Causal reasoning is the ability to make inferences by using the relationship between a cause and its effect. Because MFM decomposes a complex system in both means-end and whole-part dimensions, the cause-effect in both dimensions are considered. MFM constructs the model by using building blocks that correspond to functions and goals. It describes energy and mass flow structures in a physical system with different levels of decompositions and provides an abstract representation which is independent of individual components in the physical system.
Reasoning in MFM models is based on dependency relations between states of targets, threats and functions. Each function in an MFM model can be either enabled or disabled. For any enabled functions, the qualitative states (e.g. high, normal or low) can be evaluated based on process parameters. Given a known or assumed state of an MFM function, the MFM model can be used to reason about the other function states when the functions are causally connected in the model.
In MFM models, the causal reasoning starts from one hypothesis about a function state in the model. The hypothesis will generate new hypotheses based on MFM patterns through both causal relations and means-end relations. In this way, a hypothesis tree can be built by searching through all MFM patterns in the model and generating propositions along the procedure.
See how causal reasoning is used in this example.