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Managing Emerging Risks in Strategic Scenarios of Uncertainty and Complexity: A Theoretical Framework

  • Liliana CavatortaEmail author
  • Gianluca Rosso
Chapter
Part of the Anticipation Science book series (ANTISC, volume 4)

Abstract

In a context of accelerated change and interlinked systems we are facing the challenge to build a strategic planning that can be resilient in different future scenarios, in order to be prepared for emerging risks and opportunities. In this work we study an approach to manage emerging risks and opportunities for strategic purposes in conditions of uncertainty and complexity.

Frequently a classic risk management approach for assessment cannot be sufficient because probability models could be unsuccessful. It is necessary to recognize the uncertainty and complexity in emerging risks and to address both with suitable methodologies that allow a holistic view.

Emerging risks that involve complex systems and entangled cause-and-effect relationships require adequate methodologies that allow the assessment of the degree of exposure to risks and a holistic and integrated vision. In order to avoid a compartmental view and see the global picture, we need to overcome the focus on single parts. It’s necessary to evolve from bidimensional to tridimensional models introducing the concept of interconnections between each bidimensional structure. As we are exposed to risks with insufficient knowledge or imprecise data, a fuzzy approach can be useful. In our study we explore the combination of fuzzy logic with other models such as decision trees and artificial neural networks to propose a possible theoretical framework.

Keywords

Risk Change Scenario Emerging risk Uncertainty Framework Macrotrends Future Complexity Fuzzy Opportunities Decision trees Interconnections Strategic Entropy Entropy index Holistic Anticipate Integrated Multilayer Futures Strategic planning Resilient Forward-looking Neural networks Integrated vision 

Notes

Acknowledgments

The authors wish to thank Roberto Poli for his comments on previous versions of this paper.

They are also grateful to Sarah Doring for her suggestions and contributions.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Reputational and Emerging Risk ObservatoryUnipol GruppoBolognaItaly

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