An ontology for the Functional Resonance Analysis Method (FRAM) would serve as a structured representation of the key concepts, relationships, and rules that underpin this powerful methodology for analyzing and modeling complex adaptive systems. It would be a critical tool for creating a shared understanding of this unique approach to analyzing and managing complexity in everyday systems. In FRAM, the ontology should go beyond being a shared vocabulary; it can become a framework for mapping the intricate web of interactions within systems, enabling us to grasp their emergent behaviour and variability.
FRAM is built on the premise that complex systems are defined by their dynamic interactions, inherent variability, and emergent outcomes. Hollnagel maintains that a FRAM-built model can be manipulated formally and correctly as a production system, defined as:
“A production system (or production rule system) is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behaviour, but it also includes the mechanism necessary to follow those rules as the system responds to states of the world.”
It has recently been suggested that a FRAM-built model could also be described as a collection of linked interdependent Turing machines. With this in mind, a FRAM ontology should formalize these elements, offering a way to describe the system in terms of functions, the couplings between those functions, and the variability that naturally arises within and across them. This structure provides a foundation for exploring how individual elements of a system combine to produce outcomes—sometimes unexpected, sometimes resilient, and sometimes catastrophic.
At its heart, a FRAM ontology is a layered framework. At the macro-level, it captures the overarching dynamics of the system, focusing on emergent behaviours that arise from the interplay of functions. At the meso-level, it identifies clusters of interconnected functions and couplings that reveal critical pathways and interactions. At the micro-level, it dives into the specific properties (metadata) and internal behaviour of individual functions, detailing their inputs, outputs, and dependencies. This hierarchy allows for zooming in and out, offering both a granular and holistic perspective of the system.