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Networks Under Deep Uncertainty

In: Dynamics of Disasters

Author

Listed:
  • Fuad Aleskerov

    (HSE University
    Institute of Control Sciences of Russian Academy of Sciences)

  • Daniil Tkachev

    (HSE University)

Abstract

The situation of deep uncertainty is defined by the absence of any statistical evaluations of the situation development. For instance, such situations may include events that occur for the first time. We use scenario analysis to model the potential outcomes of events affecting networks under deep uncertainty. Centrality indices are used to identify vulnerable vertices in networks. We consider classic and new centrality indices. The new centrality indices take into account the properties of vertices and group influence. We have constructed a network of export/import and production data of basic crops (rice, wheat, maize, sorghum, barley, rye, millet, buckwheat, oats), as well as oil and rare earth compound trade for 2020. We have considered scenarios of various situations and identified the most vulnerable countries in these scenarios.

Suggested Citation

  • Fuad Aleskerov & Daniil Tkachev, 2024. "Networks Under Deep Uncertainty," Springer Optimization and Its Applications, in: Ilias S. Kotsireas & Anna Nagurney & Panos M. Pardalos & Stefan Wolfgang Pickl & Chrysafis Vogiatzis (ed.), Dynamics of Disasters, pages 1-13, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-74006-0_1
    DOI: 10.1007/978-3-031-74006-0_1
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