A SET-THEORETIC APPROACH TO MODELING CASCADING DERIVATIVE RISKS IN SOCIO-TECHNICAL SYSTEMS

Abstract

The article addresses a pressing scientific and applied problem: modeling complex hierarchical relationships among risk factors arising in the operation of high-tech, socially oriented systems. The object of the study is a network of sorting stations, considered as a complex dynamic system with distributed business processes. The relevance of the research is driven by the high level of turbulence in the external environment and the need to move from qualitative descriptions of risks to their digital formalization and quantitative measurement. The proposed approach is grounded in systems analysis, which enabled a multi-level decomposition of the organizational structure into subsystems of financial planning, logistics, sales, and marketing. Set theory was employed as the mathematical framework to describe the interactions among these subsystems. This made it possible to represent the cascading development of risks as a sequence of system states in which the emergence of a primary threat (funding shortfall) initiates a set of derivative risks, ranging from technological degradation of sorting lines to the loss of intellectual capital. The scientific novelty of the work lies in the further development of set-theoretic membership models that establish logical relationships between the causes and consequences of critical situations in a format suitable for automated processing. For the first time, an algorithm for the quantitative assessment of cascading impacts has been proposed through an integral indicator of Expected Risk Value (ERV), based on a combination of probabilistic characteristics and degrees of influence on the system’s target performance indicators. The practical significance of the research is realized in the form of a strategic management map-scheme representing a set of algorithmized response strategies (avoidance, mitigation, acceptance). The proposed measures integrate both managerial decisions and technical-technological innovations, including the use of robotic systems to automate sorting processes. This reduces critical dependence on the human factor and minimizes operational risks. The application of the developed models creates a mathematical foundation for the design of intelligent Decision Support Systems (DSS) capable of predictive monitoring of complex systems and the automatic generation of cascading threat neutralization scenarios. The use of such digital tools ensures not only the short-term stabilization of business entities but also creates conditions for their sustainable development and enhanced competitiveness in the context of the economy’s digital transformation.