Abstract
With a steadily growing human population and rapid advancements in technology, the global human network is increasing in size and connection density. This growth exacerbates networked global threats and can lead to unexpected consequences such as global epidemics mediated by air travel, threats in cyberspace, global governance, etc. A quantitative understanding of the mechanisms guiding this global network is necessary for proper operation and maintenance of the global infrastructure. Each year the World Economic Forum publishes an authoritative report on global risks, and applying this data to a CARP model, we answer critical questions such as how the network evolves over time. In the evolution, we compare not the current states of the global risk network at different time points, but its steady state at those points, which would be reached if the risk were left unabated. Looking at the steady states show more drastically the differences in the challenges to the global economy and stability the world community had faced at each point of the time. Finally, we investigate the influence between risks in the global network, using a method successful in distinguishing between correlation and causation. All results presented in the paper were obtained using detailed mathematical analysis with simulations to support our findings.
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References
Asztalos, A., Sreenivasan, S., Szymanski, B.K., Korniss, G.: Distributed flow optimization and cascading effects in weighted complex networks. Eur. Phys. J. B 85(8) (2012)
Cox, D.R., Miller, H.D.: The Theory of Stochastic Processes, vol. 134. CRC Press (1977)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM Algorithm. J. R. Stat. Soc. B 39(1), 1–38 (1977)
Dobson, I., Carreras, B.A., Lynch, V.E., Newman, D.E.: Complex systems analysis of series of blackouts: cascading failure, critical points, and self-organization. Chaos 17(2), 026103 (2007)
Haldane, A.G., May, R.M.: Systemic risk in banking ecosystems. Nature 469(7330), 351 (2011)
Lin, X., Moussawi, A., Korniss, G., Bakdash, J.Z., Szymanski, B.K.: Limits of risk predictability in a cascading alternating renewal process model. Sci. Rep. 7(6699) (2017)
Moussawi, A., Derzsy, N., Lin, X., Szymanski, B.K., Korniss, G.: Limits of predictability of cascading overload failures in spatially-embedded networks with distributed flows. Sci. Rep. 7(11729) (2017)
Pawitan, Y.: In All Likelihood: Statistical Modelling and Inference Using Likelihood. Oxford University Press, USA (2001)
Pastor-Satorras, R., Castellano, C., Van Mieghem, P., Vespignani, A.: Epidemic processes in complex networks. Rev. Mod. Phys. 87(925), 141–153 (2015)
Roukny, T., Bersini, H., Pirotte, H., Caldarelli, G., Battiston, S.: Default cascades in complex networks: topology and systemic risk. Sci. Rep. 3(2759) (2013)
Szymanski, B.K., Lin, X., Asztalos, A., Sreenivasan, S.: Failure dynamics of the global risk network. Sci. Rep. 5(10998) (2015)
Watts, D.J.: A simple model of global cascades on random networks. Proc. Natl. Acad. Sci. 99(9) (2002)
World Economic Forum Global Risks Report. (2013). Last accessed 22 Mar 2013
World Economic Forum Global Risks Report. (2017). Last accessed 28 May 2017
Acknowledgements
This work was supported in part by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 (the Network Science CTA), by the Army Research Office grant no. W911NF-16-1-0524, and by DTRA Award No. HDTRA1-09-1-0049. The views and conclusions contained in this document are those of the authors.
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Niu, X., Moussawi, A., Derzsy, N., Lin, X., Korniss, G., Szymanski, B.K. (2018). Evolution of the Global Risk Network Mean-Field Stability Point. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_91
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DOI: https://doi.org/10.1007/978-3-319-72150-7_91
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