Actor model and method of evolutionary coordination of decisionsоn

Roman Mirackmedov, Vladislav Protasov

Abstract


An actor model is presented that explains the      effects of increasing the intellectual power of a group of actors compared to a single actor and significantly reducing the likelihood of erroneous decisions when using the evolutionary coordination method. A theorem confirming this effect is formulated and proven. Definitions are given and mathematical expressions are obtained for calculating intellectual power in mathematically based units using an absolute measurement scale. The results of computer modeling of the decision-making process are presented. Agreement with the theoretical model was obtained. Conclusions are drawn regarding the conditions under which it is possible for an actor of the first rank to obtain a correct decision with a probability of an erroneous decision below a predetermined small value.


Full Text:

PDF (Russian)

References


A. Gorban: Errors in data-based intelligence. Collection of articles based on the materials of the International Conference “Intelligent Systems in Science and Technology. artificial intelligence in solving pressing social and economic problems of the 21st century.” – Perm: –2020, pp. 11–13 (in Russian).

Alexander Gorban , Bogdan Grechuk, Ivan Tyukin.: Augmented Artificial Intelligence: a Conceptual Framework. 18.03.2018, arXiv preprint arXiv: https://arxiv.org/pdf/1802.02172v3.pdf (дата обращения 17.03.2024).

Fields, Chris, James F. Glazebrook, and Michael Levin.: Principled Limitations on Self-Representation for Generic Physical Systems // Entropy, 2024, no. 3: 194 p. https://doi.org/10.3390/e26030194&

Stammers, S.; Bortolotti, L. Introduction: Philosophical Perpectives on Confabulation. Topoi 2020, 39, pp.115–119. https://link.springer.com/article/10.1007/s11245-019-09668-z (дата обращения 17.03.2024).

Barba, G.F.; La Corte, V. : A neurophenomenological model for the role of the hippocampus in temporal consciousness. Evidence from confabulation // Front. Behav. Neurosci. 2015, 9, 218 p. DOIi: 10.3389/fnbeh.2015.00218

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4549641/

Keeling, S.: Confabulation and rational obligations for self-knowledge // Philos. Psychol. 2018, 31, pp.1215–1238. https://doi.org/10.1080/09515089.2018.1484086

A.Zhdanov: General theory of systems: analysis and additions. Electronic edition. –M.: Laboratory of Knowledge, 2024, 192 p. (in Russian).

V. Protasov.: Systems of collective intelligence. Theory and practice. –M: University Book, 2024, 230 p. (in Russian).

V. Protasov, Z Potapova: Methodology for radically reducing the probability of making erroneous decisions in collective intelligence systems // International scientific journal “Modern information technologies and IT education”. 2019, volume 15, no. 3, p. 588– 601 (in Russian).

DOI: https://doi.org/10.25559/SITITO.15.201903.588-601

V. Protasov, Z. Potapova , G. Akhobadze. : How to reduce the probability of erroneous decisions in the systems of collective intelligence // IOP Conference Series: Materials Science and Engineering, 2020, v.927(1), 012069.

https://iopscience.iop.org/article/10.1088/1757-899X/927/1/012069/pdf

Actor model and method of evolutionary coordination of decisionsоn

V. Sobolev: Quantum of knowledge and information as an epistemological problem // Philosophical thought. 2016. No. 6. P. 19-27. (in Russian). DOI: 10.7256/2409-8728.2016.6.19212

Kolb, D. A.: Experiential learning theory: A dynamic, holistic approach to learning. In Learning theory, 2021, pp. 386-412. Routledge. DOI: 10.4324/9780203878365

A. Smagin, S. Lipatova, A. Melnichenko: Intelligent information systems. – Ulyanovsk: Publishing center of Ulyanovsk State University, 2010, 50 p. (in Russian).

Smith, J. A., & Clark, J. M.: Knowledge modeling and the design of information systems // Journal of Knowledge Management, 2017, vol. 21(5), pp.1081-1098. DOI: 10.1108/JKM-08-2016-0345

Brown, J. S., & Duguid, P.: Organizing knowledge // MIT Sloan Management Review,2018, vol. 59(3), pp.105-112.https://sloanreview.mit.edu/article/organizing-knowledge/ (дата обращения 17.03.2024).

Greeno, J. G., & Collins, A. M.: Cognitive model of knowledge transfer // Handbook of Educational Psychology, 2019, pp. 88-106. Routledge. DOI: 10.4324/9781410605350

Jonassen, D. H.: Constructivist models of learning. In Learning to solve problems with technology, 2020, pp. 1-17. Routledge. DOI: 10.4324/9780203855519

V. Protasov, Z.Potapova: Metrology of systems of evolutionary coordination of decisions and standardization of intellectual work // International scientific journal “Modern information technologies and IT education”. 2019, volume 15, no. 4, p. 1058-1069. (in Russian).

DOI: https://doi.org/10.25559/SITITO.15.201904.1047-1055

G. Rasch.: Probabilistic Models for Some Intelligence and Attainment Tests. – Chicago: University of Chicago Press, 1981,199 p.

Сondorcet, marquis Marie-Jean-Antoine-Nicolas de Caritat. : Essai sur l’application de l’analyse à la probabilité des décisions rendues à la pluralité des voix. –Paris: Imprimerie Royale, 1785


Refbacks

  • There are currently no refbacks.


Abava  Кибербезопасность MoNeTec 2024

ISSN: 2307-8162