The transferable belief model for quantified belief representation

The transferable belief model is a model to represent quantified beliefs based on the use of belief functions, as initially proposed by Shafer. It is developed independently from any underlying related probability model. We summarize our interpretation of the model and present several recent results that characterize the model. We show how rational decision must be made when beliefs are represented by belief functions. We explain the origin of the two Dempster's rules that underlie the dynamic of the model through the concept of specialization and least commitment. We present the canonical decomposition of any belief functions, and discover the concept of 'debt of beliefs'. We also present the generalization of the Bayesian Theorem to belief functions.