НЕЧЁТКО-МНОЖЕСТВЕННЫЙ АНАЛИЗ ОТРАСЛЕВОЙ ЭКОНОМИЧЕСКОЙ УСТОЙЧИВОСТИ

Abstract

The aim of this publication is to review the approaches undertaken by our scientific school for modeling industry economic resilience in the paradigm of resilience. For the purpose of analyzing industry resilience, we employ fuzzy-logical modeling of a super-system (system of systems) consisting of impact subsystems, economic subsystem, and decision subsystems, packaged into a single model container of a 7x6 strategic matrix. All indicators in the 7x6 matrix in the forecasting model are represented as arbitrary fuzzy numbers or linguistic variables. As a result of our review, we present the following main phases of modeling: construction of industry resilience indices, modeling of external impacts, modeling of a balanced system of indicators (BSI), modeling of risks/opportunities, and modeling of decisions. The following technologies based on fuzzy descriptions are utilized in our modeling; matrix aggregate computing, BSI with fuzzy connections, R-lens, comparison of fuzzy sequences, and others. An example of unfolding the 7x6 matrix is presented using four key indicators from the BSI. The discussed modeling approaches have been applied to nine selected industrial and agricultural sectors, resulting in corresponding analytical assessments of resilience. The COVID-19 pandemic is considered as a demonstration example of impact. The demonstrated approaches to modeling resilience can be arbitrarily expanded and deepened, with the perspective of incorporating more sophisticated mathematical objects obtained at the intersection of fuzzy and probability paradigms into the 7x6 matrix model. Such inclusion can be driven by differentiating various types of uncertainty, where in one case it is appropriate to conduct modeling using non-statistical probabilities (e.g., in Bayesian schemes), in another case by utilizing possibility measures, and in a third case by structuring hybrid descriptions such as fuzzy probabilities of Puri-Ralescu type.