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Dynamical analysis of evolutionary public goods game on signed networks.
Zhong, Xiaowen; Huang, Guo; Wang, Ningning; Fan, Ying; Di, Zengru.
Affiliation
  • Zhong X; School of Systems Science, Beijing Normal University, 100875 Beijing, China.
  • Huang G; School of Systems Science, Beijing Normal University, 100875 Beijing, China.
  • Wang N; School of Systems Science, Beijing Normal University, 100875 Beijing, China.
  • Fan Y; School of Systems Science, Beijing Normal University, 100875 Beijing, China.
  • Di Z; School of Systems Science, Beijing Normal University, 100875 Beijing, China.
Chaos ; 32(2): 023107, 2022 Feb.
Article in En | MEDLINE | ID: mdl-35232045
ABSTRACT
In evolutionary dynamics, the population structure and multiplayer interactions significantly impact the evolution of cooperation levels. Previous works mainly focus on the theoretical analysis of multiplayer games on regular networks or pairwise games on complex networks. Combining these two factors, complex networks and multiplayer games, we obtain the fixation probability and fixation time of the evolutionary public goods game in a structured population represented by a signed network. We devise a stochastic framework for estimating fixation probability with weak mistrust or strong mistrust mechanisms and develop a deterministic replicator equation to predict the expected density of cooperators when the system evolves to the equilibrium on a signed network. Specifically, the most interesting result is that negative edges diversify the cooperation steady state, evolving in three different patterns of fixed probability in Erdös-Rényi signed and Watts-Strogatz signed networks with the new "strong mistrust" mechanism.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Biological Evolution / Game Theory Type of study: Prognostic_studies Language: En Journal: Chaos Journal subject: CIENCIA Year: 2022 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Biological Evolution / Game Theory Type of study: Prognostic_studies Language: En Journal: Chaos Journal subject: CIENCIA Year: 2022 Document type: Article Affiliation country: China
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