An algorithm for strategic forecasting of political changes in the VUCA environment

The study examines the problem of strategic forecasting of political changes in conditions of volatility, uncertainty, complexity and ambiguity (VUCA environment), which increase the instability of government institutions and reduce the reliability of traditional models. The relevance of addressing the topic is related to the frequent failures of linear forecasts and the limited suitability of rigid scenarios for describing the dynamics of political processes in a rapidly changing environment. The purpose of the work is to substantiate and describe an algorithm for strategic forecasting of political changes in the VUCA environment. The main objectives of the research include a theoretical understanding of the origins of the conceptualization of instability, identification of the limitations of linear forecasting, analysis of the role of subjective knowledge and institutional filters, as well as the consistent construction of an algorithm taking into account methodological risks. The methodological basis of the research is based on the concepts of complex systems and nonlinear dynamics, scenario analysis, the idea of cognitive distortions in political decision-making, as well as analogies with machine learning models that allow us to consider the political system as a learning structure. The scientific novelty lies in the formulation of a strategic forecasting algorithm that combines step-by-step modeling of political changes with a probabilistic description of scenarios and the inclusion of institutional constraints and cognitive distortions in the forecast structure. The theoretical result of the study is related to the refinement of ideas about strategic forecasting as a multivariate procedure. The practical significance of the research is determined by the possibility of using the proposed algorithm in the development of strategic planning documents and monitoring systems for political risks in conditions of volatility and uncertainty. The findings describe the conditions for improving the accuracy and adaptability of forecasts and open the way for further specification of tools for quantitative and qualitative assessment of political dynamics.