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Modeling Social and Geopolitical Disasters as Extreme Events: A Case Study Considering the Complex Dynamics of International Armed Conflicts

  • Reinaldo Roberto RosaEmail author
  • Joshi Neelakshi
  • Gabriel Augusto L. L. Pinheiro
  • Paulo Henrique Barchi
  • Elcio Hideiti Shiguemori
Chapter

Abstract

Just as various sorts of extreme climatic events are identified in Earth’s atmosphere, so are some types of extreme events in our sociosphere. A geopolitical conflict that can result in a social disaster is an example. In this chapter, the turbulent-like dynamics of international armed conflicts are treated within the scope of complex multi-agent systems explicitly considering the properties of multiplicative non-homogeneous cascade where endogeny and exogeny are key points in the mathematical model of the phenomenon. As a main result, this study introduces a cellular automata prototype that allows characterizing regimes of extreme armed conflicts such as the 9∕11 terrorist attacks and the great world wars.

Notes

Acknowledgements

The authors are grateful for the financial support of the following agencies: CNPq, CAPES, and FAPESP.

References

  1. 1.
  2. 2.
  3. 3.
    Albeverio, S., Jentsch, V., Kantz, H. (eds.): Extreme Events in Nature and Society. The Frontiers Collection, Springer (2006). https://doi.org/10.1007/3-540-28611-X Google Scholar
  4. 4.
    Arneodo, A., Bacry, E., Muzy, J.F.: Phys. A 213(1–2), 232–275 (1995)CrossRefGoogle Scholar
  5. 5.
    Arneodo, A.E., Baudet, C., Belin, F., Benzi, R., Castaing, B., Chabaud, B., Dubrulle, B., et al.: Structure functions in turbulence, in various flow configurations, at Reynolds number between 30 and 5000, using extended self-similarity. EPL (Europhys. Lett.) 34(6), 411 (1996)Google Scholar
  6. 6.
    Bailey, Kenneth D.: Social Entropy Theory (term: “Prigogine entropy”), p. 72. State University of New York Press, New York (1990)Google Scholar
  7. 7.
    Bak, P.: How Nature Works. Springer, New York (1996)CrossRefGoogle Scholar
  8. 8.
    Ben Taieb, S., Sorjamaa, A., Bontempi. G.: Multiple-output modeling for multi-step-ahead time series forecasting. Neurocomputing 73(10), 1950–1957 (2010)Google Scholar
  9. 9.
    Boccara, N.: Modeling Complex Systems. Springer, New York (2010)CrossRefGoogle Scholar
  10. 10.
    Bohr, T., Jensen, M.H., Paladin, G., Vulpiani, A.: Dynamical Systems Approach to Turbulence. Cambridge University Press, Cambridge (1998)CrossRefGoogle Scholar
  11. 11.
    Bolzan, M.J.A., Ramos, F.M., Sa, L.D.A., Neto, C.R., Rosa, R.R.: Analysis of fine-scale canopy turbulence within and above an Amazon forest using Tsallis generalized thermostatistics. JGR 107-D20, 8063 (2002)Google Scholar
  12. 12.
    Bolzan, J.M., Rosa, R.R., Sahay,Y.: Multifractal analysis of low-latitude geomagnetic fluctuations. Ann. Geophys. 27(2) (Feb 2009)Google Scholar
  13. 13.
    Brownlee, J.: Time series prediction with LSTM recurrent neural networks with Keras. In: Deep learning with python, MLM (2016)Google Scholar
  14. 14.
    Buckley, W.: Sociology and the Modern Systems Theory. Prentice-Hall, Upper Saddle River (1967)Google Scholar
  15. 15.
    Couzin, I.D., Krause, J.: The social organization of fish schools. Adv. Ethology 36, 64 (2001)Google Scholar
  16. 16.
    Davis, A., Marshak, A., Cahalan, R., Wiscombe, W.: The landsat scale break in stratocumulus as a three-dimensional radiative transfer effect: implications for cloud remote sensing. J. Atmos. Sci. 54(2) (1997)Google Scholar
  17. 17.
    Dupuy, K., Gates, S., Nygard, H.M., Rudolfsen, I., Rustad, S.A., Strand, H., Urda, H.: Trends in Armed Conflict, 1946–2016. PRIO Conflict Trends (June 2017)Google Scholar
  18. 18.
    Enescu, B., Ito, K., Struzik, Z.: Geophys. J. Int. 164(1), 63–74 (2006)CrossRefGoogle Scholar
  19. 19.
    Epstein, J.M., Axtell, R.: Growing artificial societies: social science from the bottom up. The Brookings Institution/MIT Press, Cambridge (1996)CrossRefGoogle Scholar
  20. 20.
    Farge, M.: Annu. Rev. Fluid Mech. 24, 395–457 (1992)CrossRefGoogle Scholar
  21. 21.
    Frisch, U.: Cambridge University Press, New York (1995)Google Scholar
  22. 22.
    Fuchs, C.: Internet and Society: Social Theory in the Information Age. Routledge, New York (2008)Google Scholar
  23. 23.
    Gardiner, C.W.: Handbook of Stochastic Methods: For Physics, Chemistry and the Natural Sciences, 3rd edn. Springer Series in Synergetics, Berlin (2004)CrossRefGoogle Scholar
  24. 24.
    Gleditsch, N.P., Wallensteen, P., Eriksson, M., Sollenberg, M., Strand, H.: Armed conflict 1946–2001: a new dataset. J. Peace Res. 39(5), 615–637 (2002)CrossRefGoogle Scholar
  25. 25.
    Global Terrorism Index: Institute for Economics & Peace, pp. 94–95 (November 2016). ISBN 978-0-9942456-4-9Google Scholar
  26. 26.
    Halsey, T.C., Jensen, M.H., Kadanoff, L.P., Procaccia, I., Shraiman, B.I.: Phys. Rev. A 33, 1141 (1986)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Hersh, M.: Mathematical Modelling for Sustainable Development. Springer, New York (2006)Google Scholar
  28. 28.
    Hughes, R.L.: The flow of human crowds. Annu. Rev. Fluid Mech. 35, 169–182 (2003)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Jiang, P., Chen, C., Liu, X.: Time series prediction for evolutions of complex systems: a deep learning approach. In: Proceedings of 2016 IEEE International Conference on Control and Robotics Engineering (ICCRE).  https://doi.org/10.1109/ICCRE.2016.7476150
  30. 30.
    Kari, J.: Theory of cellular automata: a survey. Theor. Comput. Sci. 334(1–3), 3–33 (2005)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Keylock, C.J.: Multifractal surrogate-data generation algorithm that preserves pointwise Hölder regularity structure, with initial applications to turbulence. Phys. Rev. E 95(3), 032123 (2017)CrossRefGoogle Scholar
  32. 32.
    Konar, A., Bhattacharya, D.: Time-Series Prediction and Applications: A Machine Intelligence Approach. Springer, Berlin (2017)CrossRefGoogle Scholar
  33. 33.
    Majda, A.: Introduction to Turbulent Dynamical Systems in Complex Systems. Springer, New York (2016)CrossRefGoogle Scholar
  34. 34.
    Mallat, S.: IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989)CrossRefGoogle Scholar
  35. 35.
    Margalef, R., Gutiérrez, E.: How to introduce connectance in the frame of an expression for diversity. Am. Nat. 5, 601–607 (1983)CrossRefGoogle Scholar
  36. 36.
    Marwick, A.: War and Social Change in the Twentieth Century: A Comparative Study of Britain. Macmillan Student Editions. Macmillan, London (1974)Google Scholar
  37. 37.
    Meneveau, C., Sreenivasan, K.R.: Simple Multifractal Cascade Model for Fully Developed Turbulence. Phys. Rev. Lett. 59, 1424–1427 (1987)CrossRefGoogle Scholar
  38. 38.
    Meyers, R.A. (Ed.): Encyclopedia of Complexity and Systems Science. Springer, New York (2009)zbMATHGoogle Scholar
  39. 39.
    Miller, A.L.: China an emerging superpower? Stanf. J. Int. Rel. 6(1) (2005)Google Scholar
  40. 40.
    Milsum, J.H.: The technosphere, the biosphere, the sociosphere: their systems modeling and optimization. IEEE Spectr. 5(6) (1968).  https://doi.org/10.1109/MSPEC.1968.5214690.
  41. 41.
    Muzy, J.F., Bacry, E., Arneodo, A.: Phys. Rev. Lett. 67(25), 3515–3518 (1991)CrossRefGoogle Scholar
  42. 42.
    Ohnishi, T.J.: A mathematical method for the turbulent behavior of crowds using agent particles. J. Phys. Conf. Ser. 738, 012091 (2016)CrossRefGoogle Scholar
  43. 43.
    Oswiecimka, P., Kwapien, J., Drozdz, S.: Phys. Rev. E 74, 016103 (2006)CrossRefGoogle Scholar
  44. 44.
    Page, S.E.: Diversity and Complexity. Princeton University Press, Princeton (2011)zbMATHGoogle Scholar
  45. 45.
    Pei, S., Morone, F., Makse, H.A.: Theories for influencer in complex networks. In: Spreading Dynamics in Social Systems, Lehmann, S., Ahn, Y.-Y. (Eds.). Springer, New York (2017)Google Scholar
  46. 46.
    Prigogine, I.; Kondepudi, D., Modern Thermodynamics. Wiley, New York (1998)zbMATHGoogle Scholar
  47. 47.
    Ramos, F.M., Rosa, R.R., Neto, C.R., Bolzan, M.J.A., Sá, L.D.A.: Nonextensive thermostatistics description of intermittency in turbulence and financial markets. Nonlinear Anal. Theory Methods Appl. 47(5), 3521–3530 (2001)MathSciNetCrossRefGoogle Scholar
  48. 48.
    Ramos, F.M., Bolzan, M.J.A., Sá, L.D.A., Rosa, R.R.: Atmospheric turbulence within and above an Amazon forest. Physica D Nonlinear Phenom. 193(1–4), 278–291 (15 June 2004)Google Scholar
  49. 49.
    Ramos, F.M., Lima, I.B.T., Rosa, R.R., Mazzi, E.A., Carvalho, J.C., Rasera, M.F.F.L., Ometto, J.P.H.B., Assireu, A.T., Stech. J.L.: Extreme event dynamics in methane ebullition fluxes from tropical reservoirs. Geophys. Res. Lett. 33(21) (2006)Google Scholar
  50. 50.
    Rieucau, G., Holmin, A.J., Castillo, J.C., Couzin, I.D., Handegard, N.-O.: School-level structural and dynamic adjustments to perceived risk promote efficient information transfer and collective evasion in herring. Anim. Behav. 117, 69–78 (2016)CrossRefGoogle Scholar
  51. 51.
    Rodrigues Neto, C., Zanandrea, A., Ramos, F.M., Rosa, R.R., Bolzan, M.J.A., Sá, L.D.A.: Phys. A 295(1–2), 215–218 (2001)CrossRefGoogle Scholar
  52. 52.
    Schertzer, D., Lovejoy, S.: Multifractal Generation of Self-Organized Criticality. In: Novak, M.M. (ed.) Fractals in the Natural and Applied Sciences, pp. 325–339. North-Holland, Elsevier (1994)Google Scholar
  53. 53.
    Sethna, J.P.: Statistical Mechanics: Entropy, Order Parameters, and Complexity. Oxford-Clarendom Press, Oxford (2017)zbMATHGoogle Scholar
  54. 54.
    Smith, M., Zeigler, M.S.: Terrorism before and after 9/11—a more dangerous world? Res. Polit. 4(4), 1–8 (2017)Google Scholar
  55. 55.
    Sornette, D., Deschâtres, F., Gilbert, T., Ageon, Y.: Endogenous versus exogenous shocks in complex networks: an empirical test using book sale rankings. Phys. Rev. Lett. 93, 228701 (2004)CrossRefGoogle Scholar
  56. 56.
    Struzik, Z.R.: Fractals 8, 163–179 (2000)CrossRefGoogle Scholar
  57. 57.
    Themnér, L.: The UCDP/PRIO Armed Conflict Dataset Codebook, Version 4- 2016 (2016)Google Scholar
  58. 58.
    Turiel, A., Perez-Vicente, C.J., Grazzini, J.: J. Comput. Phys. 216, 362–390 (2006)MathSciNetCrossRefGoogle Scholar
  59. 59.
    University of Maryland’s Global Terrorism Database (GTD). https://www.start.umd.edu/gtd/
  60. 60.
    von Bertalanffy, K.L.: General Theory of Systems. Penguin University Books, Penguin (1978)Google Scholar
  61. 61.
    Weisbuch, G.: Complex Systems Dynamics. Santa Fé Institute. Westview Press, Boulder (1994)Google Scholar
  62. 62.
    WSJ Graphics: Wall Street J. Nov. 14 (2015)Google Scholar
  63. 63.
    Xiong, G., Zhang, S., Yang, X.: Phys. A 391, 6347–6361 (2012)MathSciNetCrossRefGoogle Scholar
  64. 64.
    Yam, Y.B.: Dynamics of Complex Systems. Addison-Wesley, Boston (1992)Google Scholar
  65. 65.
    Zivieri, R., Pacini, N., Finocchio, G., Carpentieri, M.: Rate of entropy model for irreversible processes in living systems. Sci. Rep. 7. Article number: 9134 (2017). https://doi.org/10.1038/s41598-017-09530-5

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Reinaldo Roberto Rosa
    • 1
    Email author
  • Joshi Neelakshi
    • 2
  • Gabriel Augusto L. L. Pinheiro
    • 2
  • Paulo Henrique Barchi
    • 2
  • Elcio Hideiti Shiguemori
    • 3
  1. 1.Lab for Computing and Applied Mathematics-INPESão José dos CamposBrazil
  2. 2.CAP-INPESão José dos CamposBrazil
  3. 3.Geo-intelligence Division, Institute of Advanced Studies (IEAV)Rov. TamoiosSão José dos CamposBrazil

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