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  An Evolutionary Game With the Game Transitions Based on the Markov Process

Feng, M., Pi, B., Deng, L.-J., Kurths, J. (2024): An Evolutionary Game With the Game Transitions Based on the Markov Process. - IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54, 1, 609-621.
https://doi.org/10.1109/tsmc.2023.3315963

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 Creators:
Feng, M.1, Author
Pi, B.1, Author
Deng, L.-J.1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: The psychology of the individual is continuously changing in nature, which has a significant influence on the evolutionary dynamics of populations. To study the influence of the continuously changing psychology of individuals on the behavior of populations, in this article, we consider the game transitions of individuals in evolutionary processes to capture the changing psychology of individuals in reality, where the game that individuals will play shifts as time progresses and is related to the transition rates between different games. Besides, the individual’s reputation is taken into account and utilized to choose a suitable neighbor for the strategy updating of the individual. Within this model, we investigate the statistical number of individuals staying in different game states and the expected number fits well with our theoretical results. Furthermore, we explore the impact of transition rates between different game states, payoff parameters, the reputation mechanism, and different time scales of strategy updates on cooperative behavior, and our findings demonstrate that both the transition rates and reputation mechanism have a remarkable influence on the evolution of cooperation. Additionally, we examine the relationship between network size and cooperation frequency, providing valuable insights into the robustness of the model.

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Language(s): eng - English
 Dates: 2023-10-032024-01-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/tsmc.2023.3315963
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Game Theory
 Degree: -

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Title: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 54 (1) Sequence Number: - Start / End Page: 609 - 621 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-systems-man-cybernetics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)