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An R-Based Landscape Validation of a Competing Risk Model
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Limits of Risk Predictability in a Cascading Alternating Renewal Process Model.

Xin Lin1,2, Alaa Moussawi1,3, Gyorgy Korniss1,3

  • 1Social and Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.

Scientific Reports
|July 29, 2017
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Summary

This study introduces the Cascading Alternating Renewal Process (CARP) to better predict catastrophic global risks by accounting for interconnectedness. The CARP model

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Area of Science:

  • Complex Systems Science
  • Risk Analysis
  • Computational Modeling

Background:

  • Traditional risk analysis models often underestimate catastrophic events due to overlooking risk interconnectivity.
  • Interdependence of global risks poses a significant challenge for accurate forecasting.
  • Lack of sufficient ground truth data hinders the assessment of predictive models for cascading events.

Purpose of the Study:

  • To propose and validate the Cascading Alternating Renewal Process (CARP) for forecasting interconnected global risks.
  • To establish a method for assessing prediction precision as a function of input data size.
  • To demonstrate the application of CARP on both simulated and real-world datasets.

Main Methods:

  • Development of the Cascading Alternating Renewal Process (CARP) model.
  • Generation of simulated ground truth data using CARP with known parameters.
  • Application of CARP to a simulated dataset of urban fires and a real-world global risk dataset.
  • Analysis of prediction precision as a function of data size.

Main Results:

  • Parameter recovery variance shows a power-law decay with increasing ground truth data length.
  • CARP demonstrates effective estimation on a disparate, real-world dataset with dependencies.
  • The study quantifies prediction precision based on data availability.

Conclusions:

  • The Cascading Alternating Renewal Process (CARP) is an efficient method for predicting catastrophic cascading events.
  • CARP has potential applications for analyzing emerging local and global interconnected risks.
  • The methodology provides a framework for evaluating model performance with limited ground truth data.