Dynamical analysis of evolutionary public goods game on signed networks
Xiaowen Zhong1, Guo Huang1, Ningning Wang1
1School of Systems Science, Beijing Normal University, 100875 Beijing, China.
Chaos (Woodbury, N.Y.)
|March 2, 2022
Summary
This study explores cooperation in evolutionary public goods games on signed networks. Negative edges diversify cooperation, leading to three distinct fixation probability patterns under a strong mistrust mechanism.
Area of Science:
- Evolutionary game theory
- Network science
- Computational social science
Background:
- Population structure and multiplayer interactions shape cooperation evolution.
- Existing research often focuses on regular networks for multiplayer games or complex networks for pairwise games.
- Integrating complex networks with multiplayer interactions remains a key challenge.
Purpose of the Study:
- To investigate the evolutionary public goods game on signed networks, combining complex network structures with multiplayer interactions.
- To analyze the fixation probability and fixation time of cooperation in structured populations.
- To understand the impact of mistrust mechanisms on cooperation dynamics.
Main Methods:
- Devised a stochastic framework to estimate fixation probability under weak and strong mistrust mechanisms.
- Developed a deterministic replicator equation to predict the equilibrium density of cooperators.
- Utilized signed Erdös-Rényi and Watts-Strogatz networks to model population structure.
Main Results:
- Identified that negative edges in signed networks diversify the cooperation steady state.
- Observed three distinct patterns of fixation probability in Erdös-Rényi and Watts-Strogatz signed networks.
- Demonstrated the significant impact of the "strong mistrust" mechanism on cooperation dynamics.
Conclusions:
- Signed networks and mistrust mechanisms introduce novel dynamics to cooperation evolution.
- The study provides a framework for analyzing cooperation in complex, structured populations.
- Findings highlight the potential for diverse cooperation outcomes based on network topology and interaction rules.
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