Publication Date: December 2019
Beliefs are intuitive if they rely on associative memory, which can be described as a network of associations between events. A belief-theoretic characterization of the model is provided, its uniqueness properties are established, and the intersection with the Bayesian model is characterized. The formation of intuitive beliefs is modelled after machine learning, whereby the network is shaped by past experience via minimization of the diﬀerence from an objective probability distribution. The model is shown to accommodate correlation misperception, the conjunction fallacy, base-rate neglect/conservatism, etc.
Keywords: Beliefs, Intuition, Associative memory, Boltzmann machine, Energy-Based Neural Networks, Non-Bayesian updating
JEL Classification Codes: C45, D01, D90