Skip to main content
Log in

Restructuring of the Model “State–Probability of Choice” Based on Products of Stochastic Rectangular Matrices

  • Published:
Cybernetics and Systems Analysis Aims and scope

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. It explicitly sets 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 implies the formation of clusters of states, which can be provided with meaningful interpretation. The authors consider 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 staunch supporters of certain alternatives, as well as to agents who hesitate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. O. V. Oletsky, “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, Komp. Nauky, Vol. 1, 40–43 (2018).

    Google Scholar 

  2. O. V. Oletsky, “On some necessary and sufficient conditions for equiprobable choice of alternatives within the Markov chain of the change of probabilities of choice,” Nauk. Zap. NaUKMA, Komp. Nauky, Vol. 2, 4–9 (2019).

    Google Scholar 

  3. O. V. Oletsky and E. V. Ivohin, “Formalizing the procedure for the formation of a dynamic equilibrium of alternatives in a multi-agent environment in decision-making by majority of votes,” Cybern. Syst. Analysis, Vol. 57, No. 1, 47–56 (2021). https://doi.org/10.1007/s10559-021-00328-y.

    Article  MATH  Google Scholar 

  4. A. A. Letichevsky, “Algebraic interaction theory and cyber-physical systems,” J. Autom. Inform. Sci., Vol. 49, No. 5, 1–19 (2017).

    Article  Google Scholar 

  5. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall (1995).

  6. S. I. Nikolenko and A. L. Tulup’ev, Self-Learning Systems [in Russian], MTsNMO, Moscow (2009).

  7. S. O. Mashchenko, “A mathematical programming problem with the fuzzy set of indices of constraints,” Cybern. Syst. Analysis, Vol. 49, No. 1, 62–68 (2013). https://doi.org/10.1007/s10559-013-9485-4.

    Article  MathSciNet  MATH  Google Scholar 

  8. T. Borgers, D. Krahmer, and R. Strausz, An Introduction to the Theory of Mechanism Design, Oxford Univ. Press, Oxford (2015).

  9. T. Roughgarden, Twenty Lectures on Algorithmic Game Theory, Cambridge Univ. Press, Cambridge (2016).

  10. R. A. Horn and C. R. Johnson, Matrix Analysis, Cambridge Univ. Press, Cambridge (1990).

  11. O. Oletsky, “Exploring dynamic equilibrium of alternatives on the base of rectangular stochastic matrices,” in: CEUR Workshop Proc., Vol. 2917 (2021), pp. 151–160. URL: http://ceur-ws.org/Vol-2917/.

  12. E. V. Ivokhin and D. V. Apanasenko, “Clustering of composite fuzzy numbers aggregate based on sets of scalar and vector levels,” J. Autom. Inform. Sci., Vol. 50, No. 10, 47–59 (2018). https://doi.org/10.1615/JAutomatInfScien.v50.i10.40.

    Article  Google Scholar 

  13. O. I. Provotar and O. O. Provotar, “Fuzzy probabilities of fuzzy events,” Cybern. Syst. Analysis, Vol. 56, No. 2, 171–180 (2020). https://doi.org/10.1007/s10559-020-00232-x.

    Article  MathSciNet  MATH  Google Scholar 

  14. T. L. Saaty, The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw Hill (1980).

  15. I. G. Chernorutskii, Methods of Decision Making [in Russian], BKhV-Peterburg, St. Petersburg (2005).

  16. T. L. Saaty, Decision Making with Dependence and Feedback: The Analytic Network Process: The Organization and Prioritization of Complexity, RWS Publications (1996).

  17. E. V. Ivokhin and Yu. A. Naumenko, “On formalization of information dissemination processes based on hybrid diffusion models,” J. Autom. Inform. Sci., Vol. 50, No. 7, 79–86 (2018).

    Article  Google Scholar 

  18. O. S. Tryhub, R. O. Tryhub, and V. Gorborukov, “Researching semistructured problems of multicriteria optimization using the software system,” Nauk. Zap. NaUKMA, Komp. Nauky, Vol. 151, 79–88 (2013).

    Google Scholar 

  19. O. V. Oletsky and O. S. Trygub, “Applying the analytic hierarchy process for automated assessment of student works,” Nauk. Zap. NaUKMA, Komp. Nauky, Vol. 3, 127–131 (2020). https://doi.org/10.18523/2617-3808.2020.3.127-131.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. V. Ivokhin.

Additional information

Translated from Kibernetyka ta Systemnyi Analiz, No. 2, March–April, 2022, pp. 96–107.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ivokhin, E.V., Oletsky, O.V. Restructuring of the Model “State–Probability of Choice” Based on Products of Stochastic Rectangular Matrices. Cybern Syst Anal 58, 242–250 (2022). https://doi.org/10.1007/s10559-022-00456-z

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10559-022-00456-z

Keywords

Navigation