Self-organization, also called spontaneous order in the social sciences, is a process where some form of overall order arises from local interactions between parts of an initially disordered system. The process can be spontaneous when sufficient energy is available, not needing control by any external agent. It is often triggered by seemingly random fluctuations, amplified by positive feedback. The resulting organization is wholly decentralized, distributed over all the components of the system. As such, the organization is typically robust and able to survive or self-repair substantial perturbation. Chaos theory discusses self-organization in terms of islands of predictability in a sea of chaotic unpredictability.
Self-organization occurs in many physical, chemical, biological, robotic, and cognitive systems. Examples of self-organization include crystallization, thermal convection of fluids, chemical oscillation, animal swarming, neural circuits, and black markets.
Self-organization is realized in the physics of non-equilibrium processes, and in chemical reactions, where it is often characterized as self-assembly. The concept has proven useful in biology, from the molecular to the ecosystem level. Cited examples of self-organizing behaviour also appear in the literature of many other disciplines, both in the natural sciences and in the social sciences (such as economics or anthropology). Self-organization has also been observed in mathematical systems such as cellular automata. Self-organization is an example of the related concept of emergence.
Self-organization relies on four basic ingredients:
strong dynamical non-linearity, often (though not necessarily) involving positive and negative feedback
balance of exploitation and exploration
multiple interactions among components
availability of energy (to overcome the natural tendency toward entropy, or loss of free energy)
The cybernetician William Ross Ashby formulated the original principle of self-organization in 1947.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
The goal of this course is to provide methods and tools for modeling distributed intelligent systems as well as designing and optimizing coordination strategies. The course is a well-balanced mixture
This course introduces advanced fabrication methods enabling the manufacturing of novel micro- and nanosystems (NEMS/MEMS). Both top-down techniques (lithography, stenciling, scanning probes, additive
Life has emerged on our planet from physical principles such as molecular self-organization, thermodynamics, stochastics and iterative refinement. This course will introduce the physical methods to st
A dissipative system is a thermodynamically open system which is operating out of, and often far from, thermodynamic equilibrium in an environment with which it exchanges energy and matter. A tornado may be thought of as a dissipative system. Dissipative systems stand in contrast to conservative systems. A dissipative structure is a dissipative system that has a dynamical regime that is in some sense in a reproducible steady state. This reproducible steady state may be reached by natural evolution of the system, by artifice, or by a combination of these two.
Predictability is the degree to which a correct prediction or forecast of a system's state can be made, either qualitatively or quantitatively. Causal determinism has a strong relationship with predictability. Perfect predictability implies strict determinism, but lack of predictability does not necessarily imply lack of determinism. Limitations on predictability could be caused by factors such as a lack of information or excessive complexity. In experimental physics, there are always observational errors determining variables such as positions and velocities.
A complex system is a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as power grid, transportation or communication systems, complex software and electronic systems, social and economic organizations (like cities), an ecosystem, a living cell, and ultimately the entire universe.
Explores self-organization in natural systems and foraging strategies of ants, including the Traveling Salesman Problem and Ant Colony Optimization algorithms.
Explains the Model-View-Controller architecture and its principles, emphasizing the independence of problem resolution from user dialogue and graphical display.
On propose dans ce MOOC de se former à et avec Thymio :
apprendre à programmer le robot Thymio et ce faisant, s’initier
à l'informatique et la robotique.
In diesem Kurs handelt es sich um das Verständnis der grundlegenden Mechanismen eines Roboters wie Thymio, seiner Programmierung mit verschiedenen Sprachen und seiner Verwendung im Unterricht mit den
In diesem Kurs handelt es sich um das Verständnis der grundlegenden Mechanismen eines Roboters wie Thymio, seiner Programmierung mit verschiedenen Sprachen und seiner Verwendung im Unterricht mit den
In the standard framework of self-consistent many-body perturbation theory, the skeleton series for the self-energy is truncated at a finite order N and plugged into the Dyson equation, which is then solved for the propagator G(N). We consider two examples ...
Control of nanomaterial dimensions with atomic precision through synthetic methods is essential to understanding and engineering of nanomaterials. For single-layer inorganic materials, size and shape controls have been achieved by self-assembly and surface ...
Since the discovery of dissipative Kerr solitons in optical microresonators, significant progress has been made in the understanding of the underlying physical principles from the fundamental side and generation of broadband coherent optical Kerr frequency ...