COMPREHENSIVE FUZZY-LOGIC MODEL FOR ASSESSING THE LEVEL OF EXCELLENCE OF PROTECTION OF SOCIO-ECONOMIC SYSTEMS

Abstract

This paper presents a comprehensive fuzzy-logical model for assessing Level of Protection Mechanism Maturity (LPMM) of socio-economic systems under conditions of high uncertainty, wartime pressures and accelerated transformation. The approach builds on Mamdani-type fuzzy inference and formalizes heterogeneous economic and institutional signals as linguistic variables in order to reduce information loss and to avoid strong distributional assumptions. The construct includes three input dimensions selected for their integrative role in the protective mechanism: Financial Stability (FS), Level of Adaptability to External Threats (LETA), and Social and Institutional Security (SIS), which jointly determine an integral output indicator LPMM. Each input variable is described by two terms (low/high) and mapped to generalized bell-shaped membership functions μ(x)=gbellmf(x,[a,b,c]), allowing flexible curvature and data-sensitive calibration. With ω = 3 inputs and z = 2 terms per input, the rule base contains r = 2³ = 8 if-then rules, ensuring structural completeness and transparent expert interpretation. The model is implemented in Matlab using Fuzzy Logic Toolbox (version R2021A) with a standard defuzzification workflow. Empirical verification for 2021–2025 demonstrates high sensitivity of the integral indicator: a clear LPMM decline in 2022 reflects the effect of external shocks and crisis factors, whereas consistent growth in 2023–2025 indicates gradual recovery and improved adaptation capacity. These results support the practical use of the framework for monitoring resilience, comparing scenarios, ranking policy options, and designing targeted interventions across financial, security, and institutional domains. Because knowledge is encoded as a rule base, the model can be updated as new risks or structural changes emerge, and integrated into automated monitoring and risk-forecasting pipelines. The framework strengthens methodological rigor by combining quantitative inputs with qualitative judgments in a single semantic space, and by providing interpretable outputs suitable for stakeholder communication. It is recommended for use in state design of economic security policy and strategic planning where fast decisions are made under incomplete information.