The Azimuth Project
Self-organization (Rev #1)

Contents

Idea

Stated in the article on Wikipedia:

Self-organization is the process where a structure or pattern appears in a system without a central authority or external element imposing it through planning. This globally coherent pattern appears from the local interaction of the elements that make up the system, thus the organization is achieved in a way that is parallel (all the elements act at the same time) and distributed (no element is a central coordinator).

One example is the Belusov-Zhabotisnksy reaction:

B-Z reaction

Details

From Wikipedia:

The most robust and unambiguous examples[1] of self-organizing systems are from the physics of non-equilibrium processes. Self-organization is also relevant in chemistry, where it has often been taken as being synonymous with self-assembly. The concept of self-organization is central to the description of biological systems, from the subcellular to the ecosystem level. There are also cited examples of “self-organizing” behaviour found in the literature of many other disciplines, both in the natural sciences and the social sciences such as economics or anthropology. Self-organization has also been observed in mathematical systems such as cellular automata.

Sometimes the notion of self-organization is conflated with that of the related concept of emergence.[citation needed] Properly defined, however, there may be instances of self-organization without emergence and emergence without self-organization, and it is clear from the literature that the phenomena are not the same. The link between emergence and self-organization remains an active research question.

Self-organization usually relies on three basic ingredients:

  • Strong dynamical non-linearity, often though not necessarily involving Positive feedback? and Negative feedback?
  • Balance of exploitation and exploration
  • Multiple interactions

References

Abstract. A new cumulus convection parameterisation is presented in this paper. The parameterisation uses an explicit spectral approach and determines, unlike other convection schemes, for each convection event a new cloud distribution function regarding to the given vertical temperature and humidity profiles. This is done by using a one dimensional cloud model to create a spectrum of different clouds. The interaction between all non convective physical processes in the AGCM and all different clouds is taken into account to calculate a selfconsistent cloud spectrum. The

model has been implemented in the ECHAM5 AGCM andtested against a large eddy simulation model. The representation of a shallow cumulus cloud field by the AGCM could be much improved. Diurnal cycle, cloud cover, liquid water path and the vertical structure of the mass flux, determined by the new convection scheme are close to the large eddy simulation, whereas the standard convection scheme failed in simulating this convection episode.

abstract: Perfect behaviours that are optimal to the environment an agent operates within rarely exist in real animals or in robotic systems. The costs (be they biological or economic) of building sensors and processing the information they capture become excessive compared to the small advantages that occur from the modifications of behaviour.

Many self-organised systems are thought to change their properties as a result of changes in individual behaviour. Here, using both natural systems and computer simulations, we demonstrate that intertidal snail aggregations slightly decrease in size when individuals forage for shorter periods due to hotter and more desiccating conditions – a non-optimal behaviour for the snails since aggregation reduces desiccation stress. However, this decrease only occurs in simple experimental systems (and simulations of these systems). When studied in their more complex natural environment, and when simulated in such an environment, using the same information-processing behaviours, no difference in aggregation behaviour was found between hot and cool days. These results give an indication of the robustness of selforganised systems to changes in individual-level behavior. They demonstrate that information processing capabilities of self-organised groups may not need to be as great as for agents that perform solitary tasks, and also that oversimplified tests of swarm intelligence may not give a true indication of how tasks may be performed in a more complex environment.