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Tyranny of Uncertainty

- A New Framework to Predict, Remediate and Monitor Risk

Über Tyranny of Uncertainty

The authors offer a revolutionary solution to risk management. It¿s the unknown risks that keep leaders awake at night¿wondering how to prepare for and steer their organization clear from that which they cannot predict. Businesses, governments and regulatory bodies dedicate endless amounts of time and resources to the task of risk management, but every leader knows that the biggest threats will come from some new chain of events or unexpected surprises¿none of which will be predicted using conventional wisdom or current risk management technologies and so management will be caught completely off guard when the next crisis hits. By adopting a scientific approach to risk management, we can escape the limited and historical view of experience and statistical based risk management models to expose dynamic complexity risks and prepare for new and never experienced events.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9783662491034
  • Einband:
  • Gebundene Ausgabe
  • Seitenzahl:
  • 373
  • Veröffentlicht:
  • 18. Mai 2016
  • Ausgabe:
  • 12016
  • Abmessungen:
  • 166x288x29 mm.
  • Gewicht:
  • 756 g.
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Beschreibung von Tyranny of Uncertainty

The authors offer a revolutionary solution to risk management. It¿s the unknown risks that keep leaders awake at night¿wondering how to prepare for and steer their organization clear from that which they cannot predict. Businesses, governments and regulatory bodies dedicate endless amounts of time and resources to the task of risk management, but every leader knows that the biggest threats will come from some new chain of events or unexpected surprises¿none of which will be predicted using conventional wisdom or current risk management technologies and so management will be caught completely off guard when the next crisis hits. By adopting a scientific approach to risk management, we can escape the limited and historical view of experience and statistical based risk management models to expose dynamic complexity risks and prepare for new and never experienced events.

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