Neural Simulation of Digital Twin of Top Management Motivation Mechanism in Regional Government Agencies

Abstract

The aim of the research was the problem of neural simulation of the digital twin of non-financial and financial motivation of top management in government agencies, as well as the strategic potential of regions. Bayesian regularization is used as the network training algorithm because the quasi-time series developed for 83 regions in Russia for the period from 2010 to 2021 is highly noisy. The inner layer of the network has 15 neurons since in this case, the network is trained most optimally. In the verification stage of the trained network, the comparison of actual and forecast data showed that in 2021, the error of the trained network was to average the fluctuations of the quasi-time series. In other words, the network does not account for the overall downward trend in the data. This problem requires a separate in-depth study. For instance, in the case of the Nizhny Novgorod Region, it has been observed that in 2020 and 2021, top managers performed better than those in the leading region (Moscow) based on the parameter of the total area of residential premises per capita. Therefore, they should be financially rewarded for their performance. In terms of non-financial motivation, the top managers should be rewarded more in 2021 than in 2020. The strategic potential of the Nizhny Novgorod Region as a whole is more developed in 2021 than in 2020, which allows us to assess the region's development prospects positively.

Keywords

• bayesian regularization
• digital twin
• motivation of top managers
• neural simulation