Hierarchical DSmP Transformation for Decision-Making under Uncertainty

Dempster-Shafer evidence theory is widely used for approximate reasoning under uncertainty; however, the decision-making is more intuitive and easy to justify when made in the probabilistic context. Thus the transformation to approximate a belief function into a probability measure is crucial and important for decision-making based on evidence theory framework. In this paper we present a new transformation of
any general basic belief assignment (bba) into a Bayesian belief assignment (or subjective probability measure) based on new proportional and hierarchical principle of uncertainty reduction. Some examples are provided to show the rationality and efficiency of our proposed probability transformation approach.