Agent based models (ABM) and multiagent systems (MAS) can be used to solve problems in many fields of research - from natural and computer to economics and social sciences. Many natural and social phenomena can be represented in form of complex simulations so over time agent models and multi-agent systems have proven to be a really powerful tool in areas such as economics and trade, health, urban planning and social sciences. In addition multi-agent systems can be represented as an artificial society similar to a human one and consisting of entities with characteristics similar to human ones, for example in terms of autonomy and intelligence. ABM are based on the principle of objective orientation as well as the evolution (training) of agents in the process of modeling various variants of the proposed events. Despite the apparent simplicity of the rules of interaction between agents the results are usually non-obvious and quite meaningful. ABM can be developed both at the micro level and represent models with multiple agents at the macro level. The concept of multi-agent systems which immediately gained followers and support in both scientific circles and industrial communities, first started talking in the mid-1980s. Over the past thirty years, the methodology of IAU creation has been constantly improved: technologies and tools for its promotion and use in the management of large-scale network structures (such as defense systems, energy, health, transport, logistics, urban management, collective robotics, etc.) have been actively developed. The scope of application of MAS is very wide. The analysis of implemented MAS proves that currently the tool is the most advanced technology for managing any objects built on the principles of self-organization. However, despite all the evidence of positive prospects for the introduction of AOM technology the number of examples of its successful application to date is small. In this regard creation of new platforms for discussion of international experience and improvement of the approach to simulation modeling in general is especially important for further dissemination of AMB and MAS. Creation of an open consortium for agent-oriented modeling as well as promotion of development, communication and dissemination of research results as well as implementation of educational activities together will contribute to the development of agent based modeling. The analysis and review of existing methodology of social modeling with use of agent based approach in the application to scientific and technical development, implementation of R&D and maintenance of innovative potential showed that models characterized by complex multi-level processes and interactions of agents have more capacious software structures which depend more on the "fine" tuning of the agents themselves. Such models can contain and use a voluminous set of data, and in the field of economic research tend to focus on the analysis and forecasting of various socio-economic processes at the macro level.

agent based modeling, simulation modeling, multi-agent systems, scientific and technological progress, R&D, S&D, innovation

1. Abramov V.I., Evdokimov D.S. Development of a complex of agent-based models of the public procurement system of the countries of the Eurasian continent. Problems of management theory and practice. 2019. pp. 15–23. (in Russian).

2. Dyachuk P.P., Dyachuk P.P., Karabalykov S.A., Shadrin I.V. Diagnosis of unstable cognitive states of active agents. Neuroinformatics 2016: collection of scientific papers: in 3 parts. Moscow, National Research Nuclear University MEPhI, 2016. pp. 259–270. (in Russian).

3. Kogalovsky M.R., Kalinichenko L.A. Conceptual and ontological modeling in information systems. Programming. 2009. vol. 35. no. 5. pp. 3–25. (in Russian).

4. Makarov V.L., Bakhtizin A.R. Social modeling is a new computer breakthrough. Agent-based models. Moscow, Economics, 2013. 295 p. (in Russian).

5. Makarov V.L., Bakhtizin A.R., Sushko E.D., Abramov V.I. Computer simulation in economic management (methodological basis for strategic planning). State audit. Right. Economy. 2017. no. 3. (in Russian).

6. Tarasov V.B. From multi-agent systems to intelligent organizations: philosophy, psychology, computer science. Moscow, Editorial URSS, 2002. 352 p. (in Russian).

7. Ahrweiler P., Schilperoord M., Pyka A., Gilbert N. Modelling Research Policy: Ex-Ante Evaluation of Complex Policy Instruments. Journal of Artificial Societies and Social Simulation. 2015. vol. 18 (4). no. 5. doi: 10.18564/jasss.2927

8. Angelini A., Cerulli G., Cecconi F., Miceli A. et al. R&D Subsidization Effect and Network Centralization: Evidence from an Agent-Based Micro-Policy Simulation. Journal of Artificial Societies and Social Simulation. 2017. vol. 20 (4). no. 4. doi: 10.18564/jasss.3494

9. Antonelli С., Ferrarisb G. Innovation as an Emerging System Property: An Agent Based Simulation Model. Journal of Artificial Societies and Social Simulation. 2011. vol. 14 (2). no. 1.

10. Bousquet F., Tr?buil G., Hardy B. Companion Modeling and Multi-Agent Systems for Integrated Natural Resource Management in Asia Los Baсos (Philippines). International Rice Research Institute. 2005. 360 p.

11. Bianchi F., Grimaldo F., Bravo G., Squazzoni F. The peer review game: an agent-based model of scientists facing resource constraints and institutional pressures. Springer, 2018. doi: 10.1007/s11192–018–2825–4

12. Brinner R. The 1985 DRI Model: An Overview, in Data Resources Review of the US Economy. Lexington, 1985.

13. Epstein J., Axtell R. Growing Artificial Societies: Social Science From the Bottom Up. Washington, D.C., MIT Press / Brookings Institution, 1996.

14. Goldspink C. Methodological Implications Of Complex Systems Approaches to Sociality: Simulation as a foundation for knowledge. Journal of Artificial Societies and Social Simulation. 2002. vol. 5. no. 1.

15. Goto Y., Takeuchi I., Kakumoto S. Integrated earthquake disaster simulation systems for the highlynetworked information society. Proc. of the 13th World Conference on Earthquake Engineering. Vancouver, 2004. pp. 2793. Available at: http://www.iitk.ac.in/ nicee/wcee/article/13_2793.pdf

16. Gilbert N., Pyka A., Ahrweiler P. Innovation Networks – A Simulation Approach. Journal of Artificial Societies and Social Simulation. 2001. vol. 4. no. 3.

17. Haag G., Liedl P. Modelling and Simulating Innovation Behaviour within Micro-based Correlated Decision Processes. Journal of Artificial Societies and Social Simulation. 2001.

18. Haydari S., Smead R. Does Longer Copyright Protection Help or Hurt Scientific Knowledge Creation? Journal of Artificial Societies and Social Simulation. 2015. vol. 18 (2). no. 23. doi: 10.18564/jasss.2720

19. Janssen A., Alessa L., Barton M., Bergin S. et al. Towards a Community Framework for Agent-Based Modelling. Journal of Artificial Societies and Social Simulation. 2008

20. Kravari K., Bassiliades N. A Survey of Agent Platforms. Journal of Artificial Societies and Social Simulation. 2015. doi: 10.18564/jasss.2661

21. Lee S. Simulation of the Long-Term Effects of Decentralized and Adaptive Investments in Cross-Agency Interoperable and Standard IT Systems. Journal of Artificial Societies and Social Simulation. 2010. vol. 13 (2). no. 3. doi: 10.18564/jasss.1488

22. Law A.M., Kelton W.D. Simulation Modeling and Analysis. New York: McGraw-Hill, 1991.

23. Monticino M.G., Brooks E., Cogdill T., Acevedo M. et al. Applying a Multi-Agent Model to Evaluate Effects of Development Proposals and Growth Management Policies on Suburban Sprawl. Proc. of the International Environmental Modelling and Software Society, Summit on Environmental Modelling and Software. Burlington, 2006. Available at: http://www.math.unt.edu/~monticino/papers/mult-agent_development.pdf

24. Neves F., Campos P., Silva S. Innovation and Employment: An Agent-Based Approach. Journal of Artificial Societies and Social Simulation. 2019. vol. 22 (1). no. 8. doi: 10.18564/jasss.3933

25. Pajares J., Hern?ndez-Iglesias C., L?pez-Paredes A. Modelling Learning and R&D in Innovative Environments: a Cognitive Multi-Agent Approach. Journal of Artificial Societies and Social Simulation. 2004. vol. 7. no. 2.

26. Pajares J., L?pez A., Hern?ndez C. Industry as an Organisation of Agents: Innovation and R&D Management. Journal of Artificial Societies and Social Simulation. 2003. vol. 6. no. 2.

27. Parinov S., Neylon C. Science as a Social System and Virtual Research Environment. Journal of Artificial Societies and Social Simulation. 2011. vol. 14 (4). no. 10. doi: 10.18564/jasss.1835

28. Sobkowicz P. Innovation Suppression and Clique Evolution in Peer-Review-Based, Competitive Research Funding Systems: An Agent-Based Model. Journal of Artificial Societies and Social Simulation. 2015. vol. 18 (2). no. 13. doi: 10.18564/jasss.2750

29. Schulze J., M?ller B., Groeneveld J., Grimm V. Agent-Based Modelling of Social-Ecological Systems: Achievements, Challenges, and a Way Forward. Journal of Artificial Societies and Social Simulation. 2017. vol. 20 (2). no. 8. doi: 10.18564/jasss.3423

30. Tsekeris T., Vogiatzoglou K. Multi-Regional Agent-Based Economic Model of Household and Firm Location and Transport Decisions. Proc. of the 10th STRC Swiss Transport Research Conference. Monte Veritа, 2010. Available at: http://www.strc.ch/conferences/2010/Tsekeris.pdf

31. Yilmaz L. Toward Multi-Level, Multi-Theoretical Model Portfolios for Scientific Enterprise Workforce Dynamics. Journal of Artificial Societies and Social Simulation. 2011. vol. 14 (4). no. 2. doi: 10.18564/jasss.1853