In this paper we develop an organizational model of uncertainty handling based on an information processing model. In order to describe effects of organizing strategies, which an organization may employ to cope with its external uncertainty, variables of internal organization are identified and integrated into two indices. These indices are a level of department activity and an extent of programmed activity.

We employ simulation in order to illustrate the effects of organizing strategies by the indices. The organizing strategies are basically composed in relation to a transition to a divisional organization from a functional organization.

In conclusion, we can successfully illustrate the effects of uncertainty handling of organization by the model.

Our group has been developing a DSS generator, which is called actDSS(III). Its model management system(MMS) is aimed to achieve the goal that a user can build and manipulate models freely by himself. The MMS generates two types of internal representations from a model written in the model description language of it. One is a network form representation of referential relations among variables in the model, that is, the causal structure of the model. The other is a representation of calculation process of the model. The MMS handles models using these internal representations.

This paper, in order to develop a formal theory of MMS, describes the model description language of the MMS and how a model described with it is processed in model theoretic way. Especially, desirable properties of the MMS are deduced through focusing the causality structure of a model. Finally, its implementation on actDSS(III) is discussed.

This paper discusses the Bayesian decision-making problem associated with the entropy of the elements in fuzzy states, considering the states constructed in fuzzy events. The fuzzy decision-making problem deals simultaneously with both fuzziness and randomness by decision theory. The entropy of the elements in fuzzy states is defined to solve the decision-making problem with fuzzy states. By applying the entropy of the elements in fuzzy states, the relationships between randomness and fuzziness are analyzed.

The error discrimination probability in fuzzy states doesn't converge to zero owing to fuzziness, if we let observation times of information infinity. And then we defined the difference D(X(t)) which applied the entropy of the elements in fuzzy states unlike before and obtained new instruction on the observation times of information.

In these days network merits of social and economic networks are recognized and discussed widely. In this paper we analyze network merits and its decision making structure focusing on the successive improvement and evolution of alternatives. We make a model of the network decision making by using genetic algorithm.

We also analyze three actual cases of network decision making such as Japanese Keiretsu among subcontract companies and its parent company, franchise shops network and software R & D under an information network like the Internet. We make a model of network DM for simple cases by using genetic algorithm and analyze the model by simulation.