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Updated: Mar 21, 2026

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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How damage diversification can reduce systemic risk.

Rebekka Burkholz1, Antonios Garas1, Frank Schweitzer1

  • 1ETH Zurich, Chair of Systems Design, Weinbergstrasse 56/58, 8092 Zurich, Switzerland.

Physical Review. E
|May 14, 2016
PubMed
Summary
This summary is machine-generated.

Risk diversification strategies significantly reduce systemic risk in complex networks. Damage diversification (DD) is more effective than exposure diversification (ED) at preventing cascading failures by limiting losses from node failures.

Related Experiment Videos

Last Updated: Mar 21, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Area of Science:

  • Network Science
  • Risk Management
  • Complex Systems

Background:

  • Cascading failures in weighted complex networks pose significant systemic risks.
  • Diversification strategies are crucial for mitigating these risks.
  • Understanding the impact of node failures on network stability is essential.

Purpose of the Study:

  • To investigate the influence of risk diversification on cascading failures in weighted complex networks.
  • To compare the effectiveness of exposure diversification (ED) and damage diversification (DD) strategies.
  • To analyze the role of node degree in failure cascades and amplification.

Main Methods:

  • Development of a branching process approximation to quantify cascade size.
  • Incorporation of node-specific properties (exposed and failing) into loss calculations.
  • Mesoscopic analysis of node failure risk based on degree.

Main Results:

  • Diversification strategies can significantly reduce systemic risk through topological adjustments.
  • Damage diversification (DD) effectively mitigates cascade amplification by reducing losses from high-degree node failures.
  • Node degree plays a critical role in cascade amplification and failure risk.

Conclusions:

  • Risk diversification, particularly damage diversification, is a powerful tool for enhancing network resilience.
  • The developed branching process model provides an accurate analytic extension for cascade analysis.
  • Systemic and mesoscopic perspectives are complementary for understanding complex network failures.