Assessing Catastrophes-Dragon-Kings, Black, and Gray Swans-for Science-Policy

Area of Science:

• Complex systems science
• Risk analysis
• Decision theory

Background:

• Catastrophic incidents, from nonroutine to extreme events like Dragon-Kings (DK), Black Swans (BS), and Gray Swans, necessitate precautionary measures.
• These initiatives often face public resistance or post-event criticism, highlighting a gap in understanding and prediction.

Purpose of the Study:

• To develop a framework for analyzing catastrophic events by examining their generating mechanisms and consequence distributions.
• To aggregate expert opinions on assumptions, mechanisms, and consequences to inform science-policy decisions.
• To improve the prediction and management of extreme events.

Main Methods:

• Causal analysis incorporating nonlinear behaviors and power-law distributions.
• Modeling of self-organizing criticality, self-similarity, and feedback loops.
• Aggregation of divergent expert beliefs using novel methods to address paradoxes in majority rule.

Main Results:

• Catastrophic event consequences exhibit longer and fatter right tails than traditional failure analyses predict.
• Dragon-Kings (DK) are extreme events with higher-than-expected frequencies and are predictable, unlike Black Swans (BS).
• Identified precursor signals ('snaps, crackles, and pops') from feedback couplings.

Conclusions:

• Causal analysis must account for nonlinear dynamics, power-law distributions, and complex system properties.
• The study provides a method for combining expert opinions on extreme events, improving science-policy integration.
• Predictive models for certain extreme events, like DK, can be developed, offering a departure from the unpredictability of BS.