Self-Organization: What Is It, What Isn’t It, and What’s It Got to Do with Morphogenesis?

  • Kate Forbes-PittEmail author


This chapter provides basic definitions of self-organization and the terms most frequently associated with it within the theory from which it originated: Complexity theory. Using these definitions, I draw out the consistent meta-theoretical assumptions on which the assertions and practice of complexity theory in the natural sciences have been based. One in particular is highlighted: the causal relation between elements and relations between relations. This, I argue, is a crucial ontological premise of self-organization. In relating self-organization and morphogenesis, I concentrate on this broadly shared meta-theoretical assumption, showing that the causal social relation has also been consistently important as an ontological premise in morphogenesis. The chapter concludes that only through philosophical analysis of the possibility of a meta-theory underpinning the concepts of both naturalistic complexity and social science can self-organization be successfully integrated into social science.


Definitions of self-organization Complexity theory Self-organization and morphogenesis Social science and self-organization 


  1. Adriani P, McKelvey B (2006) From Gaussian to Paretian thinking: causes and implications of power-laws in organizations. Organ Sci 20(6):1053–1071CrossRefGoogle Scholar
  2. Anderson PW (1972) More is different. Science 177:393–396CrossRefGoogle Scholar
  3. Archer, M.S. (1984). The social origins of educational systems. Sage Publications, LondonGoogle Scholar
  4. Archer MS (1988) Culture and agency. Cambridge University Press, CambridgeGoogle Scholar
  5. Archer MS (1995) Realist social theory: the morphogenetic approach. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  6. Archer MS (2000) Being human: the problem of agency. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  7. Archer MS (2003) Structure, agency and the internal conversation. Cambridge University Press, CambridgeGoogle Scholar
  8. Bak P (1996) How nature works. Springer-Verlag, New YorkGoogle Scholar
  9. Bak P, Tang C, Wiesenfeld K (1987a) Self-organized criticality. Phys Rev A 38(1):364–374CrossRefGoogle Scholar
  10. Bak P, Tang C, Wiesenfeld K (1987b) Self-organized criticality: an explanation of 1/f noise. Phys Rev Lett 59(4):381–384CrossRefGoogle Scholar
  11. Barabási L (2009) Scale-free networks: a decade and beyond. Science 325:325–326CrossRefGoogle Scholar
  12. Barabási L (2011) Bursts. Dutton, New YorkGoogle Scholar
  13. Bhaskar R (1979) The possibility of naturalism. Routledge, OxfordGoogle Scholar
  14. Boccara N (2003) Modeling complex systems. Springer, New YorkGoogle Scholar
  15. Cilliers P (1998) Complexity and postmodernism. Routledge, AbingdonGoogle Scholar
  16. Chowdhury D, Santern L, Schdschnieder A (2000) Statistical physics of vehicular traffic and some related systems. Phys Rep 329:199–329CrossRefGoogle Scholar
  17. Donati P (2011) Relational sociology. Routledge, AbingdonGoogle Scholar
  18. Dreyfus HL (1999) What computers still can’t do: a critique of artificial reason. MIT Press, CambridgeGoogle Scholar
  19. Dyson F (1978) Characterizing irregularities. Science 200:677–678CrossRefGoogle Scholar
  20. Galam S (2004) Sociophysics: a personal testimony. Phys A 336:49–55CrossRefGoogle Scholar
  21. Galam S (2006) Pourquoi des électons se seres? Le monde, 20Google Scholar
  22. Gell-Man M (1988) The concept of the institute. In: Pines D (ed) Emerging synthesis in science. Addison Wesley, BostonGoogle Scholar
  23. Gell-Man M (2002) What is complexity? In: Curzio AQ, Fortis M (eds) Complexity and industrial clusters. Physica-Verlag, HeidelbergGoogle Scholar
  24. Lazer D et al (2009) Computational social science. Science 323:721–723CrossRefGoogle Scholar
  25. Mainzer K (2007) Thinking in complexity. Springer, New YorkGoogle Scholar
  26. Mandelbrot BB (1977) The Fractal geometry of nature. Freeman, New YorkGoogle Scholar
  27. Miller JH, Page SE (2007) Complex adaptive systems: an introduction to the computational models of social life. Princeton University Press, New JerseyGoogle Scholar
  28. Newman MEJ (2005) Power-laws, Pareto distributions and Zipfs law. Contem Phys 46:323–351CrossRefGoogle Scholar
  29. Nicolis G, Prigogine I (1989) Exploring complexity. Freeman, New YorkGoogle Scholar
  30. Prigogine I, Stengers I (1984) Order out of chaos. Bantam, New YorkGoogle Scholar
  31. Sokal A, Bricmont J (1998) Fashionable nonsense: postmodern intellectuals’ abuse of science. Picador, New YorkGoogle Scholar
  32. Sokal A, Bricmont J (1999) Fashionable nonsense: postmodern intellectuals’ abuse of science. Picador, New YorkGoogle Scholar
  33. Sornette D (2003) Critical phenomena in natural sciences: chaos, fractals, self organization, and disorder: concepts and tools. Springer, New YorkGoogle Scholar
  34. Sterman JD (1994) Learning in and about complex systems. Syst Dyn Rev 10(2–3):291–330CrossRefGoogle Scholar
  35. van Fraassen BC (1980) The Scientific Image. Clarendon, OxfordCrossRefGoogle Scholar
  36. Wagner HR (1964) Displacement of scope: a problem of the relationship between small-scale and large-scale social theories. Am J Sociol 36(6):571–584CrossRefGoogle Scholar

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© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Centre d’Ontologie SocialeEcole Polytechnique Fédérale de LausanneLausanneSwitzerland

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