  A quantum probability model is introduced and used to explain human
probability judgment errors including the conjunction, disjunction, inverse,
and conditional fallacies, as well as unpacking effects and partitioning
effects. Quantum probability theory is a general and coherent theory based on a
set of (von Neumann) axioms which relax some of the constraints underlying
classic (Kolmogorov) probability theory. The quantum model is compared and
contrasted with other competing explanations for these judgment errors
including the representativeness heuristic, the averaging model, and a memory
retrieval model for probability judgments. The quantum model also provides ways
to extend Bayesian, fuzzy set, and fuzzy trace theories. We conclude that
quantum information processing principles provide a viable and promising new
way to understand human judgment and reasoning.
