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How people estimate the probabilities of unique events

By Sunny

On 05, Jun 2013 | In | By Sunny

How people estimate the probabilities of unique events

We developed dual-process theory of how individuals estimate the probabilities of unique events, such as Hillary Clinton becoming US President. It postulates that uncertainty is a guide to improbability.

An intuitive system 1 simulates evidence in mental models, and forms analog, non-numerical representations of the magnitude of degrees of belief. This system has minimal computational power, and combines evidence using a small repertoire of primitive operations. It resolves the uncertainty of divergent evidence for a single event, for a conjunction of events, and for an inclusive disjunction of events, by taking a primitive average of probabilities. A deliberative system 2 maps icons into numerical probabilities. With access to working memory, it carries out arithmetical operations in combining numerical estimates.  Experiments corroborated the theory and its computer implementation.  Participants concurred in estimates of real possibilities.  They violated the complete joint probability distribution in the predicted ways, when they made estimates about conjunctions: P(A), P(B), P(A and B), disjunctions:  P(A), P(B), P(A or B or both), and conditional probabilities P(A), P(B), P(B|A).   They were faster to estimate the probabilities of compound propositions when they have already estimated the probabilities of each of their components.


Supplementary materials
  • Khemlani, S., Lotstein, M., & Johnson-Laird, P.N. (2015). Naive probability: Model-based estimates of unique events. Cognitive Science. (pdf)
  • Materials for Experiments 1, 2, and 3 in “Naive probability: Model-based estimates of unique events” by Khemlani, Lotstein, & Johnson-Laird (2015) (pdf)