Essential Components of the Set of Weakly Pareto-Nash Equilibrium Points for Multiobjective Generalized Games in Two Different Topological Spaces

2005 ◽  
Vol 124 (2) ◽  
pp. 387-405 ◽  
Author(s):  
Z. Lin
Keyword(s):  
Nash Equilibrium ◽  
Equilibrium Points ◽  
Topological Spaces ◽  
Nash Equilibrium Points ◽  
Essential Components ◽  
Generalized Games ◽  
Multiobjective Generalized Games

Existence of Saddle Points and Nash Equilibrium Points for Differential Games

1975 ◽  
Vol 13 (5) ◽  
pp. 977-980 ◽  
Author(s):  
T. Parthasarathy ◽  
T. E. S. Raghavan
Keyword(s):  
Nash Equilibrium ◽  
Differential Games ◽  
Equilibrium Points ◽  
Saddle Points ◽  
Nash Equilibrium Points

scholarly journals GAME MODEL OF ONTOLOGICAL PROJECT SUPPORT

2021 ◽  
Vol 1 (1) ◽  
pp. 172-183
Author(s):  
P. Kravets ◽  
V. Lytvyn ◽  
V. Vysotska
Keyword(s):  
Nash Equilibrium ◽  
Virtual Teams ◽  
Stochastic Approximation ◽  
Intelligent Agents ◽  
Stochastic Game ◽  
Equilibrium Points ◽  
Experimental Studies ◽  
Game Model ◽  
Network Marketing ◽  
Nash Equilibrium Points

Context. In today’s information society with advanced telecommunications through mobile devices and computer networks, it is important to form a variety of virtual organizations and communities. Such virtual associations of people by professional or other interests are designed to quickly solve various tasks: to perform project tasks, create startups to attract investors, network marketing, distance learning, solving complex problems in science, economics and public administration , construction of various Internet services, discussion of political and social processes, etc. Objective of the study is to develop an adaptive Markov recurrent method based on the stochastic approximation of the modified condition of complementary non-rigidity, valid at Nash equilibrium points for solving the problem of game coverage of projects. Method. In this work the multiagent game model for formation of virtual teams of executors of projects on the basis of libraries of subject ontologies is developed. The competencies and abilities of agents required to carry out projects are specified by sets of ontologies. Intelligent agents randomly, simultaneously and independently choose one of the projects at discrete times. Agents who have chosen the same project determine the current composition of the team of its executors. For agents’ teams, a current penalty is calculated for insufficient coverage of competencies by the combined capabilities of agents. This penalty is used to adaptively recalculate mixed player strategies. The probabilities of selecting those teams whose current composition has led to a reduction in the fine for non-coverage of ontologies are increasing. During the repetitive stochastic game, agents will form vectors of mixed strategies that will minimize average penalties for non-coverage of projects. Results. For solve the problem of game coverage of projects, an adaptive Markov recurrent method based on the stochastic approximation of the modified condition of complementary non-rigidity, valid at Nash equilibrium points, was developed. Conclusions. Computer simulation confirmed the possibility of using the stochastic game model to form teams of project executors with the necessary ontological support in conditions of uncertainty. The convergence of the game method is ensured by compliance with the fundamental conditions and limitations of stochastic optimization. The reliability of experimental studies is confirmed by the repeatability of the results obtained for different sequences of random variables.

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