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Comparing Classical and Quantum Probability Accounts of the Interference Effect in Decision Making

Authors
Dr. Christopher Fisher
Parallax Advanced Research
Dr. Lorraine Borghetti
Air Force Research Labratory ~ 711 HPW/RHWOH
Prof. Joe Houpt
University of Texas at San Antonio ~ Psychology
Christopher Adam Stevens
Air Force Research Laboratory
Dr. Leslie Blaha
Air Force Office of Scientific Research
Abstract

Prior research has found interference effects (IEs) in decision making, which violate classical probability theory (CPT). We developed a model of IEs called the probability theory + noise (PTN) model and compare its predictions to an existing quantum model called the Belief-Action Entanglement (BAE) model. The PTN assumes that memory operates consistently with CPT, but noise in the retrieval process produces violations of CPT. Using parameter space partitioning, we identified that both models can produce all qualitative patterns of IEs. We found that the BAE tends to produce IE distributions with a larger variance compared to the PTN. We also show that PTN predicts a relationship we term the conditional attack probability equality (CAEP) which is violated in previously reported data. The CAEP holds for the PTN regardless of chosen parameter values. However, the BAE is not constrained by the CAEP.

Tags

Keywords

Quantum Cognition
Interference Effect
Probability Theory
Decision Making
Discussion
New
CAEP question follow up Last updated 1 year ago

Dear Dr.Fisher, Just to make sure I understand correctly, in classical probability, we have Pr xd (a| tq, z) = Pr cd (a| tq, z), does this sounds right? If so, I am sure that most of the classical probability with noisy sampling model (including Bayesian sampler) can not explain a statistically significant violation to this equality. We need somet...

Mr. Adam Huang 0 comments
Cite this as:

Fisher, C. R., Borghetti, L., Houpt, J., Stevens, C., & Blaha, L. (2023, June). Comparing Classical and Quantum Probability Accounts of the Interference Effect in Decision Making. Paper presented at Virtual MathPsych/ICCM 2023. Via mathpsych.org/presentation/1270.