Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/201110
Title: A Graphical Evolutionary Game Approach to Social Learning
Authors: Xuanyu Cao;K. J. Ray Liu
Year: 2017
Publisher: IEEE
Abstract: In this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game-theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady-state equilibria of the game and show that the evolutionarily stable states coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.
URI: http://localhost/handle/Hannan/201110
volume: 24
issue: 6
More Information: 765,
769
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7898499.pdf229.6 kBAdobe PDF
Title: A Graphical Evolutionary Game Approach to Social Learning
Authors: Xuanyu Cao;K. J. Ray Liu
Year: 2017
Publisher: IEEE
Abstract: In this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game-theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady-state equilibria of the game and show that the evolutionarily stable states coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.
URI: http://localhost/handle/Hannan/201110
volume: 24
issue: 6
More Information: 765,
769
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7898499.pdf229.6 kBAdobe PDF
Title: A Graphical Evolutionary Game Approach to Social Learning
Authors: Xuanyu Cao;K. J. Ray Liu
Year: 2017
Publisher: IEEE
Abstract: In this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game-theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady-state equilibria of the game and show that the evolutionarily stable states coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.
URI: http://localhost/handle/Hannan/201110
volume: 24
issue: 6
More Information: 765,
769
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7898499.pdf229.6 kBAdobe PDF