Physics:Self-organized criticality

Short description: Concept in physics

An image of the 2d Bak-Tang-Wiesenfeld sandpile, the original model of self-organized criticality.

Self-organized criticality (SOC) is a property of dynamical systems that have a critical point as an attractor. Their macroscopic behavior thus displays the spatial or temporal scale-invariance characteristic of the critical point of a phase transition, but without the need to tune control parameters to a precise value, because the system, effectively, tunes itself as it evolves towards criticality.

The concept was put forward by Per Bak, Chao Tang and Kurt Wiesenfeld ("BTW") in a paper published in 1987 in Physical Review Letters, and is considered to be one of the mechanisms by which complexity arises in nature. Its concepts have been applied across fields as diverse as geophysics,

Despite the considerable interest and research output generated from the SOC hypothesis, there remains no general agreement with regards to its mechanisms in abstract mathematical form. Bak Tang and Wiesenfeld based their hypothesis on the behavior of their sandpile model.

Models of self-organized criticality

In chronological order of development:

• Stick-slip model of fault failure
• Bak–Tang–Wiesenfeld sandpile
• Forest-fire model
• Olami–Feder–Christensen model
• Bak–Sneppen model

Early theoretical work included the development of a variety of alternative SOC-generating dynamics distinct from the BTW model, attempts to prove model properties analytically (including calculating the critical exponents), and examination of the conditions necessary for SOC to emerge. One of the important issues for the latter investigation was whether conservation of energy was required in the local dynamical exchanges of models: the answer in general is no, but with (minor) reservations, as some exchange dynamics (such as those of BTW) do require local conservation at least on average .

It has been argued that the BTW "sandpile" model should actually generate 1/f² noise rather than 1/f noise. This claim was based on untested scaling assumptions, and a more rigorous analysis showed that sandpile models generally produce 1/f^a spectra, with a<2. Other simulation models were proposed later that could produce true 1/f noise.

In addition to the nonconservative theoretical model mentioned above , other theoretical models for SOC have been based upon information theory, mean field theory, the convergence of random variables, and cluster formation. A continuous model of self-organised criticality is proposed by using tropical geometry.

Key theoretical issues yet to be resolved include the calculation of the possible universality classes of SOC behavior and the question of whether it is possible to derive a general rule for determining if an arbitrary algorithm displays SOC.

Self-organized criticality in nature

The relevance of SOC to the dynamics of real sand has been questioned.

SOC has become established as a strong candidate for explaining a number of natural phenomena, including:

• The magnitude of earthquakes (Gutenberg–Richter law) and frequency of aftershocks (Omori law)
• Fluctuations in economic systems such as financial markets (references to SOC are common in econophysics)
• The evolution of proteins
• Forest fires
• Neuronal avalanches in the cortex
• Acoustic emission from fracturing materials

Despite the numerous applications of SOC to understanding natural phenomena, the universality of SOC theory has been questioned. For example, experiments with real piles of rice revealed their dynamics to be far more sensitive to parameters than originally predicted. Furthermore, it has been argued that 1/f scaling in EEG recordings are inconsistent with critical states, and whether SOC is a fundamental property of neural systems remains an open and controversial topic.

Self-organized criticality and optimization

It has been found that the avalanches from an SOC process make effective patterns in a random search for optimal solutions on graphs. An example of such an optimization problem is graph coloring. The SOC process apparently helps the optimization from getting stuck in a local optimum without the use of any annealing scheme, as suggested by previous work on extremal optimization.

See also

• 1/f noise
• Complex systems
• Critical brain hypothesis
• Critical exponents
• Detrended fluctuation analysis, a method to detect power-law scaling in time series.
• Dual-phase evolution, another process that contributes to self-organization in complex systems.
• Fractals
• Ilya Prigogine, a systems scientist who helped formalize dissipative system behavior in general terms.
• Power laws
• Red Queen hypothesis
• Scale invariance
• Self-organization
• Self-organized criticality control

Further reading

• "Self-organized criticality in living systems". Physics Letters A 203 (1): 29–32. 1995. doi:10.1016/0375-9601(95)00372-A. Bibcode: 1995PhLA..203...29A. 
• How Nature Works: The Science of Self-Organized Criticality. New York: Copernicus. 1996. ISBN 978-0-387-94791-4. 
• "Complexity, contingency, and criticality". Proceedings of the National Academy of Sciences of the United States of America 92 (15): 6689–6696. July 1995. doi:10.1073/pnas.92.15.6689. PMID 11607561. Bibcode: 1995PNAS...92.6689B. 
• "Punctuated equilibrium and criticality in a simple model of evolution". Physical Review Letters 71 (24): 4083–4086. December 1993. doi:10.1103/PhysRevLett.71.4083. PMID 10055149. Bibcode: 1993PhRvL..71.4083B. 
• "Self-organized criticality: An explanation of the 1/f noise". Physical Review Letters 59 (4): 381–384. July 1987. doi:10.1103/PhysRevLett.59.381. PMID 10035754. Bibcode: 1987PhRvL..59..381B. 
• "Self-organized criticality". Physical Review A 38 (1): 364–374. July 1988. doi:10.1103/PhysRevA.38.364. PMID 9900174. Bibcode: 1988PhRvA..38..364B.  Papercore summary.
• Ubiquity. London: Weidenfeld & Nicolson. 2000. ISBN 978-0-7538-1297-6. 
• Self-Organized Criticality. Cambridge: Cambridge University Press. 1998. ISBN 978-0-521-48371-1. 
• "A model of propagating brittle failure in heterogeneous media". Journal of Geophysical Research 91 (B10): 10412. 1986. doi:10.1029/JB091iB10p10412. Bibcode: 1986JGR....9110412K. 
• "Society as a Selforganized Critical System". Cybernetics and Human Knowing 16: 65–82. 2009. 
• "Networks as renormalized models for emergent behavior in physical systems". Complexity, Metastability and Nonextensivity. The Science and Culture Series – Physics. 2005. 363–374. doi:10.1142/9789812701558_0042. ISBN 978-981-256-525-9. Bibcode: 2005cmn..conf..363P. 
• Fractals and Chaos in Geology and Geophysics. Cambridge: Cambridge University Press. 1997. ISBN 978-0-521-56733-6. 
• "Self-organized criticality". Reports on Progress in Physics 62 (10): 1377–1429. 1999. doi:10.1088/0034-4885/62/10/201. Bibcode: 1999RPPh...62.1377T. 
• "Realization of {SOC} behavior in a dc glow discharge plasma". Physics Letters A 360 (6): 717–721. 2007. doi:10.1016/j.physleta.2006.09.005. Bibcode: 2007PhLA..360..717N. 
• Self-organized criticality on arxiv.org