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  Evolutionary multigame with conformists and profiteers based on dynamic complex networks

Pi, B., Zeng, Z., Feng, M., Kurths, J. (2022): Evolutionary multigame with conformists and profiteers based on dynamic complex networks. - Chaos, 32, 2, 023117.
https://doi.org/10.1063/5.0081954

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 Creators:
Pi, Bin1, Author
Zeng, Ziyan1, Author
Feng, Minyu1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Evolutionary game on complex networks provides a new research framework for analyzing and predicting group decision-making behavior in an interactive environment, in which most researchers assumed players as profiteers. However, current studies have shown that players are sometimes conformists rather than profit-seeking in society, but most research has been discussed on a simple game without considering the impact of multiple games. In this paper, we study the influence of conformists and profiteers on the evolution of cooperation in multiple games and illustrate two different strategy-updating rules based on these conformists and profiteers. Different from previous studies, we introduce a similarity between players into strategy-updating rules and explore the evolutionary game process, including the strategy updating, the transformation of players’ type, and the dynamic evolution of the network structure. In the simulation, we implement our model on scale-free and regular networks and provide some explanations from the perspective of strategy transition, type transition, and network topology properties to prove the validity of our model. The study of network evolutionary games can provide a new perspective for explaining cooperation in society. Our task is to incorporate conformists and multigames into the traditional evolutionary game, which are more consistent with reality. Based on this model, this paper proposes two different strategy-updating rules and investigates their impact on the evolution of cooperation in the network. In addition, we make an interpretation of the simulation results in terms of strategy transition, type transition, and network topology properties. Our work may shed some new light on the study of network evolutionary games with conformists and multigames.

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Language(s): eng - English
 Dates: 2022-02-162022-02
 Publication Status: Finally published
 Pages: 9
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0081954
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Model / method: Game Theory
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 32 (2) Sequence Number: 023117 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)