Failure dynamics of the global risk network

Area of Science:

• Complex systems science
• Risk analysis
• Network theory

Background:

• Modern societies face complex, interconnected risks across various domains (environmental, economic, technological, geopolitical, social).
• Understanding the interdependencies and cascading effects of these risks is crucial for crisis management and societal stability.
• Existing knowledge on how risk materializations in distinct domains influence each other is limited.

Purpose of the Study:

• To develop a quantitative model of global risk network dynamics.
• To analyze the influence and interconnectedness of diverse risks, including those difficult to quantify.
• To identify key risk properties such as contagion potential and detrimental impacts on system stability.

Main Methods:

• Utilizing expert assessments of risk likelihoods and influences.
• Developing a quantitative network model incorporating these expert assessments.
• Employing maximum likelihood estimation to optimize model parameters.
• Analyzing model dynamics, resilience, and stability.

Main Results:

• The model incorporating network effects significantly outperforms models without them, highlighting the value of expert data.
• Identified key risk properties including contagion potential, persistence, and roles in failure cascades.
• Determined specific risks most detrimental to overall system stability.
• The model provides quantitative measures for risk interdependencies and materialization effects.

Conclusions:

• Risk materializations are interconnected, forming a network that significantly impacts societal crises.
• The developed quantitative model effectively captures these complex risk dynamics and interdependencies.
• Findings offer crucial insights into systemic risk, stability, and the identification of critical vulnerabilities.