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

The threat of catastrophic incidents—from nonroutine events to extreme ones, such as Dragon‐Kings (DK), Black Swans (BS), and Gray Swans—induces precautionary initiatives that, before the fact, may encounter public resistance or after the fact recriminations. This study develops three aspects of these events: (1) generating mechanisms, (2) the statistical distributions of near and far‐term consequences, and (3) the aggregation of expert opinions about assumptions, mechanisms, and consequences that informs science‐policy. This study shows how causal analysis should account for the: (1) nonlinear catastrophic behaviors that generate predictions, (2) common and power‐law distributions of the consequences, (3) self‐organizing criticality and self‐similarity, and (4) feedbacks and couplings between mechanisms that produce snaps, crackles, and pops as precursor, warning signals. The distribution of the consequences associated with catastrophic incidents has longer and fatter right tails than those expected from failure analysis based on known nonroutine events. DK are extreme events that deviate from these fat tail distributions, have a much higher frequency than expected, and can be predicted unlike BS. This shows how to combine divergent expert individual beliefs over assumptions, causation, and results, and a paradox that affects agreements obtained by majority rule.