A Negation Quantum Decision Model to Predict the Interference Effect in Categorization
This work addresses a specific problem in cognitive science and decision-making by modeling interference effects, but it is incremental as it builds on existing quantum decision models with a negation-based enhancement.
The authors tackled the problem of predicting interference effects in categorization, which violate classical probability principles, by developing a negation quantum decision model (NQ model) that combines negation of probability distributions with quantum decision theory. The results show that the NQ model closely matches real experimental data and has less error than existing models.
Categorization is a significant task in decision-making, which is a key part of human behavior. An interference effect is caused by categorization in some cases, which breaks the total probability principle. A negation quantum model (NQ model) is developed in this article to predict the interference. Taking the advantage of negation to bring more information in the distribution from a different perspective, the proposed model is a combination of the negation of a probability distribution and the quantum decision model. Information of the phase contained in quantum probability and the special calculation method to it can easily represented the interference effect. The results of the proposed NQ model is closely to the real experiment data and has less error than the existed models.