Cooperation is an effective manner to enable different elements of complex networks to work well. In this work, we propose a coevolution mechanism of learning willingness in the network population: an agent will be more likely to imitate a given neighbor’s strategy if her payoff is not less than the average performance of all her neighbors. Interestingly, increase of learning willingness will greatly promote cooperation even under the environment of extremely beneficial temptation to defectors. Through a microscopic analysis, it is unveiled that cooperators are protected due to the appearance of large-size clusters. Pair approximation theory also validates all these findings. Such an adaptive mechanism thus provides a feasible solution to relieve social dilemmas and will inspire further studies.
Skip Nav Destination
Research Article| November 13 2019
Adaptive willingness resolves social dilemma in network populations
Special Collection: Focus Issue: Complex Network Approaches to Cyber-Physical Systems
Zhao Song ;
Peican Zhu, Zhao Song, Hao Guo, Zhen Wang, Tianyun Zhao; Adaptive willingness resolves social dilemma in network populations. Chaos 1 November 2019; 29 (11): 113114. https://doi.org/10.1063/1.5093046
Download citation file: