturbances, adaptivity to novel tasks, and scalability with respect to new challenges. However, self-organisation is difficult to engineer on demand: the intricate fabric of interactions within a self-organising system cannot follow a simple-minded blueprint and resists crude interventions.