Dynamical Markov decision-making model based on mass function to quantitatively predict interference effects

Abstract

Experimental results demonstrate that the law of total probability which is used to manage probabilities of a number of decision stages, is violated when interference effects occur in the decision process. Although some attempts have been made to predict interference effects, these studies have only been able to do so for certain data while failing to do so for others in the same experiment. With the help of C-D experiment and D experiment, this paper develops a dynamical Markov decision-making model based on mass function to quantitatively predict the interference effects. This model employs both the mass function and discount coefficient to generate distribution of initial state. A transition matrix based on the characteristics of unitary matrix is then generated, which is capable of realizing both transition between adjacent states as well as constraining variation interval of the discount coefficient. Next, this model quantifies the difference between the two experimental results obtained through a probability transformation to predict interference effects. Finally, our proposed model is applied to existing dataset with the results indicating that our model can process all existing data associated with the experiments, as compared to other models.