Abstract
This piece explores the challenges of modelling contemporary complex systems, which are characterized by nonlinearity, feedback, adaptation, and emergence. It distinguishes complex systems from complicated ones using the Cynefin framework and highlights the limitations of traditional modeling approaches that rely on predefined structures and stable boundaries. A structural-semantic classification of modeling methods is proposed, emphasizing semantic substrate, structural commitment, and representational ontology. The Functional Resonance Analysis Method (FRAM) is introduced as a metamodel that focuses on functional dependencies and variability, enabling sensemaking under uncertainty. FRAM’s application in digital twinning is discussed, showcasing its ability to dynamically adapt to real-world system behaviour. The document concludes by advocating for diverse modelling methodologies to address the complexity of modern systems, with FRAM playing a pivotal role in modelling emergent and unpredictable behaviours.
From Complicated to Complex Systems
Across engineering, safety, healthcare, infrastructure, finance, and AI-enabled socio-technical domains, there is growing recognition that many systems of contemporary concern are complex rather than merely complicated. This distinction, articulated clearly in the Cynefin framework, (Figure 1), is not semantic but foundational (Snowden & Boone, 2007). Complicated systems may involve many parts, yet their behaviour remains largely decomposable, analysable, and predictable given sufficient expertise. Complex systems, by contrast, are characterised by nonlinearity, feedback, adaptation, and emergence; their behaviour cannot be reliably inferred from the properties of individual components alone (Cilliers, 1998; Mitchell, 2009).