lowprobabilityhighimpact

Low-probability high-impact (LPHI) events are events with a small probability of occurring but consequences that are disproportionately large relative to the likelihood. The term is used in risk management, finance, engineering, and policy analysis to describe tail risks that are not well captured by standard models. In some literature they overlap with the idea of black swan events, though black swans are sometimes defined as unpredictable and beyond historical experience. The term lowprobabilityhighimpact is used in some writings as a compact label for this concept.

Because such events lie in the tails of probability distributions, they can dominate risk assessments even when their probability is low. Traditional models that assume normal or near-normal distributions tend to underestimate tail risk. As a result, organizations use scenario analysis, stress testing, and robust decision-making to prepare for LPHI events. The concept also informs the distinction between risk management (mitigating expected losses) and resilience (keeping functioning after a shock).

Historical examples include major financial crises, large-scale natural disasters, or geopolitical shocks that were unlikely but produced outsized damage. In science and engineering, rare but catastrophic failures, such as structural collapses under extreme loads or asteroid impacts, are analyzed through worst-case and contingency planning. In modern risk discourse, LPHI events receive attention in cyber security, climate risk, and global health because of potential cascading effects.

Management responses include diversification, redundancy, fail-safes, insurance, and investment in monitoring and early warning. Governance approaches emphasize risk budgeting, red teaming, and scenario planning, while researchers continue to study heavy-tailed distributions and limits of probabilistic forecasting. Critics caution that overemphasis on extreme events can divert resources from more probable risks.