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Evolutionary Dynamics of Strategies without Complete Information on Complex Networks
Author(s) -
Zhang Jianlei,
Li Zhiqi,
Xu Zimin,
Zhang Chunyan
Publication year - 2020
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1874
Subject(s) - complex network , computer science , stability (learning theory) , evolving networks , evolutionary dynamics , replicator equation , evolutionarily stable strategy , evolutionary game theory , game theory , scale free network , state (computer science) , distribution (mathematics) , mathematical optimization , mathematical economics , mathematics , machine learning , population , demography , algorithm , sociology , world wide web , mathematical analysis
As for the behaviors of multi‐agent system, recent years have witnessed the growing interest in the study of cooperative behaviors by the aid of evolutionary game dynamics on complex networks. Thereinto, the updating rules deciding the evolution of strategies will significantly influence the steady state distribution of the system. The strategy updating rooted in the pursuit of larger benefits, will drive the system to evolve into the coexistence of different states or the domination by some strategies. To relax the often‐used rules required explicit knowledge of the exact payoffs, this paper describes a new approach of updating strategy based on switching probabilities, which is independent on players' payoffs and degrees. And then the equilibrium state of the strategy evolution in the networks is studied. Our work here provides a computationally feasible way of estimating the steady characteristics of the strategy adoption of agents situating on complex networks. The stability analysis elucidates two important features: (i) the takeover of cooperation can be enhanced by the appropriate settings of the switching probabilities between strategies and (ii) larger average degree and power exponent in the employed scale‐free network can make it easier for the coexistence of strategies. The results can help the design of initial strategy distribution of agents located on social networks to promote cooperation.