НЕЧЕТКО-МНОЖЕСТВЕННЫЙ ПОДХОД К ОЦЕНКЕ УРОВНЯ ИННОВАЦИОННОГО ПОТЕНЦИАЛА РЕГИОНА

Abstract

Innovations have become a significant factor in the contemporary global context, and the precise evaluation of the innovation potential at the regional level holds strategic importance for ensuring sustainable economic growth. This research is dedicated to the analysis and assessment of the innovation potential of regional systems. The objective of the study is to develop and establish a mathematical framework capable of accommodating various measurement units characterizing indicators of innovation potential. Additionally, it allows for the utilization of the acquired data for further comparative analysis of regional innovation potential. To achieve this goal, the authors employ a methodology grounded in the principles of fuzzy logic, which enables the consideration of uncertainty and complexity inherent in regional systems. The authors introduce a constructed fuzzy set-based model that encompasses the following indicators of innovation potential: human resources, resources, entrepreneurship, social, and infrastructural. Individual indicators of each category reflect specific aspects within the innovation context. This integrative approach comprehensively characterizes all indicators of a region’s innovation potential. In the process of constructing the fuzzy set-based model, the specific weight and admissibility level of each individual indicator were determined. The model was successfully tested using data from eight regions across different Federal Districts for the year 2020. The results of the computations yielded a quantitative expression of the innovation potential for each of the studied regions, which was subsequently linguistically interpreted. This research is relevant for the quantitative assessment of a region’s innovation potential and its subsequent comparative analysis, which contributes to the formulation of effective strategies aimed at the development, implementation, and utilization of innovations in regional economies.