An influence diagram is a decision structuring or modeling tool that graphically represents the relationship between decisions, uncertainties and outcomes, using nodes and arrows. It is similar to a mean-ends diagram, but with greater emphasis on documenting the full range of cause and effect relationships and clarifying uncertainties. They’re very flexible. You can build them at a conceptual level “on-the-fly”, to put some structure to complex discussions as they are taking place. Or they can be constructed more formally, as the product of a structured and in-depth workshop. It just depends on the context.

Influence diagrams emphasize the causal variables over which managers have some control, although other variables may also be represented. They can play an important role in defining evaluation criteria and in determining modeling and information needs directly related to the evaluation criteria. They facilitate communication among technical experts, decision makers and stakeholders about how a system works and what information is important in a decision. (When constructed under more formalized rules to describe inter-related conditional probabilities, influence diagrams become Bayesian Networks.

See the Tools section for more information on influence diagrams, including examples. Such diagrams serve several important functions in the deliberations of a multi-stakeholder group:

• 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, local or traditional knowledge, etc.)
• encouraging disciplined thinking about cause and effect relationships;
• being explicit about uncertainty, in particular, emphasizing the existence of competing hypotheses about what factors were most likely limiting fish, and facilitating informed debate about them;
• defining evaluation criteria;
• determining and prioritizing information needs directly related to the evaluation criteria;
• structuring quantitative models to estimate the consequences of options (especially when the influence diagram is constructed under more formalized rules to describe inter-related conditional probabilities);
• documenting the basis for and improving the transparency of expert judgments.

Key Ideas

• Influence diagrams show the cause and effect relationship between variables that can be controlled by managers
• Influence diagrams serve several purposes in the deliberations of a multi-stakeholder group