Neural Simulation of Digital Twin of Top Management Motivation Mechanism in Regional Government Agencies
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-t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2023-12-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/6840 |
| Summary: | 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. |
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| ISSN: | 2086-9614 2087-2100 |