Background: Artificial Intelligence theories of model-based analogy in conceptual design have led to a methodology and a language for building functional and causal models of complex systems called Structure-Behavior-Function (SBF) models. An SBF model of a system explicitly represents its structure [S] (i.e., its configuration of components and connections), its functions (F) (i.e., its output behaviors), and internal causal behaviors [B] (i.e. its causal processes that compose the functions of the components into the functions of the system). The SBF language provides a vocabulary for expressing and organizing knowledge in a hierarchy, which captures functionality and causality at multiple levels of aggregation and abstraction. Empirical research in the Learning Sciences using the SBF methodology have led to substantial evidence that while experts model a complex system in terms of its structure, behaviors and functions, novices express primarily the structure of the system, demonstrate minimal understanding of its functions, and largely miss its behaviors.
