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,[1] 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.
