A quantum dynamic belief model to explain the interference effects of categorization on decision making
This work addresses a specific problem in cognitive science and decision theory for researchers studying categorization effects, but it is incremental as it builds on existing quantum probability models.
The authors tackled the problem of predicting interference effects of categorization on decision making, where the law of total probability is violated, by proposing a quantum dynamic belief model that incorporates uncertainty to simulate human thinking; the model was applied to an experiment and shown to be more succinct and effective compared to other models.
Categorization is necessary for many decision making tasks. However, the categorization process may interfere the decision making result and the law of total probability can be violated in some situations. To predict the interference effect of categorization, some model based on quantum probability has been proposed. In this paper, a new quantum dynamic belief (QDB) model is proposed. Considering the precise decision may not be made during the process, the concept of uncertainty is introduced in our model to simulate real human thinking process. Then the interference effect categorization can be predicted by handling the uncertain information. The proposed model is applied to a categorization decision-making experiment to explain the interference effect of categorization. Compared with other models, our model is relatively more succinct and the result shows the correctness and effectiveness of our model.