Authors

Economic assessment of land use efficiency in urban agglomerations based on regression-game modeling

Land use processes are the basic economic processes that determine the efficiency of the functioning of socio-economic systems. This is especially true for large urban agglomerations, for which these processes directly and indirectly significantly affect the volume of revenues to the budgets of various levels, as well as determine the investment attractiveness of the regions. As a result, the principles of rational land use should be based on the results of system analysis and mathematical modeling of the impact of external and internal environmental factors on all subjects involved in the processes of using land plots as the most important resource of the urban economy. The article is devoted to the development of two-level models that involve the use of regression analysis to determine the characteristics of the game matrix in the presence of a sufficiently large number of players with different interests. This approach is distinguished by the possibility of more complete consideration of factors influencing the efficiency of land use when assessing the intensity of land use in large cities and agglomerations, in conditions of external volatility. As a result, the construction of a multiple regression model describing the influence of factors, and the subsequent matrix modeling of game-theoretic conflict situations in land use makes it possible to support decision-making in the development of investment programs for the development of territories. The article discusses an example of the application of the proposed approach to the analysis of factors that have a significant impact on the efficiency of land use in the Moscow agglomeration. The results of the regression analysis led to the conclusion that the predicate of increasing financial and economic returns in the implementation of land use processes is the level of investment in fixed assets. On the example of the Moscow agglomeration, it is also shown that the obtained results of regression analysis can be used in the construction of game-theoretic models of land use, which, in turn, are expedient to apply to support decisions on state management of the market of land plots for various purposes. Read more...

Creation and application of graph models for employee recruitment tasks

This study presents a mathematical hiring model based on the integration of graph methods and blockchain verification. Unlike traditional recruitment approaches, the proposed model incorporates candidates’ network characteristics, such as degree centrality, betweenness centrality, and PageRank, while also utilizing trust scores (Trust Score) to predict hiring success. A cluster analysis of interactions between candidates, employers, HR platforms, and certification centers was conducted, revealing key patterns in professional network formation. The scientific novelty of this research lies in the development of a comprehensive personnel selection algorithm that, in addition to standard ­HR metrics, employs graph-based indicators and blockchain verification mechanisms to enhance transparency and accuracy in hiring. For the first time, a methodology for calculating trust scores based on network analysis has been proposed, allowing for a more objective decision-making process in ­HR analytics. The results indicate that the application of graph methods in recruitment reduces hiring time by 32 %, decreases the likelihood of mismatched hires by 18 %, and improves candidate success prediction accuracy to 85 %. These findings confirm the potential of integrating graph neural networks and blockchain verification in automated HR systems. Read more...