Evolutionary investors’ power-based game on networks

Xu, Hedong; Tian, Chao; Xiao, Xuefeng; Fan, Shuxiang

doi:10.1016/j.amc.2018.02.041

Cited by 16 publications

(2 citation statements)

References 71 publications

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“…Bakker et al [29] and Stefan et al [30] established an evolution model of investors' investment behavior based on the investor's social network to analyze investor behavior and its impact on the stock market. Xu et al [31] proposed a weighted game model in investor network and found that the investor behavior evolution in WS small-world network is more stable than that in BA scale-free network. Khashanah and Alsulaiman [32] analyzed the performance of their investment behavior on the scale-free network by taking four types of investors, basic strategy traders, momentum traders, adaptive strategy traders, and zero-intelligence traders, into consideration.…”

Section: Introductionmentioning

confidence: 99%

A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network

et al. 2020

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This paper builds an evolution model of investors behavior based on the reinforcement learning in multiplex networks. Due to the heterogeneity of learning characteristics of bounded rational investors in investment decisions, we consider, respectively, the evolution mechanism of individual investors and institutional investors on the complex network theory and reinforcement learning theory. We perform mathematical analysis and simulation to further explain the evolution characteristics of investors behavior. The conclusions are drawn as follows: First, the intensity of returns competition among institutional investors and the forgetting effect both have an impact on the equilibrium of their evolution as to all institutional investors and individual investors. Second, the network topology significantly affects the behavioral evolution of individual investors compared with institutional investors.

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Section: Introductionmentioning

confidence: 99%

A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network

et al. 2020

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“…Evolutionary game dynamics combines game theory and nonlinear dynamics to describe the evolution of the frequencies of strategies in one or more large population [1,2]. It has edged into many elds such as networks population [3][4][5][6], economics [7,8], biology [9,10], management [11,12], and cooperative behaviors [13][14][15][16]. ere are many important evolutionary game dynamics such as replicator dynamics, imitative dynamics [17], best-response dynamics [18], and so on [19].…”

Section: Introductionmentioning

confidence: 99%

Bifurcation Analysis of Three‐Strategy Imitative Dynamics with Mutations

Tian

Zhang

2019

Complexity

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Evolutionary game dynamics is an important research, which is widely used in many fields such as social networks, biological systems, and cooperative behaviors. This paper focuses on the Hopf bifurcation in imitative dynamics of three strategies (Rock-Paper-Scissors) with mutations. First, we verify that there is a Hopf bifurcation in the imitative dynamics with no mutation. Then, we find that there is a critical value of mutation such that the system tends to an unstable limit cycle created in a subcritical Hopf bifurcation. Moreover, the Hopf bifurcation exists for other kinds of the considered mutation patterns. Finally, the theoretical results are verified by numerical simulations through Rock-Paper-Scissors game.

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Evolutionary investor sharing game on networks

Fan

Tian

et al. 2019

Applied Mathematics and Computation

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Evolutionary investors’ power-based game on networks

Cited by 16 publications

References 71 publications

A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network

A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network

Bifurcation Analysis of Three‐Strategy Imitative Dynamics with Mutations

Evolutionary investor sharing game on networks

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