Our research concerns the problem of explainable decision in a context of incomplete knowledge. We define a framework called Bipolar Layered Framework with Support and Weights (BLFSW) that represents the set of argument graphs that can be used in the domain, enabling us to compute what results can be obtained in the different decision situations. This framework also contains information about the utilities/disutilities of these tangible results. This paper extends Bipolar Layered Frameworks defined in [1] by enabling the expression of supports for decision principles and by giving the user the possibility to fix the strength of inhibitors and supports with weights. This increased expressiveness of the framework is important both for refining the evaluation of alternatives and to improve the compactness of the representation. The main result of this paper is to provide an automatic way to explain a possibilistic decision setting in terms of a BLFSW which makes explicit the principles that govern the decision.