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  • Print publication year: 2013
  • Online publication date: June 2014

1 - Self-organisations and emergence

Summary

Abstract

Many examples exist of systems made of a large number of comparatively simple elementary constituents which exhibit interesting and surprising collective emergent behaviours. They are encountered in a variety of disciplines ranging from physics to biology and, of course, economics and social sciences. We all experience, for instance, the variety of complex behaviours emerging in social groups. In a similar sense, in biology, the whole spectrum of activities of higher organisms results from the interactions of their cells and, at a different scale, the behaviour of cells from the interactions of their genes and molecular components. Those, in turn, are formed, as all the incredible variety of natural systems, from the spontaneous assembling, in large numbers, of just a few kinds of elementary particles (e.g., protons, electrons).

To stress the contrast between the comparative simplicity of constituents and the complexity of their spontaneous collective behaviour, these systems are sometimes referred to as “complex systems”. They involve a number of interacting elements, often exposed to the effects of chance, so the hypothesis has emerged that their behaviour might be understood, and predicted, in a statistical sense. Such a perspective has been exploited in statistical physics, as much as the later idea of “universality”. That is the discovery that general mathematical laws might govern the collective behaviour of seemingly different systems, irrespective of the minute details of their components, as we look at them at different scales, like in Chinese boxes.

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[1] Barabasi, A. L. and Bonabeau, E. 2003. Scale-free networks. Scientific American, 288(5), 60–70.
[2] Bak, P., Christensen, K., Danon, L. and Scanlon, T. 2002. Unified scaling law for earthquakes. Physical Review Letters, 88(17), 178501.
[3] Carlos, C. 2004. Effective web crawling. http://en.wikipedia.org/wiki/File:Scale-free_network_sample.png.
[4] Chandler, D. 1987. Introduction to Modern Statistical Mechanics. Oxford University Press.
[5] Christensen, K. and Maoloney, N. R. 2005. Complexity and Criticality. Imperial College Press, London.
[6] Coniglio, A., Fierro, A., Herrmann, H. J. and Nicodemi, M. (eds). 2004. Unifying Concepts in Granular Media and Glasses: From the Statistical Mechanics of Granular Media to the Theory of Jamming. Elsevier, Amsterdam.
[7] de Arcangelis, L., Godano, C., Lippiello, E. and Nicodemi, M. 2006. Universality in solar flare and earthquake occurrence. Physical Review Letters, 96(5), 051102.
[8] Guzzetti, F., Malamud, B., Turcottle, D. and Reichenbach, P. 2002. Power-law correlations of landslide areas in central Italy. Earth and Planetary Science Letters, 195, 169–183.
[9] Horvath, A. 2008 (May). Watts–Strogatz graph. http://en.wikipedia.org/wiki/File:Watts_strogatz.svg.
[10] Jensen, H. J. 1998. Self-Organized Criticality. Cambridge University Press.
[11] Liljeros, F., Edling, C. R., Amaral, L. A. N., Stanley, H. E. and Aberg, Y. 2001. The web of human sexual contacts. Nature, 411, 907–908.
[12] Nicodemi, M. 1997. Percolation and cluster formalism in continuous spin systems. Physica A, 238, 9.
[13] Nicodemi, M. and Prisco, A. 2007. A symmetry breaking model for X chromosome inactivation. Phys. Rev. Lett., 98(10), 108104.
[14] Olami, Z., Feder, H. J. S., and Christensen, K. 1992. Self-organized criticality in a continuous, nonconservative cellular automaton modeling earthquakes. Physical Review Letters, 68(8), 1244–1247.
[15] Paulsson, J. 2005. Stochastic gene expression. Physics ofLife, 2, 157–175.
[16] Piegari, E., Cataudella, V., Di Maio, R., Milano, L. and Nicodemi, M. 2006. A cellular automaton for the factor of safety field in landslides modeling. Geophysics Research Letters, 33, L01403.
[17] Richard, P., Nicodemi, M., Delannay, R., Ribière, P. and Bideau, D. 2005. Slow relaxation and compaction of granular systems. Nature Materials, 4, 121.
[18] Sethna, J. P. 2006. Statistical Mechanics: Entropy, Order Parameters and Complexity. Oxford University Press.
[19] Stauffer, D. and Aharony, A. 1994. Introduction to Percolation Theory. Taylor and Francis, London.
[20] Strogatz, S. H. 2001. Exploring complex networks. Nature, 410, 268–276.
[21] van Kampen, N. G. 1992. Stochastic Processes in Physics and Chemistry. Elsevier, Amsterdam.
[22] Watts, D. J. and Strogatz, S. H. 1998. Collective dynamics of ‘small-world’ networks. Nature, 393, 409–410.