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| A Social Learning Model Based on Composite Strategy |
| LIU Kunkun, WEI Xinjiang, FANG Aili
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| School of Mathematics and Statistics Science, Ludong University, Yantai 264025, China |
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Abstract In order to study social learning in complex social networks, a social learning model based on composite belief update strategy is proposed by considering the heterogeneity and complexity of the social individuals. At each time step, individuals in the model choose Bayesian update strategy or the update strategy based on their neighbors’ beliefs according to the strategy selection probability. The simulation results show that under some conditions such as the positive strategy selection probability, all the social individuals can achieve asymptotic learning. Furthermore, the learning speed is relative to the strategy selection probability, the larger the strategy selection probability is, the faster the learning speed will be.
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Received: 07 November 2014
Published: 22 June 2026
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