Chaos in World Politics: A Reflection

The “Drop of Honey Effect”
  • Manuel Alberto Martins Ferreira
  • José António Candeias Bonito Filipe
  • Manuel F. P. Coelho
  • Isabel C. Pedro
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

Chaos theory results from natural scientists’ findings in the area of non-linear dynamics. The importance of related models has increased in the last decades, by studying the temporal evolution of non-linear systems. In consequence, chaos is one of the concepts that most rapidly have been expanded in what research topics respects. Considering that relationships in non-linear systems are unstable, chaos theory aims to understand and to explain this kind of unpredictable aspects of nature, social life, the uncertainties, the nonlinearities, the disorders and confusion, scientifically it represents a disarray connection, but basically it involves much more than that. The existing close relationship between change and time seems essential to understand what happens in the basics of chaos theory. In fact, this theory got a crucial role in the explanation of many phenomena. The relevance of this kind of theories has been well recognized to explain social phenomena and has permitted new advances in the study of social systems. Chaos theory has also been applied, particularly in the context of politics, in this area. The goal of this chapter is to make a reflection on chaos theory – and dynamical systems such as the theories of complexity – in terms of the interpretation of political issues, considering some kind of events in the political context and also considering the macro-strategic ideas of states positioning in the international stage.

Keywords

Chaos World politics Economics Drop of honey effect 

References

  1. 1.
    Amin, S. (2004). U.S. imperialism, Europe, and the Middle East. Monthly Review, 56(6), 13–33.CrossRefMathSciNetGoogle Scholar
  2. 2.
    Bayart, J.-F. (2000). Africa in the world: A history of extraversion. African Affairs’, 395(99), 217–267.CrossRefGoogle Scholar
  3. 3.
    Berge, P., Pomeau, Y., & Vidal, C. (1984). Order within chaos. New York: Wiley.MATHGoogle Scholar
  4. 4.
    Berliner, L. M. (1992). Statistics, probability and chaos. Statistical Science, 7(1), 69–122.CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Bjorndal, T. (1987). Production economics and optimal stock size in a North Atlantic fishery. Scandinavian Journal of Economics, 89(2), 145–164.CrossRefGoogle Scholar
  6. 6.
    Bjorndal, T., & Conrad, J. (1987). The dynamics of an open access fishery. Canadian Journal of Economics, 20(1), 74–85.CrossRefGoogle Scholar
  7. 7.
    Campbell, D. K., & Mayer-Kress, G. (1997). Chaos and politics: Applications of nonlinear dynamics to socio-political issues. In C. Grebogi & J. A. Yorke (Eds.), The impact of chaos on science and society. Tokyo: United Nations University Press.Google Scholar
  8. 8.
    Capra, F. (1996). The web of life: A new scientific understanding of living systems. New York: Anchor Books.Google Scholar
  9. 9.
    Clark, C. W. (1974). Possible effects of schooling on the dynamics of exploited fish populations. Journal du Conseil Internatinal pour L’Exploration de la Mer, 36(1), 7–14.CrossRefGoogle Scholar
  10. 10.
    Farazmand, A. (2003, December). Chaos and transformation theories: A theoretical analysis with implications for organization theory and public management. Public Organization, 3(4), 339–372.CrossRefGoogle Scholar
  11. 11.
    Ferreira, M. A. M., & Menezes, R. (1992). Equações com Diferenças – Aplicações em problemas de Finanças, Economia, Sociologia e Antropologia. Lisboa: Sílabo.Google Scholar
  12. 12.
    Ferreira, M. A. M., Filipe, J. A., Coelho, M., & Pedro, M. I. (2010), Fishing policies and the contribution of Chaos theory for fisheries management. In International conference on technology and business management. Proceedings. Dubai: ICTBM-10.Google Scholar
  13. 13.
    Ferreira, M. A. M., Filipe, J. A., Coelho, M., & Pedro, M. I. C. (2011). Chaos effect in fisheries management. Journal of Economics and Engineering, 2(1), 36–43.Google Scholar
  14. 14.
    Ferreira, M. A. M., Filipe, J. A., Coelho, M., & Pedro, M. I. C. (2011). Modelling the dissipative effect of fisheries. China-USA Business Review, 10(11), 1110–1114.Google Scholar
  15. 15.
    Ferreira, M. A. M., Filipe, J. A., Coelho, M., & Pedro, M. I. C. (2013). Managing fisheries in light of complexity and chaos theories. In S. Banerjee (Ed.), Chaos and complexity theory for management: Nonlinear dynamics. Hershey: IGI Globalinstead of Hershey: Information Science Reference.Google Scholar
  16. 16.
    Ferreira, M. A. M., Filipe, J. A., & Coelho, M. (2014). The fisheries dissipative effect modelling through dynamical systems and chaos theory. Applied Mathematical Sciences, 8(9–12), 573–578. doi: 10.12988/ams.2014.312686.
  17. 17.
    Filipe, J. A. (2006). O Drama dos Recursos Comuns. Um caso de aplicação da Teoria dos Jogos aos comuns da pesca. Ph.D. thesis, ISCTE, Lisboa.Google Scholar
  18. 18.
    Filipe, J. A., Coelho, M., & Ferreira, M. A. M. (2005). Sistemas Dinâmicos, Caos e os Comuns da Pesca. Revista de Economia Global e Gestão. N.º 2/2005. Lisboa: ISCTE.Google Scholar
  19. 19.
    Filipe, J. A., Ferreira, M. A. M., & Coelho, M. (2007). O Drama dos Recursos Comuns nas Sociedades Actuais: à procura de soluções para os Ecossistemas em perigo. Lisboa: Edições Sílabo.Google Scholar
  20. 20.
    Filipe, J. A., Ferreira, M. A. M., & Coelho, M. (2008). The relevance of chaos theory to explain problems of overexploitation in fisheries (Working Paper, WP/24/2008/DE/SOCIUS). Lisboa: ISEG.Google Scholar
  21. 21.
    Filipe, J. A., Ferreira, M. A. M., Coelho, M., & Pedro, M. I. C. (2009). Complexity, theory of chaos and fishing. In D. Porath & A. Bayer (Eds.), “International suplement” special “update”. Mainz: FH Mainz, University of Applied Sciences.Google Scholar
  22. 22.
    Filipe, J. A., Ferreira, M. A. M., Coelho, M., & Pedro, M. I. C. (2010). Chaos, anti-chaos and resources: Dealing with complexity. Aplimat-Journal of Applied Mathematics, 3(2), 83–90.Google Scholar
  23. 23.
    Filipe, J. A., Ferreira, M. A. M., Coelho, M., & Pedro, M. I. (2010). Managing complexity: A problem of chaos in fisheries policy. China-USA Business Review, 9(3), 15–23. David Publishing Company.Google Scholar
  24. 24.
    Filipe, J. A., Ferreira, M. A. M., Coelho, M., Pedro, M. I., & Andrade, M. (2010). Analysing fisheries management through complexity and chaos theories framework. Journal of Mathematics and Technology, 1(2), 5–12.MATHGoogle Scholar
  25. 25.
    Galtung, J. (1975). Entropy and the general theory of peace (Peace: Research education action, essays in peace research, Vol. 1). Copenhagen: Ejlers.Google Scholar
  26. 26.
    Geyer, R. (2003, September 19). Europeanisation, complexity, and the British Welfare State. Paper presented to the UACES/ESRC Study Group on The Europeanisation of British Politics and Policy-Making, Department of Politics, University of Sheffield, Sheffield.Google Scholar
  27. 27.
    Grabinski, M. (2004). Is there chaos in management or Just Chaotic Management? Reprint of Complex Systems, intelligence and Modern Technology Applications. Paris. http://www.h-n-u.de/Veroeffentlichungen/CSIMTA%202004.pdf
  28. 28.
    Grabinski, M. (2008). Chaos – Limitation or even end of supply chain management. High speed flow of material, information and capital. Istanbul. ISBN: 978-605-399-070-3. http://www.h-n-u.de/Veroeffentlichungen/Chaos%202008.pdf
  29. 29.
    Hastings, A., Hom, C. L., Ellner, S., Turchin, P., & Godfray, H. C. J. (1993). Chaos in ecology: Is mother nature a strange attractor? Annual Review of Ecology and Systematics, 24(1), 1–33.Google Scholar
  30. 30.
    I Font, J. P. P., & Régis, D. (2006). Chaos theory and its application in political science [Draft]. Fukuoka: IPSA – AISP Congress.Google Scholar
  31. 31.
    Kauffman, S. (1993). The origins of order: Self-organization and selection in evolution. New York: Oxford University Press.Google Scholar
  32. 32.
    Lansing, J. S. (2003). Complex adaptive systems. Annual Review of Anthropology, 32, 183–204. http://www.ic.arizona.edu/lansing/GompAdSys.pdf
  33. 33.
    Lévêque, G. (2002). Ecologia: do ecossistema à biosfera. Lisboa: Instituto Piaget.Google Scholar
  34. 34.
    Levin, S. (2003). Complex adaptive systems: Exploring the known, the unknown and the unknowable. Bulletin of the American Mathematical Society, 40, 3–19.CrossRefMATHGoogle Scholar
  35. 35.
    Mangel, M., & Clark, G. (1983). Uncertainty, search and information in fisheries. Journal du Conseil International pour L’Exploration de, la Mer, 41, 93–103.CrossRefGoogle Scholar
  36. 36.
    Maynard Smith, J. (1968). Mathematical ideas in biology. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  37. 37.
    Moore, R. (2004). Education and society: Issues and explorations in the sociology of education. Cambridge: Polity.Google Scholar
  38. 38.
    Neher, P. (1990). Natural resource economics: Conservation and exploitation. Cambridge: Cambridge University Press.Google Scholar
  39. 39.
    Olsen, L. F., & Degn, H. (1985). Chaos in biological systems. Quarterly Review of Biophysics, 18(2), 165–225.CrossRefGoogle Scholar
  40. 40.
    Peled, A. (2000). The new sciences, self-organization and democracy. Democratization, 7(2), 19–35.CrossRefMathSciNetGoogle Scholar
  41. 41.
    Prigogine, I. (1993). Les Lois du chaos. Paris: Flammarion.Google Scholar
  42. 42.
    Prigogine, I., & Nicolis, G. (1989). Exploring complexity: an introduction. New York: W.H. Freeman and Company.Google Scholar
  43. 43.
    Prigogine, I., & Stenglers, I. (1984). Order out of chaos. Boulder: New Science Library.Google Scholar
  44. 44.
    Radu, M. (2000, Winter). Festina Lente: United States and Cuba after Castro. What the experience in Eastern Europe suggests. Probable realities and recommendations. Studies in Comparative International Development, 34(4), 7–22.CrossRefGoogle Scholar
  45. 45.
    Rasband, N. S. (1990). Chaotic dynamics of nonlinear systems. New York: Wiley.Google Scholar
  46. 46.
    Scones, I. (1999). New ecology and the social sciences: What prospects for a fruitful engagement? Annual Review of Anthropology, 28, 479–507.ADSCrossRefGoogle Scholar
  47. 47.
    Thrift, N. (2008). Non-representational theory. New York: Routledge.Google Scholar
  48. 48.
    Tsing, A. L. (2005). Friction: An ethnography of global connection. Princeton: Princeton University Press.Google Scholar
  49. 49.
    Williams, G. P. (1997). Chaos theory tamed. Washington, DC: Joseph Henry Press.MATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Manuel Alberto Martins Ferreira
    • 1
  • José António Candeias Bonito Filipe
    • 1
  • Manuel F. P. Coelho
    • 2
  • Isabel C. Pedro
    • 3
  1. 1.Departamento de MatemáticaInstituto Universitário de Lisboa (ISCTE-IUL), BRU-UNIDELisboaPortugal
  2. 2.Departamento de EconomiaSOCIUS/ISEG-UTLLisboaPortugal
  3. 3.Departamento de Engenharia e GestãoInstituto Superior Técnico (CEGIST/IST)LisboaPortugal

Personalised recommendations