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Globally networked risks and how to respond

Nature volume 497, pages 51–59 (2013)Cite this article

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Abstract

Today’s strongly connected, global networks have produced highly interdependent systems that we do not understand and cannot control well. These systems are vulnerable to failure at all scales, posing serious threats to society, even when external shocks are absent. As the complexity and interaction strengths in our networked world increase, man-made systems can become unstable, creating uncontrollable situations even when decision-makers are well-skilled, have all data and technology at their disposal, and do their best. To make these systems manageable, a fundamental redesign is needed. A ‘Global Systems Science’ might create the required knowledge and paradigm shift in thinking.

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Figure 1: Risks Interconnection Map 2011 illustrating systemic interdependencies in the hyper-connected world we are living in.
Figure 2: Spreading and erosion of cooperation in a prisoner’s dilemma game.
Figure 3: Illustration of probabilistic cascade effects in systems with networked risks.
Figure 4: Cascade spreading is increasingly hard to recover from as failure progresses.
Figure 5: Box 3 Figure Illustration of the principle of a ‘time bomb’.

References

  1. World Economic Forum. Global Risks 2012 and 2013 (WEF, 2012 and 2013); http://www.weforum.org/issues/global-risks.

  2. Rinaldi, S. M., Peerenboom, J. P. & Kelly, T. K. Critical infrastructure interdependencies. IEEE Control Syst. 21, 11–25 (2001)

    Article  Google Scholar 

  3. Rosato, V. et al. Modelling interdependent infrastructures using interacting dynamical models. Int. J. Critical Infrastruct. 4, 63–79 (2008)

    Article  Google Scholar 

  4. Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E. & Havlin, S. Catastrophic cascade of failures in interdependent networks. Nature 464, 1025–1028 (2010)

    Article  ADS  CAS  PubMed  Google Scholar 

  5. Gao, J., Buldyrev, S. V., Havlin, S. & Stanley, H. E. Robustness of networks of networks. Phys. Rev. Lett. 107, 195701 (2011)

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Vespignani, A. The fragility of interdependency. Nature 464, 984–985 (2010)

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Brockmann, D., Hufnagel, L. & Geisel, T. The scaling laws of human travel. Nature 439, 462–465 (2006)

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Vespignani, A. Predicting the behavior of techno-social systems. Science 325, 425–428 (2009)

    Article  ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

  9. Epstein, J. M. Modelling to contain pandemics. Nature 460, 687 (2009)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Crutzen, P. & Stoermer, E. The anthropocene. Global Change Newsl. 41, 17–18 (2000)

    Google Scholar 

  11. Helbing D., Carbone A., eds. Participatory science and computing for our complex world. Eur. Phys. J. Spec. Top. 214, (special issue). 1–666 (2012)

    Article  Google Scholar 

  12. Zeeman E. C., ed. Catastrophe Theory (Addison-Wesley, 1977)

  13. Stanley, H. E. Introduction to Phase Transitions and Critical Phenomena (Oxford Univ. Press, 1987)

    Google Scholar 

  14. Watts, D. J. A simple model of global cascades on random networks. Proc. Natl Acad. Sci. USA 99, 5766–5771 (2002)

    Article  ADS  MathSciNet  CAS  PubMed  MATH  PubMed Central  Google Scholar 

  15. Motter, A. E. Cascade control and defense in complex networks. Phys. Rev. Lett. 93, 098701 (2004)

    Article  ADS  CAS  PubMed  Google Scholar 

  16. Simonsen, I., Buzna, L., Peters, K., Bornholdt, S. & Helbing, D. Transient dynamics increasing network vulnerability to cascading failures. Phys. Rev. Lett. 100, 218701 (2008)

    Article  ADS  CAS  PubMed  Google Scholar 

  17. Little, R. G. Controlling cascading failure: understanding the vulnerabilities of interconnected infrastructures. J. Urban Technol. 9, 109–123 (2002)This is an excellent analysis of the role of interconnectivity in catastrophic failures.

    Article  Google Scholar 

  18. Buzna, L., Peters, K., Ammoser, H., Kühnert, C. & Helbing, D. Efficient response to cascading disaster spreading. Phys. Rev. E 75, 056107 (2007)

    Article  ADS  CAS  Google Scholar 

  19. Lorenz, J., Battiston, S. & Schweitzer, F. Systemic risk in a unifying framework for cascading processes on networks. Eur. Phys. J. B 71, 441–460 (2009)This paper gives a good overview of different classes of cascade effects with a unifying theoretical framework.

    Article  ADS  MathSciNet  CAS  MATH  Google Scholar 

  20. Battiston, S., Delli Gatti, D., Gallegati, M., Greenwald, B. & Stiglitz, J. E. Default cascades: when does risk diversification increase stability? J. Financ. Stab. 8, 138–149 (2012)

    Article  MATH  Google Scholar 

  21. Albeverio S., Jentsch V., Kantz H., eds. Extreme Events in Nature and Society (Springer, 2010)

  22. Bak, P., Tang, C. & Wiesenfeld, K. Self-organized criticality: an explanation of the 1/f noise. Phys. Rev. Lett. 59, 381–384 (1987)

    Article  ADS  CAS  PubMed  Google Scholar 

  23. Albert, R., Jeong, H. & Barabasi, A. L. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000)

    Article  ADS  CAS  PubMed  Google Scholar 

  24. Kun, F., Carmona, H. A., Andrade, J. S. Jr & Herrmann, H. J. Universality behind Basquin’s law of fatigue. Phys. Rev. Lett. 100, 094301 (2008)

    Article  ADS  CAS  PubMed  Google Scholar 

  25. Achlioptas, D., D’Souza, R. M. & Spencer, J. Explosive percolation in random networks. Science 323, 1453–1455 (2009)

    Article  ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

  26. Sornette, D. & Ouillon, G. Dragon-kings: mechanisms, statistical methods and empirical evidence. Eur. Phys. J. Spec. Top. 205, 1–26 (2012)

    Article  Google Scholar 

  27. Nicolis, G. Introduction to Nonlinear Science (Cambridge Univ. Press, 1995)

    Book  Google Scholar 

  28. Strogatz, S. H. Nonlinear Dynamics and Chaos (Perseus, 1994)

    Google Scholar 

  29. Liu, Y. Y., Slotine, J. J. & Barabasi, A. L. Controllability of complex networks. Nature 473, 167–173 (2011)

    Article  ADS  CAS  PubMed  Google Scholar 

  30. Dörner, D. The Logic of Failure (Metropolitan, 1996)This book is a good demonstration that we tend to make wrong decisions when trying to manage complex systems.

    Google Scholar 

  31. Nowak, M. A. Evolutionary Dynamics (Belknap, 2006)

    Book  MATH  Google Scholar 

  32. Helbing, D. Social Self-Organization (Springer, 2012)This book offers an integrative approach to agent-based modelling of emergent social phenomena, systemic risks in social and economic systems, and how to manage complexity.

    Book  Google Scholar 

  33. Johansson, A., Helbing, D., Al-Abideen, H. Z. & Al-Bosta, S. From crowd dynamics to crowd safety: a video-based analysis. Adv. Complex Syst. 11, 497–527 (2008)

    Article  MATH  Google Scholar 

  34. Helbing, D. & Mukerji, P. Crowd disasters as systemic failures: analysis of the Love Parade disaster. Eur. Phys. J. Data Sci. 1, 7 (2012)

    Google Scholar 

  35. Bettencourt, L. M. A. et al. Growth, innovation, scaling and the pace of life in cities. Proc. Natl Acad. Sci. USA 104, 7301–7306 (2007)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ball, P. Why Society is a Complex Matter (Springer, 2012)

    Book  Google Scholar 

  37. Aven T., Vinnem J. E., eds. Risk, Reliability and Societal Safety Vols 1–3 (Taylor and Francis, 2007)This compendium is a comprehensive source of information about risk, reliability, safety and resilience.

  38. Rodriguez H., Quarantelli E. L., Dynes R. R., eds. Handbook of Disaster Research (Springer, 2007)

  39. Cox, L. A. Jr Risk Analysis of Complex and Uncertain Systems (Springer, 2009)

    Book  MATH  Google Scholar 

  40. Perrow, C. Normal Accidents. Living with High-Risk Technologies (Princeton Univ. Press, 1999)This eye-opening book shows how catastrophes result from couplings and complexity.

    Google Scholar 

  41. Peters, G. A. & Peters, B. J. Human Error. Causes and Control (Taylor and Francis, 2006)This book is a good summary of why, how and when people make mistakes.

    Book  Google Scholar 

  42. Clarke, L. Worst Cases (Univ. Chicago, 2006)

    Google Scholar 

  43. Axelrod, R. & Cohen, M. D. Harnessing Complexity (Basis Books, 2000)This book offers a good introduction into complex social systems and bottom-up management.

    Google Scholar 

  44. Tumer, K. & Wolpert, D. H. Collectives and the Design of Complex Systems (Springer, 2004)

    Book  MATH  Google Scholar 

  45. Lämmer, S. & Helbing, D. Self-control of traffic lights and vehicle flows in urban road networks. J. Stat. Mech. P04019 (2008)

  46. Perkins, C. E. & Royer, E. M. Ad-hoc on-demand distance vector routing. In Second IEEE Workshop on Mobile Computing Systems and Applications 90–100 (WMCSA Proceedings, 1999)

    Google Scholar 

  47. Amin, M. M. & Wollenberg, B. F. Toward a smart grid: power delivery for the 21st century. IEEE Power Energy Mag. 3, 34–41 (2005)

    Article  Google Scholar 

  48. Schneider, C. M., Moreira, A. A., Andrade, J. S. Jr, Havlin, S. & Herrmann, H. J. Mitigation of malicious attacks on networks. Proc. Natl Acad. Sci. USA 108, 3838–3841 (2011)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  49. Comfort L. K., Boin A., Demchak C. C., eds. Designing Resilience. Preparing for Extreme Events (Univ. Pittsburgh, 2010)

  50. Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009)

    Article  ADS  CAS  PubMed  Google Scholar 

  51. Pikovsky, A., Rosenblum, M. & Kurths, J. Synchronization (Cambridge Univ. Press, 2003)

    Book  MATH  Google Scholar 

  52. Haldane, A. G. & May, R. M. Systemic risk in banking ecosystems. Nature 469, 351–355 (2011)

    Article  ADS  CAS  PubMed  Google Scholar 

  53. Battiston, S., Puliga, M., Kaushik, R., Tasca, P. & Caldarelli, G. DebtRank: too connected to fail? Financial networks, the FED and systemic risks. Sci. Rep. 2, 541 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Stiglitz, J. E. Freefall: America, Free Markets, and the Sinking of the World Economy (Norton & Company, 2010)

    Google Scholar 

  55. Sterman, J. Business Dynamics: Systems Thinking and Modeling for a Complex World (McGraw-Hill/Irwin, 2000)

    Google Scholar 

  56. Helbing, D. & Lämmer, S. in Networks of Interacting Machines: Production Organization in Complex Industrial Systems and Biological Cells (eds Armbruster, D., Mikhailov, A. S. & Kaneko, K.) 33–66 (World Scientific, 2005)

  57. Young, H. P. Innovation diffusion in heterogeneous populations: contagion, social influence, and social learning. Am. Econ. Rev. 99, 1899–1924 (2009)

    Article  Google Scholar 

  58. Montanari, A. & Saberi, A. The spread of innovations in social networks. Proc. Natl Acad. Sci. USA 107, 20196–20201 (2010)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  59. Grund, T., Waloszek, C. & Helbing, D. How natural selection can create both self- and other-regarding preferences, and networked minds. Sci. Rep. 72 1480 http://dx.doi.org/10.1038/srep01480 (2013)

    Article  CAS  Google Scholar 

  60. Lazer, D. et al. Computational social science. Science 323, 721–723 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Epstein, J. M. & Axtell, R. L. Growing Artificial Societies: Social Science from the Bottom Up (Brookings Institution, 1996)This is a groundbreaking book on agent-based modelling.

    Book  Google Scholar 

  62. Gilbert, N. & Bankes, S. Platforms and methods for agent-based modeling. Proc. Natl Acad. Sci. USA 99 (S3). 7197–7198 (2002)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  63. Farmer, J. D. & Foley, D. The economy needs agent-based modeling. Nature 460, 685–686 (2009)

    Article  ADS  CAS  PubMed  Google Scholar 

  64. Szell, M., Sinatra, R., Petri, G., Thurner, S. & Latora, V. Understanding mobility in a social petri dish. Sci. Rep. 2, 457 (2012)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  65. de Freitas, S. Game for change. Nature 470, 330–331 (2011)

    Article  ADS  CAS  Google Scholar 

  66. McNeil, A. J., Frey, R. & Embrechts, P. Quantitative Risk Management (Princeton Univ. Press, 2005)

    MATH  Google Scholar 

  67. Preis, T., Kenett, D. Y., Stanley, H. E., Helbing, D. & Ben-Jacob, E. Quantifying the behaviour of stock correlations under market stress. Sci. Rep. 2, 752 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  68. Floriano, D. & Mattiussi, C. Bio-Inspired Artificial Intelligence (MIT Press, 2008)

    Google Scholar 

  69. Pentland, A. Society’s nervous system: building effective government, energy, and public health systems. IEEE Computer 45, 31–38 (2012)

    Article  Google Scholar 

  70. Kesting, A., Treiber, M., Schönhof, M. & Helbing, D. Adaptive cruise control design for active congestion avoidance. Transp. Res. C 16, 668–683 (2008)

    Article  Google Scholar 

  71. Fowler, J. H. & Christakis, N. A. Dynamic spread of happiness in a large social network. Br. Med. J. 337, a2338 (2008)

    Article  Google Scholar 

  72. Helbing, D. & Johansson, A. Cooperation, norms, and revolutions: a unified game-theoretical approach. PLoS ONE 5, e12530 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  73. Seydel, R. U. Practical Bifurcation and Stability Analysis (Springer, 2009)

    MATH  Google Scholar 

  74. Bak, P., Christensen, K., Danon, L. & Scanlon, T. Unified scaling law for earthquakes. Phys. Rev. Lett. 88, 178501 (2002)

    Article  ADS  CAS  PubMed  Google Scholar 

  75. Helbing, D. Traffic and related self-driven many-particle systems. Rev. Mod. Phys. 73, 1067–1141 (2001)

    Article  ADS  MathSciNet  Google Scholar 

  76. Lozano, S., Buzna, L. & Diaz-Guilera, A. Role of network topology in the synchronization of power systems. Eur. Phys. J. B 85, 231–238 (2012)

    Article  ADS  MATH  Google Scholar 

  77. Schuster, H. G. & Just, W. Deterministic Chaos (Wiley-VCH, 2005)

    Book  MATH  Google Scholar 

  78. Wiener, N. Cybernetics (MIT Press, 1965)

    Google Scholar 

  79. Beale, N. et al. Individual versus systemic risk and the regulator’s dilemma. Proc. Natl Acad. Sci. USA 108, 12647–12652 (2011)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  80. Allen, P. M. Evolution, population dynamics, and stability. Proc. Natl Acad. Sci. USA 73, 665–668 (1976)

    Article  ADS  MathSciNet  CAS  PubMed  MATH  PubMed Central  Google Scholar 

  81. Tainter, J. The Collapse of Complex Societies (Cambridge Univ. Press, 1988)

    Google Scholar 

  82. The World Economic Forum, Global Risks 2011 6th edn (WEF, 2011); http://reports.weforum.org/wp-content/blogs.dir/1/mp/uploads/pages/files/global-risks-2011.pdf.

  83. Huntington, S. P. The clash of civilisations? Foreign Aff. 72, 22–49 (1993)

    Article  Google Scholar 

  84. Cederman, L. E. Endogenizing geopolitical boundaries with agent-based modeling. Proc. Natl Acad. Sci. USA 99 (suppl. 3). 7296–7303 (2002)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  85. Johnson, N. et al. Pattern in escalations in insurgent and terrorist activity. Science 333, 81–84 (2011)

    Article  ADS  CAS  PubMed  Google Scholar 

  86. Beck, U. Risk Society (Sage, 1992)

    Google Scholar 

  87. Lin, N. Social Capital (Routeledge, 2010)

    Google Scholar 

  88. Kröger, W. & Zio, E. Vulnerable Systems (Springer, 2011)

    Book  Google Scholar 

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Acknowledgements

This work has been supported partially by the FET Flagship Pilot Project FuturICT (grant number 284709) and the ETH project “Systemic Risks—Systemic Solutions” (CHIRP II project ETH 48 12-1). I thank L. Böttcher, T. Grund, M. Kaninia, S. Rustler and C. Waloszek for producing the cascade spreading movies and figures. I also thank the FuturICT community for many inspiring discussions.

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  1. ETH Zurich, Clausiusstrasse 50, Zurich, 8092, Switzerland

    Dirk Helbing

  2. Risk Center, ETH Zurich, Swiss Federal Institute of Technology, Scheuchzerstrasse 7, Zurich, 8092, Switzerland

    Dirk Helbing

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Helbing, D. Globally networked risks and how to respond. Nature 497, 51–59 (2013). https://doi.org/10.1038/nature12047

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