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Self-organization (Rev #5, changes)

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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 simple example is the Belusov-Zhabotinsky reaction:Belusov-Zhabotinsky reaction:

B-Z reaction


From Wikipedia:

The most robust and unambiguous examples 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 and negative feedback
  • Balance of exploitation and exploration
  • Multiple interactions


Salvador Pueyo, Springer Climatic Change 2005 Self organized criticality and the Response of Wildland Fires to Climate Change

Here I present a new approach to forecasting the effects of climate change on catastrophic events, based on the ‘self-organised criticality’ concept from statistical physics. In particular, I develop the ‘self-organised critical fuel succession model’ (SOCFUS), which deals with wildland fires. I show that there is good agreement between model and data for the response pattern of the whole fire size statistical distribution to weather fluctuations in a boreal forest region. I tentatively predict the fire regime in this region for an instance of possible climate change scenario. I show that the immediate response is sharper than usually thought, but part of the added burning rate might not persist indefinitely. A large fraction of the extra burning in the transition period is likely to be concentrated in a few ‘climate change fires’, much larger than the largest fires that could currently occur. I also suggest that the major fire events recently observed in some tropical rainforest regions belong to a qualitatively different, even more abrupt type of response, which is also predicted by the model.

Abstract. A new cumulus convection parametrization is presented in this paper. The parametrization 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 self-consistent cloud spectrum. The model has been implemented in the ECHAM5 AGCM and tested 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 behaviors 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 behavior.

Many self-organized systems are thought to change their properties as a result of changes in individual behavior. 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 behavior 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 behaviors, no difference in aggregation behavior was found between hot and cool days. These results give an indication of the robustness of self-organized systems to changes in individual-level behavior. They demonstrate that information processing capabilities of self-organized 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.