Multi-agent modeling as a tool of designing the parameters of the anticorruption legal regime

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Abstract

The article deals with the problem of multi-agent modeling as a tool for designing parameters of anti-corruption legal regime. Special attention is paid to the realization of the idea of creating normative "eco-environments" (normative subspaces) that meet the logic of goal-setting of the participants of legal support through the prism of implementation of the concept of bounded rationality by artificial intelligence agents.

About the authors

A. O. Stepanov

Institute of Legislation and Comparative Law under the Government of the Russian Federation

Email: stepanov.alexey99@gmail.com
postgraduate student of the Department of Legal Problems of Counteracting Corruption Moscow

References

  1. Khabrieva T.Y. Law before the challenges of digital reality// ZhRP-2018. — № 9. С.5-16.
  2. Malko A.V. Incentives and restrictions in law. M. Yurist.2003. — 250 p.
  3. Truntsevsky Y.V. Law as a code and precision law in the perspective of dating // ZhZZ and SP. 2021. T.17. — No.1. С. 49-67.
  4. Cognitive Science 101. URL: Predictive Processing https://habr.com/ru/articles/594831/
  5. Suvorova E.V. Types of inferences in discourse //Philological Sciences. Voprosy teorii i praktika Tambov: Gramota. 2018. — № 4(82). Ч. 1. C. 176-181.
  6. Simon G. Rationality as a Process and Product of Thinking // THESIS Vyp. 3. 1993. URL:https://www.bibliofond.ru/view.aspx?id=820283
  7. Simon H. A. Rationality as process and as product of thought. The American economic review, 68(2), 1978. Р. 1-16.
  8. Artificial Intelligence Agents Explained. URL: https://www.baeldung.com/cs/artificial-intelligence-agents
  9. Pospelov D.A. From the collective of automata to multiagent systems // Proc. of the International Workshop "Distributed Atrifical Intelligence und Multi-Agent Systems". Of the International Workshop "Distributed Atrifical Intelligence und Multi-Agent Systems", DIAMAS' 97, St. Peterburg, 1997. Р. 319-325.
  10. Weppner H. Individuenbasierte Simulation eines oligopolistischen Markets auf Basis des Referenzmodells PECS; Lehrstuhl fur Operations Research und Systemtheorie, Universitat Passau, 1998.
  11. Karpov Yu.G. Simulation modeling of systems. Introduction to AnyLogic 5. SPb.: BHV-Peterburg, 2005. — 400 с.
  12. Kulikov JI.M. Fundamentals of economic knowledge.-M: Finance and Statistics, 1998. — 272 с.
  13. Beillie P., Toleman M. Creating an Emotional Cpace for Artificial Beings. // 2-nd Workshop on Agent-Based Simulation., SCS-Europe BVBA, Ghent, Belgium. 2001. Р. 13-17.
  14. Tyrin, G. N. Development and research of multi-agent models // Young Scientist. Young Scientist. 2017. — № 23 (157). С. 166-173.
  15. Rosstat presented the Strategy for the development of state statistics up to 2030. URL:https://rosstat.gov.ru/folder/313/document/244701

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