ANALYSIS OF THE IMPACT OF UNCERTAINTY ON SOLVING THE INVESTMENT ALLOCATION PROBLEM IN THE FIELD OF ECONOMIC ACTIVITY

Abstract

The distribution of investments in different sectors of the economy determines their future development. In conditions of economic instability, the profitability of investments becomes unpredictable, which complicates decision-making. Therefore, it is important to assess the consequences of different investment allocation strategies, exclude irrational options and determine the optimal ones. Research objective. To investigate the impact of statistical uncertainty on the optimal allocation of investments in the field of economic activity and develop tools for making effective investment decisions in unstable conditions. Methodology. Methods of exhaustive search methods, statistical analysis and multi-criteria optimization were used. The mathematical model of the investment allocation problem was implemented in the Python environment taking into account the uniform distribution of profits. Scientific novelty. A generalized statement of the investment allocation problem under conditions of uncertainty was formulated. An algorithm and software implementation were developed for calculating and analyzing optimal investment strategies taking into account variable profitability parameters. The proposed algorithm allows: to estimate future profits taking into account economic instability; determine a rational distribution of investments for different scenarios of profit dynamics; reduce the risks of losing investors or attracting new ones due to substantiated forecasts. The distribution of investments in agriculture with variable profits (10% and 40%) was studied, optimal solutions were analyzed according to various criteria. The feasibility of investing in the selected sector was determined depending on the level of uncertainty and external factors. Conclusions. The proposed approach allows predicting the profitability of investments taking into account economic instability. Criteria for optimal resource allocation that reduce investor risks were determined. The results can be used: by investors for decision-making under uncertainty; by government agencies to support strategically important industries; by analysts to model economic scenarios. Prospects for further research: expanding the model to other types of profit distributions (normal, Poisson); taking into account additional risk factors (for example, currency fluctuations); integrating machine learning to predict profit dynamics. This study highlights the importance of uncertainty analysis in investment decisions and offers a practical tool for their optimization.