Topic: Fat tails

Nassim Nicholas Taleb examines three technical problems in applied probability. First, he considers how errors in probabilities derived from fat-tailed survival functions can translate into disproportionately large, potentially unbounded errors in thresholds. He relates this to uncertainty in disease growth rates and forecasting. Second, he discusses what he regards as fundamental probability errors in psychology papers. Third, he examines how correlation and relative distances can mislead in the geometry of information, proposing heuristics for applying entropy-based methods to genetic dista

Nassim Nicholas Taleb examines distributions whose behaviour is dominated by extremes and tail events. He classifies these distributions and identifies circumstances in which familiar statistical tools become unreliable, including slow or problematic convergence of sample averages under the law of large numbers. The lecture questions the robustness of commonly used statistical procedures in fat-tailed settings, the reliability of frequency-based forecasting, and the use of past averages as guides to future outcomes. Taleb then develops implications for decision-making and the changes in metho

Nassim Nicholas Taleb develops two approaches to reasoning about extreme risk. In the first part, on the law of large numbers in the real world, he defines fat-tailed distributions and examines why conventional statistical procedures can perform poorly for economic variables. He stresses the much larger data requirements and the choice of estimators when rare observations dominate outcomes. The second part considers how to detect fragility in portfolios. Taleb describes fragility as sensitivity to volatility and argues for analysing the shape of an exposure and its response to shocks when rel