Volume 143, Number 2, July 2023
|Number of page(s)||7|
|Section||Statistical physics and networks|
|Published online||17 July 2023|
Effect of update rule transition triggered by Q-learning algorithm in evolutionary prisoner's dilemma game involving extortion
1 School of Information Science and Engineering, Hebei University of Science and Technology Shijiazhuang 050018, PRC
2 Institute of Translational Medicine, Medical College Yangzhou University - Yangzhou, 225001, PRC
Received: 12 May 2023
Accepted: 4 July 2023
Most studies have shown that the heterogeneity of update rules has an important impact on evolutionary game dynamics. In the meanwhile, Q-learning algorithm has gained attention and extensive study in evolutionary games. Therefore, a mixed stochastic evolutionary game dynamic model involving extortion strategy is constructed by combining imitation and aspiration-driven updating rules. During the evolution of the model, individuals will use the Q-learning algorithm which is a typical self-reinforcement learning algorithm to determine which update rule to adopt. Herein, through numerical simulation analyses, it is found that the mixed stochastic evolutionary game dynamic model affected by the Q-learning algorithm ensures the survival of cooperators in the grid network. Moreover, the cooperators cannot form a cooperation cluster in the grid network but will form a chessboard-like distribution with extortioners to protect cooperators from the invasion of defectors. In addition, a series of results show that, before the evolution turns into steady state, our model increases the number of nodes utilizing the average aspiration-driven update rule, thereby promoting the emergence of chessboard-like distribution. Overall, our study may provide some interesting insights into the development of cooperative behavior in the real world.
© 2023 EPLA
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.