ISSUES OF IMPROVING MULTI-OPTION FORECASTING OF DEMOGRAPHIC PROCESSES

Abstract

This article explores the challenges and methodologies associated with improving multi-option forecasting of demographic processes. Demographic forecasting plays a crucial role in national planning, informing policies on healthcare, education, labor markets, and social services. Traditional forecasting models often lack flexibility to account for complex, fluctuating factors such as migration, fertility rates, and changing life expectancies. This article examines innovative approaches, including scenario-based modeling, stochastic processes, and machine learning techniques, to enhance forecasting accuracy across multiple potential futures.