Abstract
In this article, we consider how to allocate budgets based on citizen votes in regions. Using data on the degree of urbanization and voter preferences in each city's areas of activity, we develop a funding allocation model taking into account the level of balance in each area of activity. This data is up-to-date and taken from official sources such as the Bureau of National Statistics and others. This article shows the changes in budget allocation because of applying these adjustments and highlights the impact of the chosen method on improving the targeted use of public financial resources. These methods use constrained linear programming and maximization of a certain function and the Python library PuLP, aimed at solving problems of this type. The first model allowed for taking into account the opinion of citizens and redistributing the budget taking into account priority areas, increasing funding for critical areas by 15%. The second model, using the simplex method, increased the efficiency of distribution by 12% due to budget optimization, but some areas, such as culture and ecology, were left without funding due to the prioritization of more significant areas. As a result, each model showed its advantages: the first in transparency and democracy, the second in increasing the efficiency of using limited resources. Both approaches demonstrate the potential for improving the efficiency of public financial resource management.