Forecasting development trends in the information technology industry using fuzzy logic

Abstract

The IT industry occupies a significant place in the economy of the world. Trends in its development directly affect changes in various sectors of the economy. This paper considers the dynamics of IT industry under conditions of increased entropy of the functioning environment caused by crises, military conflicts, non-standard events. The mathematical basis of the study is an apparatus of fuzzy logic. The proposed technique for analyzing and forecasting the state and dynamics of the IT sector makes it possible to take into account the influences of a set of factors of different nature and direction of action, which contributes to obtaining a reasonable assessment under conditions of inaccurate information. The software platform for implementing the methodology is the authentically designed fuzzy expert system (ES) FuzzyKIDE. The conceptual foundations of the platform structure have been defined. A modification of the fuzzy inference mechanism has been proposed, which, unlike existing samples, makes it possible to eliminate saving intermediate results and reduces the load on the database. The composition and structure of the knowledge base (KB) for ES have been proposed. Fuzzy rules are based on predetermined relationships between key factors of influence. The technology of ES operation is represented using simulated scenarios with obtaining predictive results. The analysis of the final data of expert consultations proves the ES operability, providing for the possibility of KB significant expansion in the process of industrial operation. The use of ES is aimed at forming a holistic view of possible directions of IT sector development. Maintaining the actual state of ES KB is a condition for early warning of the emergent negative/crisis phenomena. The FuzzyKIDE expert consulting system is proposed as a tool to support management decision-making based on the analysis and forecasting of the state and dynamics of IT sector under conditions of high uncertainty