DOI: 10.33917/es-6.204.2025.26-33

Conceptual approaches to modeling trends in information technology are discussed. It is shown that modeling IT trends solely based on technology interest data, such as Gartner’s Hype Cycle, is insufficient to predict the emergence of new technologies. Various IT examples demonstrate the need to analyze the connections between technologies, as success in some areas of economic digitalization creates the conditions for technological development in others. Furthermore, due to the high knowledge intensity of information technology, the depth of fundamental and applied scientific research must be taken into account. When modeling trends, the demand for technologies in specific economic sectors must also be considered. Simulations based on empirical data must identify simple and explainable relationships, and high-quality predictions must be achieved by synthesizing all the relationships of the technology under study. The proposed conceptual approach can also be applied to modeling other socioeconomic processes based on the synthesis of empirical data.

References:

1. Almalawi A., Soh B., Li A., Samra H. Predictive Models for Educational Purposes. A Systematic Review. Big Data Cogn. Comput., 2024, no 8, pp. 187, DOI: https://doi.org/10.3390/ bdcc8120187

2. Hassan M., Awan F.M., Naz A., Andrés-Galiana, de E.J., Alvarez O., Cernea A.; Fernández-Brillet L., Fernández-Martínez J.L., Kloczkowski A. Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care. A Review. Int. J. Mol. Sci., 2022, no 23, 4645, DOI: https://doi.org/10.3390/ ijms23094645

3. Ribeiro F.L., Rybski D. Mathematical models to explain the origin of urban scaling laws. Physics Reports, 2023, vol. 1012, 23 April, pp. 1–39.

4. Mokhov V., Aliukov S., Alabugin A., Osintsev K. A Review of Mathematical Models of Macroeconomics, Microeconomics, and Government Regulation of the Economy. Mathematics, 2023, no 11, 3246, DOI: https://doi.org/10.3390/ math11143246

5. Helbing D. Pluralistic Modeling of Complex System. Science and Culture, 2010, vol. 76, no 9/10, pp. 399–417.

6. Zhu Z., Xie H., Chen L. ICT industry innovation: Knowledge structure and research agenda. Technological Forecasting and Social Change, 2023, vol. 189, 122361.

7. Kleyner G.B. Dokazatel’noe modelirovanie kak perspektivnyy instrument nauchnogo issledovaniya sotsial’no-ekonomicheskikh protsessov [Evidence-based Modeling as a Promising Tool for Scientific Research of Socio-economic Processes]. Ekonomika i upravlenie: problemy, resheniya, 2023, no 6, vol. 2, pp. 5–16, DOI: https://doi.org/10.36871/ek.up.p.r.2023.06.02.001

8. O’Leary D.E. Gartner’s hype cycle and information system research issues. International Journal of Accounting Information Systems, 2008, no 9, pp. 240–252.

9. Chen X., Han T. Disruptive Technology Forecasting based on Gartner Hype Cycle. 2019 IEEE Technology & Engineering Management Conference (TEMSCON), Atlanta, GA, USA, 2019, pp. 1–6, DOI: 10.1109/TEMSCON.2019.8813649.

DOI: 10.33917/es-6.198.2024.90-97

A special feature of the use of expert networks in public administration is the need to involve collective intelligence technologies based on a competence-based approach. The article proposes a scheme for organizing such use of expert communities with the help of national, sectoral and departmental competency frameworks. Introduction of expert networks in public administration can become a driver for their implementation in all spheres of the economy and public life of Russia, a tool for involving citizens in self-government.

References:

1. The Yankee Group. Knowledge Management. People and the Process. Boston, 1997, 89 p.

2. Friberg M. Expert network market size. Inex One, 2024, available at: https://inex.one/blog/expert-network-market-size

3. Slavin B. Sovremennye ekspertnye seti [Modern Expert Networks]. Otkrytye sistemy, 2014, no 7, pp. 30–33.

4. Noveck B.S. Wiki Government: How Technology Can Make Government Better, Democracy Stronger, and Citizens More Powerful. Washington DC, Brookings Institution Press, 2009, 224 p.

5. Dutton W.H. Networking Distributed Public Expertise: Strategies for Citizen Sourcing Advice to Government. Washington D.C., One of a Series of Occasional Papers in Science and Technology Policy, Science and Technology Policy Institute, Institute for Defense Analyses, 2010. 40 p.

6. EonD top talent accross policy lifecycle. EonD, available at: https://eond.eu/for-clients/government/

7. The OECD Network of Behavioural Insights Experts in Government. The OECD Observatory of Public Sector Innovation, 2024, available at: https://oecd-opsi.org/bi-network/

8. Australian Government Consulting. AGC, available at: https://agc.gov.au/

9. Perechen’ ekspertnykh sovetov pri FAS Rossii [List of Expert Councils under the FAS Russia]. FAS Rossii, available at: https://fas.gov.ru/pages/vazhnayainformacziya/otkryitoe-vedomstvo/ekspertnye_sovety

10. Federal’nyy reestr ekspertov nauchno-tekhnicheskoy sfery [Federal Register of Experts in the Scientific and Technical Sphere]. FGBNU NII RINKTsE, available at: https://reestr.extech.ru/experty/index.php

11. Slavin B.B. Tekhnologii kollektivnogo intellekta [Collective Intelligence Technologies]. Problemy upravleniya, 2016, no 5, pp. 2–9.

12. Boucher S., Hallin C.A., Paulson L., editors. The Routledge Handbook of Collective Intelligence for Democracy and Governance. Abingdon, Taylor & Francis Group, 2023, 522 p.

13. Lepskiy V.E. Metodologicheskiy i filosofskiy analiz razvitiya problematiki upravleniya [Methodological and Philosophical Analysis of the Development of Management Issues]. Moscow, Kogito-Tsentr, 2019, 340 p.

14. Sungurov A. Ekcpertnye soobshchestva i vlast’ [Expert Communities and Authorities]. Moscow, Politicheskaya entsiklopediya, 2020, 231 p.

15. Avdeeva Z.K., Baryshnikov P.Yu., Slavin B.B. Strategicheskoe tselepolaganie v situatsionnykh tsentrakh razvitiya [Strategic Goal Setting in Situational Development Centers]. Moscow, Kogito-Tsentr, 2018, 320 p.