Mixed Strategies in Vector Optimization and Germeier’s Convolution
- Authors: Novikova N.M.1, Pospelova I.I.2
-
Affiliations:
- Dorodnicyn Computing Center, Federal Research Center for Computer Science and Control, Russian Academy of Sciences
- Faculty of Computational Mathematics and Cybernetics, Moscow State University
- Issue: Vol 58, No 4 (2019)
- Pages: 601-615
- Section: Systems Analysis and Operations Research
- URL: https://journals.rcsi.science/1064-2307/article/view/220434
- DOI: https://doi.org/10.1134/S1064230719040129
- ID: 220434
Cite item
Abstract
The simplest two-criteria examples of a vector optimization problem and a zero-sum game are considered to study the adequacy of using mixed strategies if the linear convolution is replaced by the Germeier’s convolution (the inverse logical convolution) for parametrizing the set of optimal solutions or values of the game and also for estimating the payoffs of all participants. It is shown that the linear convolution yields different results in a comparison with the averaged inverse logical convolution. The issues of stochastic vector optimization and various conceptual formalizations for the value of multi-criteria mixed strategies games are discussed.
About the authors
N. M. Novikova
Dorodnicyn Computing Center, Federal Research Center for Computer Science and Control,Russian Academy of Sciences
Email: ipospelova05@yandex.ru
Russian Federation, Moscow, 119333
I. I. Pospelova
Faculty of Computational Mathematics and Cybernetics, Moscow State University
Author for correspondence.
Email: ipospelova05@yandex.ru
Russian Federation, Moscow, 119991