  Smets proposes the Pignistic Probability Transformation (PPT) as the decision
layer in the Transferable Belief Model (TBM), which argues when there is no
more information, we have to make a decision using a Probability Mass Function
(PMF). In this paper, the Belief Evolution Network (BEN) and the full causality
function are proposed by introducing causality in Hierarchical Hypothesis Space
(HHS). Based on BEN, we interpret the PPT from an information fusion view and
propose a new Probability Transformation (PT) method called Full Causality
Probability Transformation (FCPT), which has better performance under
Bi-Criteria evaluation. Besides, we heuristically propose a new probability
fusion method based on FCPT. Compared with Dempster Rule of Combination (DRC),
the proposed method has more reasonable result when fusing same evidence.
