Probabilistic Knowledge Processing and Remaining Uncertainty

Information is indispensable in preparing economic de-cisions purposefully. In this paper knowledge is rep-resented by a probability distribution. Knowledge ac-quisition is realized by the principle of maximum en-tropy which guarantees an unsophisticated expansion of knowledge and provides an unbiased basis for decision processes. Under a mathematical point of view entropy measures remaining uncertainty and reduction of en-tropy is knowledge increase (first order uncertainty). In general, an actual epistemic state might vary due to fur-ther information. Thus, it is necessary to have an instru-ment which reflects also the potential of distribution’s possible variation. Therefore, we present the concept of so called second order uncertainty, which allows not only a ’look ’ on an epistemic state, as the entropy does, but also ’looks ’ into an epistemic state. First the argu-ments will be complemented by a short example and then they will be illustrated by a real world model in the field of business to business transactions. All calcula-tions will be supported by the expert system SPIRIT