Algebraic formulation and strategy optimization for a class of evolutionary networked games via semi-tensor product method

Using the semi-tensor product method, this paper investigates the algebraic formulation and strategy optimization for a class of evolutionary networked games with “myopic best response adjustment” rule, and presents a number of new results. First, the dynamics of the evolutionary networked game is c...

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Veröffentlicht in:Automatica (Oxford) Jg. 49; H. 11; S. 3384 - 3389
Hauptverfasser: Guo, Peilian, Wang, Yuzhen, Li, Haitao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.11.2013
Elsevier
Schlagworte:
Algebra
Algebraic formulation
Algorithms
Applied sciences
Computer science; control theory; systems
Control system synthesis
Control theory. Systems
Dynamics
Evolutionary
Evolutionary networked game
Exact sciences and technology
Formulations
Game theory
Games
Linear and multilinear algebra, matrix theory
Mathematics
Operational research and scientific management
Operational research. Management science
Optimization
Sciences and techniques of general use
Semi-tensor product
Strategy
Strategy optimization
Semi-tensor product
Evolutionary networked game
Algebraic formulation
Strategy optimization
Evolutionary algorithm
Myopia
Control synthesis
Evolutionary game
Dynamical system
Game theory
Optimization
Algebraic method
Game network
Mathematical programming
ISSN:0005-1098, 1873-2836
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Zusammenfassung:Using the semi-tensor product method, this paper investigates the algebraic formulation and strategy optimization for a class of evolutionary networked games with “myopic best response adjustment” rule, and presents a number of new results. First, the dynamics of the evolutionary networked game is converted to an algebraic form via the semi-tensor product, and an algorithm is established to construct the algebraic formulation for the game. Second, based on the algebraic form, the dynamical behavior of evolutionary networked games is discussed, and some interesting results are presented. Finally, the strategy optimization problem is considered by adding a pseudo-player to the game, and a free-type control sequence is designed to maximize the average payoff of the pseudo-player. The study of an illustrative example shows that the new results obtained in this paper work very well.
Bibliographie:ObjectType-Article-2
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ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2013.08.008