Extended Influence Diagram Research Articles

Architecture Analysis Method Study Based on EID

Extended Influence Diagram (EID) is a graph model for modeling and solving decision problems under uncertainty, extended from the Influence Diagram. Architecture analysis is one of the main contents of architecture lifecycle. This paper introduces the modeling elements and architecture analysis process of EID, specifics probability elicitation procedure of directly controllable node according to DM2, discusses architecture maintainability by EID, finally summarizes this method's advantages and disadvantages.

Extended Influence Diagrams for System Quality Analysis

Making major changes in enterprise information systems, such as large IT-investments, often have a significant impact on business operations. Moreover, when deliberating which IT-changes to make, the consequences of choosing a certain scenario may be difficult to grasp. One way to ascertain the quality of IT investment decisions is through the use of methods from decision theory. This paper proposes the use of one such method to facilitate IT-investment decision making, viz. extended influence diagrams. An extended influence diagram is a tool able to completely describe and analyse a decision situation. The applicability of extended influence diagrams is demonstrated at the end of the paper by using an extended influence diagram in combination with the ISO/IEC 9126 software quality characteristics and metrics as means to assist a decision maker in a decision regarding an IT-investment.

Structuring Conditional Relationships in Influence Diagrams

An influence diagram is a graphical representation of a decision problem that is at once a formal description of a decision problem that can be treated by computers and a representation that is easily understood by decision makers who may be unskilled in the art of complex probabilistic modeling. The power of an influence diagram, both as an analysis tool and a communication tool, lies in its ability to concisely summarize the structure of a decision problem. However, when confronted with highly asymmetric problems in which particular acts or events lead to very different possibilities, many analysts prefer decision trees to influence diagrams. In this paper, we extend the definition of an influence diagram by introducing a new representation for its conditional probability distributions. This extended influence diagram representation, combining elements of the decision tree and influence diagram representations, allows one to clearly and efficiently represent asymmetric decision problems and provides an attractive alternative to both the decision tree and conventional influence diagram representations.