Walking “randomly”: how to tract intractable FRAM models

The Functional Resonance Analysis Method (FRAM) represents a well-established approach to capture the socio-technical complexity of modern systems. However, a FRAM model that depicts a highly complex system risks to become complicated in itself. These FRAM models may suffer from limited tractability, i.e., they become hard to manage as analysts are expected to segment and deconstruct the model to reach a sustainable level of understanding. As a result, a reductionist path is often taken to analyze complex problems, which sounds paradoxical. This proposal aims to discuss a novel approach to tackle the complexity of FRAM representations without resorting to reductionism. The approach is grounded on the principles of network theory, starting from the assumption that an instantiation of a FRAM model could be represented by a directed graph of nodes (i.e., functions) connected with each other via specific rule-based edges (i.e., aspects). Once conceptualized the graph, the authors developed a Functional Random Walker (FRW), i.e., a mathematical construct capable of modelling information flowing throughout the functions of the socio-technical system. This proposal aims to unveil how the novel FRW moves “randomly” within the FRAM model, still preserving the characterization of functional dependencies. As such, the definition of FRW accounts for the variability being transferred inside the system from one function to another, in turn exploiting its full characterization. By observing the steady state of the FRWs, it is also possible to obtain metrics such as the probability of finding the FRW at a certain position, or the “distance” the FRW must complete to move from one function to another. These measures are directly related to the description of selected properties of the FRAM model. A simple walk-through application of the FRW will be presented to show how this concept may help managing complex FRAM models. Overall, the use of FRW helps assessing complex systems strengths and vulnerabilities, unveiling knowledge otherwise hidden in a cluttered FRAM model.