We are today flooded with information from various sources. The permanent uncertainty and imprecision of the information however forces us daily to live with their fuzziness and to handle them in their fuzzy state. Without applying precise mathematical methods, based on the classical logic, we would be condemned to vicious situations. Therefore it is mandatory to consider many possible scenarios, to optimize principles of evaluation of the concepts of equilibrium points and points of stability. This is the reason why the theory of the Fuzzy Logic originated.

In the subsequent applications of the Fuzzy Logic, practical economic, social, political ,daily decisions will be analyzed on the base of a simple case of fuzzy logic - linear fuzzy logic (LFS). In the information aspect it leads to the Linear Partial Information (LPI).

The theory of Linear Partial Information (LPI) belongs to the, so called, soft modelling. In comparison to the Fuzzy Sets theory /12/ ^*) the LPI-fuzziness is algorithmically more simple and particularly in decision making, more practically oriented. Instead of often dubious membership functions, the decision maker liberalises any fuzziness by establishing linear restrictions for fuzzy probability distributions or normalized weights (stochastic or non-stochastic LPI).

In the decision aspect the axiomatic based MaxWmin-principle (Maximization of the minimal weighted sums), MaxEmin (Maximization of the minimal expected value) and PDP (Prognostic Decision Principle), the last by taking into account the risk readiness of the decision maker, are applied. According to the linearity of the LPI-fuzziness only the extreme points of the corresponding LPI-convex polyhedrons are considered.

The introduced considerations of fuzzy equilibrium and stability are important for the diminution of classic mistake decisions. Depending on the decision principle the concepts of MaxEmin-, MaxWmin- and PDP-stability are analyzed. The Ultra- and Multi-stability under LPI-conditions are explained on some examples. On the area of multi-stage fuzzy decisions on the base of the Roll back procedure, the adaptive stability with respect of learning and regulation aspects are investigated. The instability is interpreted as a violation of the given stability interval. Removing this interval, is often connected with a profit-and-loss procedure. In a situation with multiple objectives, decision making under partial information the LPI-weighting method and stability problems will be analyzed. Finally some practical applications are presented.