Cybernetics And Systems Analysis logo
Editorial Board Announcements Abstracts Authors Archive
Cybernetics And Systems Analysis
International Theoretical Science Journal
-->

UDC 004.82.855’24
E.V. Ivokhin1, O.V. Oletsky2


1 Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

ivohin@univ.kiev.ua

2 National University of "Kyiv-Mohyla Academy", Kyiv, Ukraine

oletsky@ukr.net

RE-STRUCTURING OF THE MODEL "STATE–PROBABILITY OF CHOICE"
BASED ON PRODUCTS OF STOCHASTIC RECTANGULAR MATRICES

Abstract. To analyze the individual and collective behavior of agents, a "state–probability of choice" model is proposed, based on considering the probabilities of choosing alternatives and using the Markov chain of changes in these probabilities. Further development of the direction associated with modeling the description of the decision-making situation is proposed, which consists in explicitly setting the probabilities of decision-making based on the "state–probability of choice" model, provided that these probabilities can change over time. The proposed structuring of the model based on decomposition consists in the formation of the introduction of clusters of states, which can be provided with meaningful interpretation. The paper considers a two-level system of states, in which the base states correspond to specific probabilities of decision-making, and the states of the second level correspond to groups of states. It is shown that decomposition significantly weakens the factor related to the arbitrariness of the choice of base states. An example is given in which several groups of states are clearly distinguished, among which special attention is paid to the behavior of convinced supporters of certain alternatives, as well as to agents who hesitate.

Keywords: model "state–probability of choice," situation of decision-making, rectangular stochastic matrix, dynamic equilibrium of alternatives.


FULL TEXT

REFERENCES

  1. Oletsky O.V. On the approach to modeling the decision-making process in a multi-agent environment based on the Markov process of changing the probabilities of choice. Nauk. zap. NaUKMA. Computer Science. 2018. Vol. 1. P. 40–43.

  2. Oletsky O.V. On some necessary and sufficient conditions for equally probable choice of alternatives within the Markov chain of change of probabilities of choice. Nauk. zap. NaUKMA. Computer Science. 2019. Vol. 2. P. 4–9.

  3. Oletsky O.V., Ivohin E.V. Formalizing the procedure for the formation of a dynamic equilibrium of alternatives in a multi-agent environment in decision-making by majority of votes. Cybernetics and Systems Analysis. 2021. Vol. 57, N 1. P. 47–56. https://doi.org/10.1007/s10559-021-00328-y.

  4. Letichevsky A.A. Algebraic interaction theory and cyber-physical systems. Problemy upravleniya i informatiki. 2017. N 5. P. 37–55.

  5. Russell S., Norvig P. Artificial intelligence: a modern approach [Russian translation]. Moscow: Ed. house "Williams", 2006. 1408 p.

  6. Nikolenko S.I., Tulupyev A.L. Self-learning systems [in Rusian]. Moscow: MCNMO, 2009. 288 p.

  7. Mashchenko S.O. A mathematical programming problem with the fuzzy set of indices of constraints. Cybernetics and Systems Analysis. 2013. Vol. 49, N 1. P. 62–68. https://doi.org/10.1007/s10559-013-9485-4.

  8. Borgers T., Krahmer D., Strausz R. An introduction to the theory of mechanism design. Oxford: Oxford Univ. Press, 2015.

  9. Roughgarden T. Twenty lectures on algorithmic game theory. Cambridge: Cambridge Univ. Press, 2016.

  10. Horn R., Johnson C. Matrix Analysis [Russian translation]. Moscow: Mir, 1989. 655 p.

  11. Oletsky O. Exploring dynamic equilibrium of alternatives on the base of rectangular stochastic matrices. CEUR Workshop Proc. 2021. Vol. 2917. P. 151–160. http://ceur-ws.org/Vol-2917/.

  12. Ivokhin E.V., Apanasenko D.V. Clustering of composite fuzzy numbers aggregate based on sets of scalar and vector levels. Journal of Automation and Information Sciences. 2018. Vol. 50, N 10. P. 47–59. https://doi.org/10.1615/JAutomatInfScien.v50.i10.40.

  13. Provotar O.I., Provotar O.O. Fuzzy probabilities of fuzzy events. Kibernetika i sistemnyj analiz. 2020. Vol. 56, N 2. P. 3–13.

  14. Saaty T. Decision making. Hierarchy analysis method [Russian translation]. Moscow: Radio i svyaz', 1993. 278 p.

  15. Chernorutsky I.G. Decision-making methods [in Russian]. St. Petersburg: BHV-Petersburg, 2005. 416 p.

  16. Saaty T.L. Decision making under dependencies and feedbacks. Analytical networks [Russian translation]. Moscow: LKI, 2008. 360 p.

  17. Ivokhin E.V., Naumenko Yu.A. On the formalization of information dissemination processes based on hybrid diffusion models. Problemy upravleniya i informatiki. 2018. N 4. P. 120–127.

  18. Tryhub O.S., Tryhub R.O., Gorborukov V. Researching semistructured problems of multicriteria optimization using the software system. Nauk. zap. NaUKMA. Computer Science. 2013. Vol. 151: P. 79–88.

  19. Oletsky O.V., Trygub O.S. On the application of the method of analysis of hierarchies for automated assessment of student work. Nauk. zap. NaUKMA. Computer Science. 2020. Vol. 3. P. 127–131. https://doi.org/10.18523/2617-3808.2020.3.127-131.




© 2022 Kibernetika.org. All rights reserved.