The “Wisdom of Crowds” is powerful. It helps us understand what is likely. However, extremely unlikely data tails of a distribution are more difficult.

“Humans are not very good at probabilistic thinking. Kahneman and Tversky formulated Prospect Theory to explain how our intuition breaks down. Simply stated, humans are better at pattern recognition than probability distributions.”

A wide range of biases limits human understanding of rare events. Can crowd wisdom help explain rare events? Our work shows this is both possible and practical. This paper describes means to better understand rare events using models and estimates from groups; crowdsourcing distributions, not just specific forecasts. A brief survey of prior work and some original Lone Star research is presented.

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