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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Mikhail Prokopenko
    • 1
    • 2
    • 3
  • Daniel Polani
    • 4
  • Nihat Ay
    • 5
    • 6
  1. 1.CSIRO Computational InformaticsEppingAustralia
  2. 2.School of PhysicsThe University of SydneySydneyAustralia
  3. 3.Department of ComputingMacquarie UniversityNorth RydeAustralia
  4. 4.Adaptive Systems Research Group, School of Computer ScienceUniversity of HertfordshireHatfieldUnited Kingdom
  5. 5.Max Planck Institute for Mathematics in the SciencesLeipzigGermany
  6. 6.Santa Fe InstituteSanta FeUSA

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