A Systems Theory-Based Framework for Uncertainty in Complex System Governance

Complex systems are all around. A complex system is a system whose collective behavior is more than the behavior exhibited by the individual parts. Such a system is characterized by an exponential increase in information, complexity, ambiguity, emergence, and high levels of uncertainty (Jaradat, 2015). Uncertainty is intrinsic to complex systems. Uncertainty refers to epistemic situations involving imperfect or unknown information. Uncertainty can refer to the chance of certain anticipated outcomes occurring, or it can refer to the unknown events that are not foreseeable as to when or if one might occur. It applies to predictions of future events, to physical measurements that are already made, or to the unknown. Uncertainty appears in many fields, including insurance, philosophy, physics, economics, finance, medicine, psychology, sociology, meteorology, ecology, and information science, to name a few. The purpose of this research was to develop and demonstrate a systems theory-based framework for uncertainty in Complex System Governance (CSG) using an inductive research design. The research was intended to help frame uncertainty’s role in the context of CSG. There are two questions this research addressed: 1. What Systems theory-based framework can be developed for uncertainty in CSG? and 2. What results from applying the framework to an operational setting? This qualitative research used an inductive research design. Literature on radical or extreme uncertainty was collected and analyzed following a constructivist grounded theory method using systems theory propositions as sensitizing concepts. This framework provided a lens through which to understand uncertainty and its role in CSG. The framework was then applied to a single case to demonstrate the framework’s utility. The resulting framework is an original contribution to the body of knowledge in two significant ways. First, the literature review of uncertainty provides a structure to a fragmented and diverse body of knowledge for uncertainty. Second, it provides a theoretical framework of uncertainty in CSG, an area lacking in the CSG body of knowledge. Additionally, the research provides insights into possibilities for further deployment to improve CSG practices and suggests corresponding methods, tools, and techniques that might be developed to propagate the CSG field.