Sooner or later, much of the research of systems theory and complexity arrives at the topic of self-organization, the spontaneous local interaction between elements of an initially disordered system as analyzed by Ilya Prigogine. This is so for a variety of reasons: first off, self-organization, if statistically significant and meaningfully predictable, may be superior to organization by command because, aside from factors influencing its design, it does not require instruction, supervision or enforcement – the organizer may spare himself critical components of due diligence (and therefore potentially resulting liability) which, in and of itself, can amount to a rather significant difference in cost-efficiency for any purpose-oriented organization.
Self-organization has been receiving much attention since the dawn of intelligent observation of swarms of fish, birds, anthills, beehives and – with increasingly obvious similarities – human behavior in cities. Later, a thermodynamic view of the phenomenon prevailed over the initial cybernetics approach. Based on initial observations on the mathematics of self-organizing systems by Norbert Wiener, they follow algorithms relying on sensor data, interacting with neighbors, looking for patterns. Such pattern-oriented behavior makes the swarm as a whole much more resilient in self-assembling structures that, given a certain numerical threshold and degree of complexity, may not be able to be destroyed by almost any influence.
This is also where robotics approximates nature and the observations of aspects of “swarm intelligence” in cells, social insects and higher-developed species of social animals like birds and fish. Swarm intelligence is the collective behavior of decentralized, self-organized systems.