Exploring Complexity: A Framework for Cross-Domain Complex System Mapping

Complex systems theory is a rapidly expanding field, yet its cross-domain application remains limited. We propose an analogy-based analytical framework for studying complex systems consisting of three stages: (i) decomposition and simplification of systems to their essential mid-scale processes, (ii) reconstruction involving the reintroduction of feedback loops, in depth analysis of variables followed by extension; and (iii) extrapolation to small scale processes and large scale emergent behavior simulations. We apply this method to an interdisciplinary pair of case studies: the impact of artificial intelligence on the workforce and community dynamics of competing barnacles and their predator with the main aim of determining the conditions under which concepts, analytical methods, tools or dynamical patterns can be meaningfully transferred from economics to marine biology. Our results suggest that this is possible as long as the systems share a comparable mid-scale chronological sequence of elemental interactions. This shared backbone leads to a common parameter space through which both system-specific emergent behaviours can be explored, while still preserving the distinct complexity fingerprint of each domain. Similarly, we find several role-based pair elements that represent different concepts but have the same behaviour-defining role in the two distinct systems.