Quantum Probability Explanations for Probability Judgment ‘Errors’

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. Over 30 years ago, Kahneman and Tversky [1] began their influential program of research to discover the heuristics and biases that form the basis of human probability judgments. Since that time, a great deal of new and challenging empirical phenomena have been discovered including conjunction, disjunction, conditional, inverse, and base rate fallacies [2]. Although heuristic concepts (such as representativeness and availability) initially served as a guide to researchers in this area, there is a growing need to move beyond these intuitions, and develop more coherent, comprehensive, and deductive theoretical explanations [3]. The purpose of this article is to propose a new way of understanding human probability judgment using quantum probability principles [4]. Quantum principles have been used recently in a number of psychological applications including perception [5], conceptual structure [6], information retrieval [7], and human judgments [8]. There is another independent line of research that uses quantum physical models of the brain to understand consciousness [9] and human memory [10]. We are not following this line,