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Self-organization

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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 order arises from local interactions between parts of an initially disordered system. The process is spontaneous, not needing control by any external agent. It is often triggered by 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, social, and cognitive systems. Examples can be found in crystallization, thermal convection of fluids, chemical oscillation, animal swarming, and artificial and biological neural networks.

Overview

Self-organization is realized[2] in the physics of non-equilibrium processes, and in chemical reactions, where it is often described as self-assembly. The concept of self-organization has proven useful in the description of biological systems,[3] from the subcellular to the ecosystem level.[4] 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.[5] Self-organization is not to be confused with the related concept of emergence.[6]

Self-organization relies on three basic ingredients:[7]

  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 cybernetician William Ross Ashby formulated the original principle of self-organization in 1947.[8][9] 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.[citation needed] 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 cybernetician Heinz von Foerster formulated the principle of "order from noise" in 1960.[10] It notes that self-organization is facilitated by random perturbations ("noise") that let the system explore a variety of states in its state space. This increases the chance that the system would arrive into the basin of a "strong" or "deep" attractor, from which it would then quickly enter the attractor itself. The thermodynamicist Ilya Prigogine formulated a similar principle as "order through fluctuations"[11] or "order out of chaos".[12] It is applied in the method of simulated annealing for problem solving and machine learning.[13]

History

The idea that the dynamics of a system can lead to an increase in its organization has a long history. The ancient atomists such as Lucretius believed that a designing intelligence is unnecessary to create order in nature, arguing that given enough time and space and matter, order emerges by itself.[14]

The philosopher René Descartes presents it hypothetically in the fifth part of his Discourse on Method. He elaborated on the idea in his unpublished work The World.[a]

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 D'Arcy Wentworth Thompson attempted to revive them.

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).

The term "self-organizing" was used by Immanuel Kant in his Critique of Judgment, where he argued that teleology is a meaningful concept only if there exists such an entity whose parts or "organs" are simultaneously ends and means. Such a system of organs must be able to behave as if it has a mind of its own, that is, it is capable of governing itself.

In such a natural product as this every part is thought as owing its presence to the agency of all the remaining parts, and also as existing for the sake of the others and of the whole, that is as an instrument, or organ... The part must be an organ producing the other parts—each, consequently, reciprocally producing the others... Only under these conditions and upon these terms can such a product be an organized and self-organized being, and, as such, be called a physical end.

The term "self-organizing" was introduced to contemporary science in 1947 by the psychiatrist and engineer W. Ross Ashby.[8] It was taken up by the cyberneticians Heinz von Foerster, Gordon Pask, Stafford Beer, and von Foerster organized a conference on "The Principles of Self-Organization" at the University of Illinois' Allerton Park in June, 1960 which led to a series of conferences on Self-Organizing Systems.[15] Norbert Wiener took up the idea in the second edition of his Cybernetics: or Control and Communication in the Animal and the Machine (1961).

Self-organization as a word and concept was used by those associated with general systems theory in the 1960s, but did not become commonplace in the scientific literature until its adoption by physicists and researchers in the field of complex systems in the 1970s and 1980s.[16] After Ilya Prigogine's 1977 Nobel Prize, the thermodynamic concept of self-organization received public attention, and scientific researchers started to migrate from the cybernetic view to the thermodynamic view.[17]

Examples

Physics

Convection cells in a gravity field

Examples from physics include:[citation needed]

Chemistry

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

Self-organization in chemistry includes molecular self-assembly, reaction-diffusion systems and oscillating chemical reactions, autocatalytic networks, liquid crystals, grid complexes, colloidal crystals, self-assembled monolayers, micelles, microphase separation of block copolymers, and Langmuir-Blodgett films.

Biology

Birds flocking, an example of self-organization in biology
Main article: Biological organisation

Self-organization in biology[3][21] can be observed in spontaneous folding of proteins and other biomacromolecules, formation of lipid bilayer membranes, homeostasis, pattern formation and morphogenesis in developmental biology, the coordination of human movement, social behaviour in insects (bees, ants, termites), and mammals, flocking behaviour in birds and fish, the origin of life itself.

Computer science

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

Phenomena from mathematics and computer science such as cellular automata, random graphs, and some instances of evolutionary computation and artificial life exhibit features of self-organization. In swarm robotics, self-organization is used to produce emergent behavior. In particular the theory of random graphs has been used as a justification for self-organization as a general principle of complex systems.

In the field of multi-agent systems, understanding how to engineer systems that are capable of presenting self-organized behavior is an active research area.[23]

Optimization algorithms can be considered self-organizing because they aim to find the optimal solution to a problem. If the solution is considered as a state of the iterative system, the optimal solution is the selected, converged structure of the system.[24][25]

Self-organizing networks include small-world networks[26] and scale-free networks. These emerge from bottom-up interactions, unlike top-down hierarchical networks within organizations, which are not self-organizing.[27]

Cloud computing systems have been argued to be inherently self-organising[28] and, while they exhibit autonomic features, are not self-managing as they do not have reducing complexity as a goal. Others argue that cloud computing represents a complex system and therefore self-organisation is an appropriate technique to address this complexity.[29][30] The European Union, through Horizon 2020, has funded CloudLightning, a Research Innovation Action. This project seeks to build a next generation cloud architecture based on the principles of decentralisation, self-organisation and self-management.

Cybernetics

Norbert Wiener regarded the automatic serial identification of a black box and its subsequent reproduction as self-organization.[31] The importance of phase locking or the "attraction of frequencies", as he called it, is discussed in the 2nd edition of his "Cybernetics".[32] K. Eric Drexler sees self-replication as a key step in nano and universal assembly.

By contrast, the four concurrently connected galvanometers of W. Ross Ashby's Homeostat hunt, when perturbed, to converge on one of many possible stable states.[33] Ashby used his state counting measure of variety[34] to describe stable states and produced the "Good Regulator"[35] theorem which requires internal models for self-organized endurance and stability (e.g. Nyquist stability criterion).

Warren McCulloch proposed "Redundancy of Potential Command"[36] as characteristic of the organization of the brain and human nervous system and the necessary condition for self-organization.

Heinz von Foerster proposed Redundancy, R = 1 − H/Hmax, where H is entropy.[37][38] In essence this states that unused potential communication bandwidth is a measure of self-organization.

In the 1970s Stafford Beer considered this condition as necessary for autonomy which identifies self-organization in persisting and living systems. Using Variety analyses he applied his neurophysiologically derived recursive 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.[39]

In the 1990s Gordon Pask pointed out von Foerster's H and Hmax were not independent and interacted via countably infinite recursive concurrent spin processes[40] (he favoured the Bohm interpretation) which he called concepts (liberally defined in any medium, "productive and, incidentally reproductive"). His strict definition of concept "a procedure to bring about a relation"[41] permitted his theorem "Like concepts repel, unlike concepts attract"[42] to state a general spin-based principle of self-organization. His edict, an exclusion principle, "There are No Doppelgangers"[43][40] means no two concepts can be the same (all interactions occur with different perspectives making time incommensurable for actors).

Human society

Social self-organization in international drug routes

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 society. Tell-tale signs of self-organization are usually statistical properties shared with self-organizing physical systems. Examples such as critical mass, herd behaviour, groupthink and others, abound in sociology, economics, behavioral finance and anthropology.[44]

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 subsystems, using functional analyses and systems theory.[45]

Economics

In economics, a market economy is sometimes said to be self-organizing. Paul Krugman has written on the role that market self-organization plays in the business cycle in his book "The Self Organizing Economy".[46] Friedrich Hayek coined the term catallaxy[47] to describe a "self-organizing system of voluntary co-operation", in regards to the spontaneous order of the free market economy. Neo-classical economists hold that imposing central planning usually makes the self-organized economic system less efficient. On the other end of the spectrum, economists consider that market failures are so significant that self-organization produces bad results and that the state should direct production and pricing. Most economists adopt an intermediate position and recommend a mixture of market economy and command economy characteristics (sometimes called a mixed economy). When applied to economics, the concept of self-organization can quickly become ideologically imbued.[17][48]

Collective intelligence

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

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 Wikipedia, are examples of applications of these principles – see collective intelligence.

Donella Meadows, who codified twelve leverage points that a self-organizing system could exploit to organize itself, saw human creativity as part of a general process of adapting human lifeways to the planet and taking humans out of conflict with natural processes. See 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 the book The Unity of Nature by Alan Marshall).

In learning

Enabling others to "learn how to learn"[49] is often taken to mean instructing them[50] how to submit to being taught. Self-organised learning (SOL)[51] denies that "the expert knows best" or that there is ever "the one best method", insisting instead on "the construction of personally significant, relevant and viable meaning"[52] to be tested experientially by the learner.[53] This may be collaborative, and more rewarding personally.[54][55] It is seen as a lifelong process, not limited to specific learning environments (home, school, university) or under the control of authorities such as parents and professors.[56] It needs to be tested, and intermittently revised, through the personal experience of the learner.[57] It need not be restricted by either consciousness or language.[58] Fritjof Capra argued that it is poorly recognised within psychology and education.[59] It may be related to cybernetics as it involves a negative feedback control loop,[41] or to systems theory.[60] It can be conducted as a learning conversation or dialogue between learners or within one person.[61][62]

Traffic flow

The self-organizing behavior of drivers in traffic flow determines almost all traffic spatiotemporal phenomena observed in real traffic data, such as traffic breakdown at a highway bottleneck, highway capacity, the emergence of moving traffic jams, etc. Self-organization in traffic flow is an extremely complex spatiotemporal dynamic process. For this reason, only in 1996–2002 did spatiotemporal self-organization effects in traffic become understood in real measured traffic data and explained by Boris Kerner's three-phase traffic theory.[citation needed]

In linguistics

In the emergence of language

Investigators have examined the emergence of language in the human species in a game-theoretic framework[63] 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 their own 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 one evolutionary step, the ability to sustain culture, laid the groundwork for the evolution of human language.[64][need quotation to verify]

In language acquisition

Within a species' ontogeny, the acquisition of language has also been shown to self-organize. Through the ability to see others as intentional agents (theory of mind), and actions such as 'joint attention,' human children have the scaffolding they need to learn the language of those around them.

In articulatory phonology

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.[65]

In diachrony and synchrony

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.

Criticism

Heinz Pagels, in a 1985 review of Ilya Prigogine and Isabelle Stengers's book Order Out of Chaos in Physics Today, appeals to authority:[66]

Most scientists would agree with the critical view expressed in Problems of Biological Physics (Springer Verlag, 1981) by the biophysicist L. A. Blumenfeld, when he wrote: "The meaningful macroscopic ordering of biological structure does not arise due to the increase of certain parameters or a system above their critical values. These structures are built according to program-like complicated architectural structures, the meaningful information created during many billions of years of chemical and biological evolution being used." Life is a consequence of microscopic, not macroscopic, organization.

In short, they [Prigogine and Stengers] maintain that time irreversibility is not derived from a time-independent microworld, but is itself fundamental. The virtue of their idea is that it resolves what they perceive as a "clash of doctrines" about the nature of time in physics. Most physicists would agree that there is neither empirical evidence to support their view, nor is there a mathematical necessity for it. There is no "clash of doctrines." Only Prigogine and a few colleagues hold to these speculations which, in spite of their efforts, continue to live in the twilight zone of scientific credibility.

In theology, Thomas Aquinas (1225–1274) in his Summa Theologica assumes a teleological created universe in rejecting the idea that something can be a self-sufficient cause of its own organization:[67]

Since nature works for a determinate end under the direction of a higher agent, whatever is done by nature must needs be traced back to God, as to its first cause. So also whatever is done voluntarily must also be traced back to some higher cause other than human reason or will, since these can change or fail; for all things that are changeable and capable of defect must be traced back to an immovable and self-necessary first principle, as was shown in the body of the Article.

See also

Notes

  1. ^ For related history, see Aram Vartanian, Diderot and Descartes.

References

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  2. ^ Glansdorff, P., Prigogine, I. (1971). Thermodynamic Theory of Structure, Stability and Fluctuations, Wiley-Interscience, London. ISBN 0-471-30280-5
  3. ^ a b Witzany G (2014). Biological Self-Organization. International Journal of Signs and Semiotic Systems 3: 1-11.
  4. ^ Compare: Camazine, Scott (2003). Self-organization in Biological Systems. Princeton studies in complexity (reprint ed.). Princeton University Press. ISBN 9780691116242. Retrieved 2016-04-05.
  5. ^ Ilachinski, Andrew (2001). Cellular Automata: A Discrete Universe. World Scientific. p. 247. ISBN 9789812381835. We have already seen ample evidence for what is arguably the single most impressive general property of CA, namely their capacity for self-organization
  6. ^ Bernard Feltz et al (2006). Self-organization and Emergence in Life Sciences. ISBN 9781402039164. p. 1.
  7. ^ Bonabeau, Eric; Dorigo, Marco and Theraulaz, Guy (1999). Swarm intelligence: from natural to artificial systems. ISBN 0195131592. pp. 9–11.
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  16. ^ 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.
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  18. ^ 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.
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  24. ^ 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. doi:10.1007/s00521-013-1498-4.
  25. ^ X. S. Yang (2014) Nature-Inspired Optimization Algorithms, Elsevier.
  26. ^ Watts, Duncan J.; Strogatz, Steven H. (June 1998). "Collective dynamics of 'small-world' networks". Nature. 393: 440–442. doi:10.1038/30918.
  27. ^ Clauset, Aaron; Cosma Rohilla Shalizi; M. E. J Newman (2007-06-07). "Power-law distributions in empirical data". SIAM Review. arXiv:0706.1062. Bibcode:2009SIAMR..51..661C. doi:10.1137/070710111.
  28. ^ Zhang, Q., Cheng, L., and Boutaba, R. (2010). "Cloud computing: state-of-the-art and research challenges" (PDF). Journal of Internet Services and Applications. 1 (1): 7. doi:10.1007/s13174-010-0007-6.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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  30. ^ Lynn; et al. (2016). "CLOUDLIGHTNING: A Framework for a Self-organising and Self-managing Heterogeneous Cloud". Proceedings of the 6th International Conference on Cloud Computing and Services Science: 333. doi:10.5220/0005921503330338. ISBN 978-989-758-182-3. {{cite journal}}: Explicit use of et al. in: |last= (help)
  31. ^ 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.
  32. ^ 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.
  33. ^ Ashby, William Ross (1952) Design for a Brain, Chapter 5 Chapman & Hall
  34. ^ Ashby, William Ross (1956) An Introduction to Cybernetics, Part Two Chapman & Hall
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  36. ^ Embodiments of Mind MIT Press (1965)"
  37. ^ von Foerster, Heinz; Pask, Gordon (1961). "A Predictive Model for Self-Organizing Systems, Part I". Cybernetica. 3: 258–300.
  38. ^ von Foerster, Heinz; Pask, Gordon (1961). "A Predictive Model for Self-Organizing Systems, Part II". Cybernetica. 4: 20–55.
  39. ^ "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.
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  43. ^ Pask, Gordon (1993) Interactions of Actors (IA), Theory and Some Applications.
  44. ^ Interactive models for self organization and biological systems Center for Models of Life, Niels Bohr Institute, Denmark
  45. ^ Luhmann, Niklas (1995) Social Systems. Stanford, California: Stanford University Press. ISBN 0804726256. p. 410.
  46. ^ Krugman, P. (1995) The Self Organizing Economy. Blackwell Publishers. ISBN 1557866996
  47. ^ Hayek, F. (1976) Law, Legislation and Liberty, Volume 2: The Mirage of Social Justice. University of Chicago Press.
  48. ^ Marshall, A. (2002) The Unity of Nature, Chapter 5. Imperial College Press. ISBN 1860943306.
  49. ^ Rogers.C. (1969). Freedom to Learn. Merrill
  50. ^ Feynman, R. P. (1987) Elementary Particles and the Laws of Physics. The Dyrac 1997 Memorial Lecture. Cambridge University Press. ISBN 9780521658621.
  51. ^ Harri-Augstein E. S. and Thomas L. F. (1991)Learning Conversations: The SOL way to personal and organizational growth. Routledge
  52. ^ Illich. I. (1971) A Celebration of Awareness. Penguin Books.
  53. ^ Harri-Augstein E. S. (2000) The University of Learning in transformation
  54. ^ Schumacher, E. F. (1997) This I Believe and Other Essays (Resurgence Book). ISBN 1870098668.
  55. ^ Revans R. W. (1982) The Origins and Growth of Action Learning Chartwell-Bratt, Bromley
  56. ^ 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
  57. ^ Rogers C.R. (1971) On Becoming a Person. Constable, London
  58. ^ Prigogyne I. & Sengers I. (1985) Order out of Chaos Flamingo Paperbacks. London
  59. ^ Capra F (1989) Uncommon Wisdom Flamingo Paperbacks. London
  60. ^ Bohm D. (1994) Thought as a System. Routledge.
  61. ^ Maslow, A. H. (1964). Religions, values, and peak-experiences, Columbus: Ohio State University Press.
  62. ^ Conversational Science Thomas L.F. and Harri-Augstein E.S. (1985)
  63. ^ Compare: Jaeger, Herbert (2009). "What Can Mathematical, Computational, and Robotic Models Rell Us about the Origins of Syntax?". In Bickerton, Derek; Szathmáry, Eörs (eds.). Biological Foundations and Origin of Syntax. Strüngmann Forum reports. MIT Press. p. 393. ISBN 9780262013567. Possible applications of evolutionary game theory to the study of the cultural evolution of language [...] have been investigated
  64. ^ Compare: Tomasello, Michael (2009-07-01) [1999]. The cultural origins of human cognition. Harvard University Press (published 2009). ISBN 9780674044371.
  65. ^ Sole, M-J. (1992). "Phonetic and phonological processes: nasalization". Language & Speech. 35: 29–43.
  66. ^ Pagels, H. R. (January 1, 1985). "Is the irreversibility we see a fundamental property of nature?" (PDF). Physics Today: 97–99.
  67. ^ Article 3. Whether God exists? newadvent.org

Further reading

  • 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 [1].
  • 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.
  • Stafford Beer, Self-organization as 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.
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