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Self-organization in micron-sized Nb3O7(OH) cubes during a hydrothermal treatment at 200 °C. Initially amorphous cubes gradually transform into ordered 3D meshes of crystalline nanowires as summarized in the model below.[1]

Self-organization is a process where some form of overall crystallization, thermal convection of fluids, chemical oscillation, animal swarming, and neural networks.


  • Overview 1
    • Principles of self-organization 1.1
  • History of the idea 2
    • Developing views 2.1
  • Examples 3
    • Physics 3.1
    • Chemistry 3.2
    • Biology 3.3
    • Computer Science 3.4
      • Algorithms 3.4.1
      • Networks 3.4.2
    • Cybernetics 3.5
    • Human society 3.6
      • Economics 3.6.1
      • Collective intelligence 3.6.2
    • Psychology and education 3.7
      • Self-organised learning 3.7.1
    • Traffic flow 3.8
    • Methodology 3.9
      • In the emergence of language 3.9.1
      • In language acquisition 3.9.2
      • In articulatory phonology 3.9.3
      • In diachrony and synchrony 3.9.4
  • Criticism 4
  • See also 5
  • References 6
  • Further reading 7
  • External links 8
    • Dissertations and theses on self-organization 8.1


Self-organization is realized[2] in the emergence.[3] Properly defined, however, there may be instances of self-organization without emergence and emergence without self-organization.

Self-organization usually relies on three basic ingredients:[4]

  1. Strong dynamical non-linearity, often though not necessarily involving positive and negative feedback
  2. Balance of exploitation and exploration
  3. Multiple interactions

Principles of self-organization

The original principle of self-organization was formulated in 1947 by the cybernetician William Ross Ashby.[5][6] It states that any deterministic dynamic system will automatically evolve towards a state of equilibrium that can be described in terms of an attractor in a basin of surrounding states. Once there, the further evolution of the system is constrained to remain in the attractor. This constraint on the system as a whole implies a form of mutual dependency or coordination between its constituent components or "subsystems". In Ashby's terms, each subsystem has adapted to the environment formed by all other subsystems.

The principle of "order from noise" was formulated by the cybernetician Ilya Prigogine as "order through fluctuations"[8] or "order out of chaos".[9] It is applied in the method of simulated annealing that is used in problem solving and machine learning

History of the idea

The idea that the Descartes, in the fifth part of his Discourse on Method, where he presents it hypothetically. Descartes further elaborated on the idea at great length in his unpublished work The World.

The ancient atomists believed that a designing intelligence is unnecessary to effect natural order, arguing that given enough time and space and matter, organization is ultimately inevitable, although there is no preferred tendency for this to happen. What Descartes introduced was the idea that the ordinary laws of nature tend to produce organization (For related history, see Aram Vartanian, Diderot and Descartes).

The economic concept of the "invisible hand" due to Adam Smith can be understood as an attempt to describe the influence of the market as a spontaneous order on people's actions.

Beginning with the 18th century, natural scientists sought to understand the "universal laws of form" in order to explain the observed forms of living organisms. Because of its association with Lamarckism, their ideas fell into disrepute until the early 20th century, when pioneers such as D'Arcy Wentworth Thompson revived them. The modern understanding is that there are indeed universal laws, arising from fundamental physics and chemistry, that govern growth and form in biological systems.

Sadi Carnot and Rudolf Clausius discovered the Second Law of Thermodynamics in the 19th century. It states that total entropy, sometimes understood as disorder, will always increase over time in an isolated system. This means that a system cannot spontaneously increase its order, without an external relationship that decreases order elsewhere in the system (e.g. through consuming the low-entropy energy of a battery and diffusing high-entropy heat).

Originally, the term "self-organizing" was used by

The term "self-organizing" was introduced to contemporary science in 1947 by the psychiatrist and engineer

  • Gershenson, Carlos. (2007). "Design and control of Self-organizing Systems" (PhD thesis).
  • de Boer, Bart. (1999). Self-Organisation in Vowel Systems Vrije Universiteit Brussel AI-lab (PhD thesis).

Dissertations and theses on self-organization

  • Self-organization at Scholarpedia, curated by Hermann Haken.
  • Max Planck Institute for Dynamics and Self-Organization, Göttingen
  • PDF file on self-organized common law with references
  • sitePrincipia CyberneticaAn entry on self-organization at the
  • The Science of Self-organization and Adaptivity, a review paper by Francis Heylighen
  • Self-Organizing Systems (SOS) FAQThe by Chris Lucas, from the [news://comp.theory.self-org-sys USENET newsgroup comp.theory.self-org.sys]
  • Primordial Soup KitchenDavid Griffeath, (graphics, papers)
  • nlin.AO, nonlinear preprint archive, (electronic preprints in adaptation and self-organizing systems)
  • Structure and Dynamics of Organic Nanostructures
  • Metal organic coordination networks of oligopyridines and Cu on graphite
  • Selforganization in complex networks The Complex Systems Lab, Barcelona
  • Computational Mechanics Group at the Santa Fe Institute
  • "Organisation must grow" (1939) W. Ross Ashby journal page 759, from The W. Ross Ashby Digital Archive
  • Cosma Shalizi's notebook on self-organization from 2003-06-20, used under the GFDL with permission from author.
  • Connectivism:SelfOrganization
  • UCLA Human Complex Systems Program
  • "Interactions of Actors (IA), Theory and Some Applications" 1993 Gordon Pask's theory of learning, evolution and self-organization (in draft).
  • The Cybernetics Society
  • Scott Camazine's webpage on self-organization in biological systems
  • Mikhail Prokopenko's page on Information-driven Self-organisation (IDSO)
  • Lakeside Labs Self-Organizing Networked Systems A platform for science and technology, Klagenfurt, Austria.
  • Watch 32 discordant metronomes synch up all by themselves

External links

  • W. Ross Ashby (1966), Design for a Brain, Chapman & Hall, 2nd edition.
  • Amoroso, Richard (2005) The Fundamental Limit and Origin of Complexity in Biological Systems [2].
  • Per Bak (1996), How Nature Works: The Science of Self-Organized Criticality, Copernicus Books.
  • Philip Ball (1999), The Self-Made Tapestry: Pattern Formation in Nature, Oxford University Press.
  • autonomy: Brain of the Firm 2nd edition Wiley 1981 and Beyond Dispute Wiley 1994.
  • A. Bejan (2000), Shape and Structure, from Engineering to Nature, Cambridge University Press, Cambridge, UK, 324 pp.
  • Mark Buchanan (2002), Nexus: Small Worlds and the Groundbreaking Theory of Networks W. W. Norton & Company.
  • Scott Camazine, Jean-Louis Deneubourg, Nigel R. Franks, James Sneyd, Guy Theraulaz, & Eric Bonabeau (2001) Self-Organization in Biological Systems, Princeton Univ Press.
  • Falko Dressler (2007), Self-Organization in Sensor and Actor Networks, Wiley & Sons.
  • Manfred Eigen and Peter Schuster (1979), The Hypercycle: A principle of natural self-organization, Springer.
  • Myrna Estep (2003), A Theory of Immediate Awareness: Self-Organization and Adaptation in Natural Intelligence, Kluwer Academic Publishers.
  • Myrna L. Estep (2006), Self-Organizing Natural Intelligence: Issues of Knowing, Meaning, and Complexity, Springer-Verlag.
  • J. Doyne Farmer et al. (editors) (1986), "Evolution, Games, and Learning: Models for Adaptation in Machines and Nature", in: Physica D, Vol 22.
  • T. Christaller, P. Dittrich, J. T. Kim, and J. Ziegler, Advances in Artificial Life, 7th European Conference, ECAL 2003, Dortmund, Germany, pp. 606–614. LNAI 2801. Springer.
  • Hermann Haken (1983) Synergetics: An Introduction. Nonequilibrium Phase Transition and Self-Organization in Physics, Chemistry, and Biology, Third Revised and Enlarged Edition, Springer-Verlag.
  • F.A. Hayek Law, Legislation and Liberty, RKP, UK.
  • Henrik Jeldtoft Jensen (1998), Self-Organized Criticality: Emergent Complex Behaviour in Physical and Biological Systems, Cambridge Lecture Notes in Physics 10, Cambridge University Press.
  • Steven Berlin Johnson (2001), Emergence: The Connected Lives of Ants, Brains, Cities, and Software.
  • Stuart Kauffman (1995), At Home in the Universe, Oxford University Press.
  • Stuart Kauffman (1993), Origins of Order: Self-Organization and Selection in Evolution Oxford University Press.
  • J. A. Scott Kelso (1995), Dynamic Patterns: The self-organization of brain and behavior, The MIT Press, Cambridge, Massachusetts.
  • J. A. Scott Kelso & David A Engstrom (2006), "The Complementary Nature", The MIT Press, Cambridge, Massachusetts.
  • Alex Kentsis (2004), Self-organization of biological systems: Protein folding and supramolecular assembly, Ph.D. Thesis, New York University.
  • E.V.Krishnamurthy(2009)", Multiset of Agents in a Network for Simulation of Complex Systems", in "Recent advances in Nonlinear Dynamics and synchronization, ,(NDS-1) -Theory and applications, Springer Verlag, New York,2009. Eds. K.Kyamakya et al.
  • Paul Krugman (1996), The Self-Organizing Economy, Cambridge, Massachusetts, and Oxford: Blackwell Publishers.
  • Elizabeth McMillan (2004) "Complexity, Organizations and Change".
  • Marshall, A (2002) The Unity of Nature, Imperial College Press: London (esp. chapter 5)
  • Müller, J.-A., Lemke, F. (2000), Self-Organizing Data Mining.
  • Gregoire Nicolis and Ilya Prigogine (1977) Self-Organization in Non-Equilibrium Systems, Wiley.
  • Heinz Pagels (1988), The Dreams of Reason: The Computer and the Rise of the Sciences of Complexity, Simon & Schuster.
  • Gordon Pask (1961), The cybernetics of evolutionary processes and of self organizing systems, 3rd. International Congress on Cybernetics, Namur, Association Internationale de Cybernetique.
  • Christian Prehofer ea. (2005), "Self-Organization in Communication Networks: Principles and Design Paradigms", in: IEEE Communications Magazine, July 2005.
  • Mitchell Resnick (1994), Turtles, Termites and Traffic Jams: Explorations in Massively Parallel Microworlds, Complex Adaptive Systems series, MIT Press.
  • Lee Smolin (1997), The Life of the Cosmos Oxford University Press.
  • Ricard V. Solé and Brian C. Goodwin (2001), Signs of Life: How Complexity Pervades Biology, Basic Books.
  • Ricard V. Solé and Jordi Bascompte (2006), Selforganization in Complex Ecosystems, Princeton U. Press
  • Steven Strogatz (2004), Sync: The Emerging Science of Spontaneous Order, Theia.
  • D'Arcy Thompson (1917), On Growth and Form, Cambridge University Press, 1992 Dover Publications edition.
  • Tom De Wolf, Tom Holvoet (2005), Emergence Versus Self-Organisation: Different Concepts but Promising When Combined, In Engineering Self Organising Systems: Methodologies and Applications, Lecture Notes in Computer Science, volume 3464, pp 1–15.
  • K. Yee (2003), "Ownership and Trade from Evolutionary Games", International Review of Law and Economics, 23.2, 183–197.
  • Louise B. Young (2002), The Unfinished Universe
  • Mikhail Prokopenko (ed.) (2008), Advances in Applied Self-organizing Systems, Springer.
  • Alfred Hübler (2009), "Digital wires," Complexity, 14.5,7–9,
  • Rüdiger H. Jung (2010), Self-organization In: Helmut K. Anheier, Stefan Toepler, Regina List (editors): International Encyclopedia of Civil Society. Springer Science + Business Media LLC, New York 2010, ISBN 978-0-387-93996-4, p. 1364–1370.

Further reading

  1. ^ Betzler, S. B.; Wisnet, A.; Breitbach, B.; Mitterbauer, C.; Weickert, J.; Schmidt-Mende, L.; Scheu, C. (2014). "Template-free synthesis of novel, highly-ordered 3D hierarchical Nb3O7(OH) superstructures with semiconductive and photoactive properties". Journal of Materials Chemistry A 2 (30): 12005.  
  2. ^ Glansdorff, P., Prigogine, I. (1971). Thermodynamic Theory of Structure, Stability and Fluctuations, Wiley-Interscience, London. ISBN 0-471-30280-5
  3. ^ Bernard Feltz et al (2006). Self-organization and Emergence in Life Sciences. ISBN 9781402039164. p. 1.
  4. ^ Bonabeau, Eric; Dorigo, Marco and Theraulaz, Guy (1999). Swarm intelligence: from natural to artificial systems. ISBN 0195131592. pp. 9–11.
  5. ^ a b Ashby, W. R. (1947). "Principles of the Self-Organizing Dynamic System". The Journal of General Psychology 37 (2): 125–8.  
  6. ^ Ashby, W. R. (1962). "Principles of the self-organizing system", pp. 255–278 in Principles of Self-Organization. Heinz von Foerster and George W. Zopf, Jr. (eds.) U.S. Office of Naval Research.
  7. ^ Von Foerster, H. (1960). [Retrieved from "On self-organizing systems and their environments"], pp. 31–50 in Self-organizing systems. M.C. Yovits and S. Cameron (eds.), Pergamon Press, London
  8. ^ Nicolis, G. and Prigogine, I. (1977). Self-organization in nonequilibrium systems: From dissipative structures to order through fluctuations. Wiley, New York.
  9. ^ Prigogine, I. and Stengers, I. (1984). Order out of chaos: Man's new dialogue with nature. Bantam Books.
  10. ^ Asaro, P. (2007). "Heinz von Foerster and the Bio-Computing Movements of the 1960s" in Albert Müller and Karl H. Müller (eds.) An Unfinished Revolution? Heinz von Foerster and the Biological Computer Laboratory BCL 1958–1976. Vienna, Austria: Edition Echoraum.
  11. ^ As an indication of the increasing importance of this concept, when queried with the keyword self-organ*, Dissertation Abstracts finds nothing before 1954, and only four entries before 1970. There were 17 in the years 1971–1980; 126 in 1981–1990; and 593 in 1991–2000.
  12. ^ a b c d e Biel, R.; Mu-Jeong Kho (November 2009). "The Issue of Energy within a Dialectical Approach to the Regulationist Problematique" (PDF). Recherches & Régulation Working Papers, RR Série ID 2009-1, Association Recherche & Régulation ( 1–21. Retrieved 2013-11-09. 
  13. ^ Bejan, A.; Lorente, S. (2006). "Constructal theory of generation of configuration in nature and engineering". Journal of Applied Physics 100 (4): 041301.  
  14. ^ Henshaw, King; Zarnikau (2011). "System Energy Assessment (SEA), Defining a Standard Measure of EROI for Energy Businesses as Whole Systems". Sustainability 3 (10): 1908–1943.  
  15. ^ Henshaw, P. F. (2010). "Models Learning Change". Cosmos and History 6 (1). 
  16. ^ Georgiev, Georgi Yordanov (2012) "A quantitative measure, mechanism and attractor for self-organization in networked complex systems", pp. 90–95 in Lecture Notes in Computer Science (LNCS 7166), F. A. Kuipers and P. E. Heegaard (Eds.): IFIP International Federation for Information Processing, Proceedings of the Sixth International Workshop on Self-Organizing Systems (IWSOS 2012), Springer-Verlag (2012).
  17. ^ Georgiev, Georgi Yordanov; Georgiev, Iskren Yordanov (2002). "The least action and the metric of an organized system". Open Systems and Information Dynamics 9 (4): 371–380.  
  18. ^ Ansari M. H. (2004) Self-organized theory in quantum gravity.
  19. ^ Zeiger, H. J. and Kelley, P. L. (1991) "Lasers", pp. 614–619 in The Encyclopedia of Physics, Second Edition, edited by Lerner, R. and Trigg, G., VCH Publishers.
  20. ^ Strong, M. (2004). "Protein Nanomachines".  
  21. ^ Camazine, Deneubourg, Franks, Sneyd, Theraulaz, Bonabeau, Self-Organization in Biological Systems, Princeton University Press, 2003. ISBN 0-691-11624-5 --ISBN 0-691-01211-3 (pbk.) p. 8
  22. ^ Dennett, Daniel (1995), Darwin's Dangerous Idea, Penguin Books, London, ISBN 978-0-14-016734-4
  23. ^ Yang, X. S.; Deb, S.; Loomes, M.; Karamanoglu, M. (2013). "A framework for self-tuning optimization algorithm". Neural Computing and Applications 23 (7–8): 2051.  
  24. ^ X. S. Yang (2014) Nature-Inspired Optimization Algorithms, Elsevier.
  25. ^ Wiener, Norbert (1962) "The mathematics of self-organising systems". Recent developments in information and decision processes, Macmillan, N. Y. and Chapter X in Cybernetics, or control and communication in the animal and the machine, The MIT Press.
  26. ^ Cybernetics, or control and communication in the animal and the machine, The MIT Press, Cambridge, Massachusetts and Wiley, NY, 1948. 2nd Edition 1962 "Chapter X "Brain Waves and Self-Organizing Systems"pp 201–202.
  27. ^ Ashby, William Ross (1952) Design for a Brain, Chapter 5 Chapman & Hall
  28. ^ Ashby, William Ross (1956) An Introduction to Cybernetics, Part Two Chapman & Hall
  29. ^ Conant, R. C.; Ashby, W. R. (1970). "Every good regulator of a system must be a model of that system" (PDF). Int. J. Systems Sci. 1 (2): 89–97. 
  30. ^ Embodiments of Mind MIT Press (1965)"
  31. ^ von Foerster, Heinz; Pask, Gordon (1961). "A Predictive Model for Self-Organizing Systems, Part I". Cybernetica 3: 258–300. 
  32. ^ von Foerster, Heinz; Pask, Gordon (1961). "A Predictive Model for Self-Organizing Systems, Part II". Cybernetica 4: 20–55. 
  33. ^ "Brain of the Firm" Alan Lane (1972) see also Viable System Model also in "Beyond Dispute " Wiley Stafford Beer 1994 "Redundancy of Potential Command" pp. 157–158.
  34. ^ a b Pask, Gordon (1996). "Heinz von Foerster's Self-Organisation, the Progenitor of Conversation and Interaction Theories" (PDF). Systems Research 13 (3): 349–362. 
  35. ^ a b Pask, G. (1973). Conversation, Cognition and Learning. A Cybernetic Theory and Methodology. Elsevier
  36. ^ Green, N. (2001). "On Gordon Pask". Kybernetes 30 (5/6): 673.  
  37. ^ Pask, Gordon (1993) Interactions of Actors (IA), Theory and Some Applications.
  38. ^ Interactive models for self organization and biological systems Center for Models of Life, Niels Bohr Institute, Denmark
  39. ^ Luhmann, Niklas (1995) Social Systems. Stanford, California: Stanford University Press. ISBN 0804726256. p. 410.
  40. ^ Krugman, P. (1995) The Self Organizing Economy. Blackwell Publishers. ISBN 1557866996
  41. ^ Hayek, F. (1976) Law, Legislation and Liberty, Volume 2: The Mirage of Social Justice. University of Chicago Press.
  42. ^ Marshall, A. (2002) The Unity of Nature, Chapter 5. Imperial College Press. ISBN 1860943306.
  43. ^ Rogers.C. (1969). Freedom to Learn. Merrill
  44. ^ Feynman, R. P. (1987) Elementary Particles and the Laws of Physics. The Dyrac 1997 Memorial Lecture. Cambridge University Press. ISBN 9780521658621.
  45. ^ Illich. I. (1971) A Celebration of Awareness. Penguin Books.
  46. ^ Harri-Augstein E. S. (2000) The University of Learning in transformation
  47. ^ Schumacher, E. F. (1997) This I Believe and Other Essays (Resurgence Book). ISBN 1870098668.
  48. ^ Revans R. W. (1982) The Origins and Growth of Action Learning Chartwell-Bratt, Bromley
  49. ^ Thomas L.F. and Harri-Augstein S. (1993) "On Becoming a Learning Organisation" in Report of a 7 year Action Research Project with the Royal Mail Business. CSHL Monograph
  50. ^ Rogers C.R. (1971) On Becoming a Person. Constable, London
  51. ^ Prigogyne I. & Sengers I. (1985) Order out of Chaos Flamingo Paperbacks. London
  52. ^ Capra F (1989) Uncommon Wisdom Flamingo Paperbacks. London
  53. ^ Bohm D. (1994) Thought as a System. Routledge.
  54. ^ Harri-Augstein E. S. and Thomas L. F. (1991)Learning Conversations: The SOL way to personal and organizational growth. Routledge
  55. ^ Maslow, A. H. (1964). Religions, values, and peak-experiences, Columbus: Ohio State University Press.
  56. ^ Conversational Science Thomas L.F. and Harri-Augstein E.S. (1985)
  57. ^ Sole, M-J. (1992). "Phonetic and phonological processes: nasalization". Language & Speech 35: 29–43. 
  58. ^ Ladefoged, Peter (2003) "Commentary: some thoughts on syllables – an old-fashioned interlude", pp. 269–276 in Papers in laboratory Phonology VI. Local, John, Richard Ogden & Ros Temple (eds.). Cambridge University Press.
  59. ^ see papers in Phonetica 49, 1992, special issue on Articulatory Phonology
  60. ^ Ohala, John J. (1996). "Speech perception is hearing sounds, not tongues". Journal of the Acoustical Society of America 99 (3): 1718–1725.  
  61. ^ Lindblom, B. (1999). Emergent phonology (PDF). Proceedings of the Twenty-fifth Annual Meeting of the Berkeley Linguistics Society, University of California, Berkeley. 
  62. ^ Pagels, H. R. (January 1, 1985). "Is the irreversibility we see a fundamental property of nature?" (PDF). Physics Today: 97–99. 
  63. ^ Article 3. Whether God exists?


See also

("The body of the Article" consists of the quinque viae.)

[63] In

Heinz Pagels, in a balanced, but ultimately negative 1985 book review of Ilya Prigogine and Isabelle Stengers' Order Out of Chaos in Physics Today, appeals to authority:[62]


Several mathematical models of language change rely on self-organizing or dynamical systems. Abrams and Strogatz (2003) produced a model of language change that focused on "language death" – the process by which a speech community merges into the surrounding speech communities. Nakamura et al. (2008) proposed a variant of this model that incorporates spatial dynamics into language contact transactions in order to describe the emergence of creoles. Both of these models proceed from the assumption that language change, like any self-organizing system, is a large-scale act or entity (in this case the creation or death of a language, or changes in its boundaries) that emerges from many actions on a micro-level. The microlevel in this example is the everyday production and comprehension of language by speakers in areas of language contact.

In diachrony and synchrony

Articulatory phonology takes the approach that speech production consists of a coordinated series of gestures, called 'constellations,' which are themselves dynamical systems. In this theory, linguistic contrast comes from the distinction between such gestural units, which can be described on a low-dimensional level in the abstract. However, these structures are necessarily context-dependent in real-time production. Thus the context-dependence emerges naturally from the dynamical systems themselves. This statement is controversial, however, as it suggests a universal phonetics which is not evident across languages.[57] Cross-linguistic patterns show that what can be treated as the same gestural units produce different contextualised patterns in different languages.[58] Articulatory Phonology fails to attend to the acoustic output of the gestures themselves (meaning that many typological patterns remain unexplained).[59] Freedom among listeners in the weighting of perceptual cues in the acoustic signal has a more fundamental role to play in the emergence of structure.[60] The realization of the perceptual contrasts by means of articulatory movements means that articulatory considerations do play a role,[61] but these are purely secondary.

In articulatory phonology

Within a species' joint attention,' human children have the scaffolding they need to learn the language of those around them.

In language acquisition

The emergence of language in the human species has been described in a game-theoretic framework based on a model of senders and receivers of information. The evolution of certain properties of language such as inference follow from this sort of framework (with the parameters stating that information transmitted can be partial or redundant, and the underlying assumption that the sender and receiver each want to take the action in his/her best interest). Likewise, models have shown that compositionality, a central component of human language, emerges dynamically during linguistic evolution, and need not be introduced by biological evolution. Tomasello (1999) argues that through one evolutionary step, the ability to sustain culture, the groundwork for the evolution of human language was laid. The ability to ratchet cultural advances cumulatively allowed for the complex development of human cognition unseen in other animals.

In the emergence of language

As it were, the construction of operational models to test proposed hypotheses in linguistics is gaining much contemporary attention. An operational model is one which defines the set of its assumptions explicitly and above all shows how to calculate their consequences, that is, to prove that they lead to a certain set of conclusions.

Building mathematical models in the context of research into language origins and the evolution of languages is enjoying growing popularity in the scientific community, because it is a crucial tool for studying the phenomena of language in relation to the complex interactions of its components. These systems are put to two main types of use: 1) they serve to evaluate the internal coherence of verbally expressed theories already proposed by clarifying all their hypotheses and verifying that they do indeed lead to the proposed conclusions ; 2) they serve to explore and generate new theories, which themselves often appear when one simply tries to build an artificial system reproducing the verbal behavior of humans.

In many complex systems in nature, there are global phenomena that are the irreducible result of local interactions between components whose individual study would not allow us to see the global properties of the whole combined system. Thus, a growing number of researchers think that many properties of language are not directly encoded by any of the components involved, but are the self-organized outcomes of the interactions of the components.


The self-organizing behaviour of drivers in Boris Kerner's three-phase traffic theory.

Traffic flow

  1. Cause and Effect (requires "other things being equal")
  2. Cybernetics[35] (incorporates item 1 in this list) with greater complexity, providing internal feedback and feed-forward controls: but still implying a sealed boundary. (i.e. other things being equal)
  3. Systems Theory[53] (incorporates item 2 in this list, and opens the boundaries)
  4. Self-organized System (incorporates item 3 in this list) and attributes this property to the interaction, patterning and coordination among the sub-systems of the system in question; in response to flow across its boundaries
  5. Self-Organised Learning (SOL)[54] (incorporates item 4 in this list) but also requires that the parts each systematically respond, change and develop in the light of their experience, whilst self-organizing in the developing experiential interest of the whole).
    SOL not only involves self-organization of the first order, i.e. what is mostly experienced as learning from experience without much conscious awareness of the process. At a second level of SOL consciousness enables us, (possibly uniquely among living beings) to reflect upon and thus self-organise the very process of self-organisation itself, (See 'Cybernetic algorithm' figure). It also enables organisations small and large to self-organise themselves, (see 'System algorithm' figure).
    Once this approach to human learning is acknowledged, then we can re-set science into its place within the total human mind-pool. A mind-pool of human know-how and feel-how as an ever expanding and hopefully self-organizing resource.
  6. Learning Conversation (incorporates item 5 in this list) and yet is at the same time its major tool. The Learning Conversation is a two-way process between SOLers, even within one person (conversing with oneself). Whilst not necessarily requiring language i.e. dialogue; it does require that the each participant really attempts to represent their meaning to the other(s), and that they all attempt to create personally significant, relevant and viable meaning in themselves in response to the others representations. So art, drama, music, computer programs, maths problems, ???, etc., can all create different, if limited, forms of Learning Conversation which really only become fully functional when at least two humans really attempt to fully communicate, and effectively share their understanding. That is achieve shared meaning in an event that approximates to what Maslow called a creative encounter[55]
  7. Conversational Science[56] (will require item 6 in this list, the main method of SOL) among all seekers after significant, relevant and viable shared meaning. Science and many other human activities still need major paradigm shifts if we are to achieve Self-Organised Living. It also requires equal stakeholder-ship for each converser. Thus SOL can be seen as necessary but not sufficient for science to contribute positively to the benefit of the society, within which it may have only spasmodically been conversing successfully (SOL wise). Until, perhaps, both science and society as a whole will become Self-Organised Learners (SOLers) continually learning from their own shared experience and using what they learn in the shared interest of all concerned.

Since SOL is as yet only very superficially recognised within psychology and education, it is useful to place it more firmly within the human public mind-pool[52] of achievement, knowledge, experience and understanding. SOL can also be placed within a hierarchy of scientific explanatory concepts, for example:

But, this SOL way of understanding the learning process need not be restricted by either consciousness or language.[51] Nor is it restricted to humans, since analogous directional self-organizing (learning?) processes are reported variously within the life sciences and even within the less-living sciences, for example, of physics and chemistry: (as is clearly articulated in other sections of this 'Self-organization' Section).

Whilst internal life may cease to expand, the external environment does not. If a learner allows themselves to become progressively more other-organised, they become less able to recognise and respond to varying needs for change. Unfortunately this is often the current reported experience of many during, and hence after their parenting, schooling and/or higher education.

SOL needs to be tested, and intermittently revised, through the ongoing personal experience[50] of the learner(s) themselves in their ever-expanding outer and inner lives.

As many young children, pupils, students and lifelong learners eventually become ruefully aware, this ‘testing out of what I have learned’ needs to be carried out in each learner(s) whole process of living, and so it extends well beyond the confines of specific learning environments (home, school, university, etc.), and eventually beyond the reaches of the controllers of these environments (parents, teachers, employers, etc.)[49]

Since human learning may be achieved by one person,[47] or groups of learners working together;[48] SOL is not only a more rewarding and effective way of living one's personal life; it is also applicable in any group of people living, playing and/or working together.

Systems algorithm
Cybernetic algorithm

This more democratic 'bottom up' approach to learning is to be frequently tested experientially[46] by the learner(s) as being more "meaningful, constructive and creatively effective for me or us."

Enabling others to "learn how to learn"[43] is usually misconstrued as instructing them[44] how to successfully submit to being taught. Whilst fully accepting that we can always learn from others, particularly those with more and/or different experience than ourselves; self-organised learning (SOL) repudiates any idea[45] that this reduces to accepting that "the expert knows best" or that there is ever "the one best method." It offers an alternative definition of learning as "the construction of personally significant, relevant and viable meaning."

Self-organised learning

Psychology and education

Gaia philosophy, deep ecology, ecology movement and Green movement for similar self-organizing ideals. (The connections between self-organisation and Gaia theory and the environmental movement are explored in A. Marshall, 2002, The Unity of Nature, Imperial College Press: London).

Non-thermodynamic concepts of entropy and self-organization have been explored by many theorists. Cliff Joslyn and colleagues and their so-called "global brain" projects. Marvin Minsky's "Society of Mind" and the no-central editor in charge policy of the open sourced internet encyclopedia, called WorldHeritage, are examples of applications of these principles – see collective intelligence.

Visualization of links between pages on a wiki. This is an example of collective intelligence through collaborative editing.

Collective intelligence

[42][12] In economics, a


Self-organization in human and computer networks can give rise to a decentralized, distributed, self-healing system, protecting the security of the actors in the network by limiting the scope of knowledge of the entire system held by each individual actor. The Tor. In each case, the network as a whole exhibits distinctive synergistic behavior through the combination of the behaviors of individual actors in the network. Usually the growth of such networks is fueled by an ideology or sociological force that is adhered to or shared by all participants in the network.[12]

In social theory the concept of self-referentiality has been introduced as a sociological application of self-organization theory by Niklas Luhmann (1984). For Luhmann the elements of a social system are self-producing communications, i.e. a communication produces further communications and hence a social system can reproduce itself as long as there is dynamic communication. For Luhmann human beings are sensors in the environment of the system. Luhmann developed an evolutionary theory of Society and its subsytems, using functional analyses and systems theory.[39]

The self-organizing behaviour of social animals and the self-organization of simple mathematical structures both suggest that self-organization should be expected in human spontaneous order theory.

Social self-organization in international drug routes

Human society

Pask's Interactions of Actors "hard carapace" model is reflected in some of the ideas of emergence and coherence. It requires a knot emergence topology that produces radiation during interaction with a unit cell that has a prismatic tensegrity structure. Laughlin's contribution to emergence reflects some of these constraints.

In the 1990s closed or homeostatic processes that produce enduring and coherent products (where spins have a fixed average phase relationship and also in the sense of Rescher Coherence Theory of Truth with the proviso that the sets and their members exert repulsive forces at their boundaries) through interactions: evolving, learning and adapting.

In the 1970s Viable System Model to management. It consists of five parts: the monitoring of performance of the survival processes (1), their management by recursive application of regulation (2), homeostatic operational control (3) and development (4) which produce maintenance of identity (5) under environmental perturbation. Focus is prioritized by an alerting "algedonic loop" feedback: a sensitivity to both pain and pleasure produced from under-performance or over-performance relative to a standard capability.[33]

Heinz von Foerster proposed Redundancy, R = 1 − H/Hmax, where H is entropy.[31][32] In essence this states that unused potential communication bandwidth is a measure of self-organization.

Warren McCulloch proposed "Redundancy of Potential Command"[30] as characteristic of the organization of the brain and human nervous system and the necessary condition for self-organization.

By contrast, the four concurrently connected galvanometers of endurance and stability (e.g. Nyquist stability criterion).

. universal assembly as a key step in nano and self-replication sees Drexler [26]".Cybernetics or the "attraction of frequencies", as he called it, is discussed in the 2nd edition of his "phase locking The importance of [25]


In many natural systems, self-organization results from repeated phase shifts in their underlying network of connections. Such phase shifts alter the balance between internal processes (e.g. selection and variation). They give rise to the phenomenon of dual-phase evolution.

Only certain kinds of networks are self-organizing. The best known examples are small-world networks and scale-free networks. These emerge from bottom-up interactions, and appear to be limitless in size. In contrast, there are top-down hierarchical networks, which are not self-organizing. These are typical of organizations, and have severe size limits.

Self-organization is an important component for a successful ability to establish networking whenever needed. Such mechanisms are also referred to as Self-organizing networks. Intensified work in the latter half of the first decade of the 21st century was mainly due to interest from the wireless communications industry. It is driven by the plug and play paradigm, and that wireless networks need to be relatively simpler to manage than they used to be.


Many optimization algorithms can be considered as a self-organization system because the aim of the optimization is to find the optimal solution to a problem. If the solution is considered as a state of the iterative system, the optimal solution is essentially the selected, converged state or structure of the system, driven by the algorithm based on the system landscape.[23][24] In fact, one can view all optimization algorithms as a self-organization system.


As mentioned above, phenomena from multi-agent systems, understanding how to engineer systems that are capable of presenting self-organized behavior is a very active research area.

Gosper's Glider Gun creating "gliders" in the cellular automaton Conway's Game of Life.[22]

Computer Science

  1. spontaneous folding of proteins and other biomacromolecules
  2. formation of lipid bilayer membranes
  3. homeostasis (the self-maintaining nature of systems from the cell to the whole organism)
  4. develops and grows. See also embryology.
  5. the coordination of human movement, e.g. seminal studies of bimanual coordination by Kelso
  6. the creation of structures by social animals, such as social insects (bees, ants, termites), and many mammals
  7. flocking behaviour (such as the formation of flocks by birds, schools of fish, etc.)
  8. the hypercycles and autocatalytic networks
  9. the organization of Earth's biosphere in a way that is broadly conducive to life (according to the controversial Gaia hypothesis)

The following is an incomplete list of the diverse phenomena which have been described as self-organizing in biology.

According to Scott Camazine.. [et al.]:

Birds flocking, an example of self-organization in biology


  1. molecular self-assembly
  2. reaction-diffusion systems and oscillating chemical reactions
  3. autocatalytic networks (see: autocatalytic set)
  4. liquid crystals
  5. colloidal crystals
  6. self-assembled monolayers
  7. micelles
  8. microphase separation of block copolymers
  9. Langmuir-Blodgett films

Self-organization in chemistry includes:

The DNA structure at left (schematic shown) will self-assemble into the structure visualized by atomic force microscopy at right. Image from Strong.[20]


  • A [19]
  • In spin foam system and loop quantum gravity that was proposed by Lee Smolin. The main idea is that the evolution of space in time should be robust in general. Any fine-tuning of cosmological parameters weaken the independency of the fundamental theory. Philosophically, it can be assumed that in the early time, there has not been any agent to tune the cosmological parameters. Smolin and his colleagues in a series of works show that, based on the loop quantization of spacetime, in the very early time, a simple evolutionary model (similar to the sand pile model) behaves as a power law distribution on both the size and area of avalanche.
    • Although, this model, which is restricted only on the frozen spin networks, exhibits a non-stationary expansion of the universe. However, it is the first serious attempt toward the final ambitious goal of determining the cosmic expansion and inflation based on a self-organized criticality theory in which the parameters are not tuned, but instead are determined from within the complex system.[18]
  • In stick-slip patterns to in-situ formed tribofilms and surface roughness adjustment of two materials in contact.
  • self-organizing dynamical systems: complex systems made up of small, simple units connected to each other usually exhibit self-organization
    • Self-organized criticality (SOC)

There are several broad classes of physical processes that can be described as self-organization. Such examples from physics include:

Convection cells in a gravity field


Similarly, when ideas about self-organization originate in, say, biology or social science, the farther one tries to take the concept into chemistry, physics or mathematics, the more resistance is encountered, usually on the grounds that it implies direction in fundamental physical processes. However the tendency of hot bodies to get cold (see Thermodynamics) and by Le Chatelier's Principle—the statistical mechanics extension of Newton's Third Law—to oppose this tendency should be noted.

The farther a phenomenon is removed from physics, the more controversial the idea of self-organization as understood by physicists becomes. Also, even when self-organization is clearly present, attempts at explaining it through physics or statistics are usually criticized as reductionistic.

The following list summarizes and classifies the instances of self-organization found in different disciplines. As the list grows, it becomes increasingly difficult to determine whether these phenomena are all fundamentally the same process, or the same label applied to several different processes. Self-organization, despite its intuitive simplicity as a concept, has proven notoriously difficult to define and pin down formally or mathematically, and it is entirely possible that any precise definition might not include all the phenomena to which the label has been applied.


[17][16][12] Other views of self-organization in physical systems interpret it as a strictly accumulative construction process, commonly displaying an

Developing views

[12] Self-organization as a word and concept was used by those associated with

(1961). Cybernetics: or Control and Communication in the Animal and the Machine also took up the idea in the second edition of his Norbert Wiener [10]

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