Release:Releases Archive. Вестник ТюмГУ. Физико-математические науки. Информатика (№7, 2013)
About the authors:Alexander A. Zakharov, Dr. Sci (Tech.), Professor, Secure Smart City Information Technologies Department, University of Tyumen; email@example.com
Abstract:This article describes an approach to the construction of a stochastic model of the compromise decision formation basing on a given set of initial proposals. The specifics of the decision process are determined by some initial conditions and the criteria of transition from one stage to another, including the final stage of decision making. The algorithm for generating decisions includes an initial ranking of initial proposals characteristics, a special rule for determining the measure of proximity proposals, a test of proposals’ compatibility taking into account the specific characteristics of the subject area and the proposals themselves, and a probability matrix which describes the compatibility of initial proposals. Next, we construct a plurality of intermediate proposals that use random combinations of all original proposals and a selected method, which determines the values of random characteristics of interim proposals. A utility function is used for the selection of significant proposals. The process is repeated until a compromise decision is reached. Computer implementation of the model is tested on the example of coalition governments formation with and without consideration of coalition parties’ compatibility.
1. Rozinata A., Wynnb M. T., Aalsta W. M. P., Hofstedeb A. H. M., Fidgeb C. J. Workflow simulation for operational decision support // Data & Knowledge Engineering. Sixth International Conference on Business Process Management. Volume 68, Issue 9, September 2009. P. 834-850.
2. Shennon, R. Imitacionnoe modelirovanie sistem — iskusstvo i nauka [Systems Simulation: The Art and Science]. M.: Mir, 1978. 418 p. (in Russian).
3. Bartholomew, D.J. Stochastic models for social processes. J. Wiley, 1973. 411 p. 4. Gilbert, N., Troitzsch, K. Simulation for the social scientist. Open University Press, McGrow-Hill Education, 2005. 312 p.
5. Golder, M., Golder, S., Siegel, D. Modeling the Institutional Foundations of Parliamentary Government Formation. Journal of Politics. 2012. № 74. Pp. 427-445.
6. Miller, J.H., Page, S.E. Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton. NY: Princeton University Press, 2007. 284 p.
7. Mosteller, F. Stochastic Models for the Learning Process. Proceedings of the American Philosophical Society. 1958. Vol. 102 (1). Pp. 53-59.
8. Vasil'eva, T.P., Myznikova, B.I., Rusakov, S.V. Stochastic Modelling of Urban Formation Process. Large-scale Systems Control. 2012. № 37. Pp. 164-179. (in Russian).
9. Zaharov, A.A., Zaharova, I.G. Simulation for Analysis of Preferences. Vestnik Tjumenskogo gosudarstvennogo universiteta — Tyumen State University Herald. 2011. № 7. Pp. 172-174.
10. Zaharova, I.G., Pushkarev, A.N. Software for the Dynamic Integrated Expert System of Support of Decision-Making in Marketing. Vestnik Tjumenskogo gosudarstvennogo universiteta — Tyumen State University Herald. 2012. № 4. Pp. 151-155. (in Russian).
11. Penn, E.M. A Model of Farsighted Voting. American Journal of Political Science. 2009.
Vol. 53 (1). Pp. 36–54.
12. Schofield, N., Sened, I. Multiparty Democracy: Elections and Legislative Elections.NY: Cambridge University Press, 2006. 258 p.