Influence diagrams represent relationships between variables that reflect an analyst's or decision maker's knowledge about a problem. Their construction involves collaboration between an analyst and decision maker, representing an exercise in knowledge acquisition where the analyst attempts to build a model that reflects the decision maker's understanding of the domain. Influence diagrams are useful for building common understanding, facilitating communication, integrating different sources of knowledge, and structuring subsequent quantitative modeling.

1. Conclusion:
-Influence diagrams represent the relationships
between variables. These relationships are important
because they reflect the analyst's, or the decision
maker’s, knowledge about a problem.
-The construction of such a model often involves
collaboration between an analyst and the decision
maker.
-This collaboration represents an exercise in
knowledge acquisition — the analyst
attempts to construct a model that reflects the
decision maker’s understanding of
the problem domain.

2. THE USAGE OF INFLUENCE DIAGRAM
• building a common understanding of “how things work”;
• facilitating communication among technical experts, decision makers and
stakeholders;
• integrating knowledge from different sources in decision making (e.g.,
science, TEK, etc.);
• encouraging disciplined thinking about cause and effect relationships;
• being explicit about uncertainty, in particular, emphasizing the existence of
competing hypotheses and facilitating informed debate about them;
• defining evaluation criteria;
• determining modeling and information needs directly related to the
evaluation criteria;
• structuring subsequent quantitative modeling (especially when constructed
under more formalized rules to describe inter-related conditional
probabilities);
• documenting the basis for and improving the transparency of expert
judgments

3. Influence diagrams are particularly helpful
• when problems have a high degree of conditional
independence,
• when compact representation of extremely large
models is needed,
• when communication of the probabilistic
relationships is important, or
• when the analysis requires extensive Bayesian
updating