Abstract
Bayesian learning is a rational and effective strategy in the opinion dynamic process. In this paper, we theoretically prove that individual Bayesian learning can realize asymptotic learning and we test it by simulations on the Zachary network. Then, we propose a Bayesian social learning model with signal update strategy and apply the model on the Zachary network to observe opinion dynamics. Finally, we contrast the two learning strategies and find that Bayesian social learning can lead to asymptotic learning more faster than individual Bayesian learning.
Highlights
We all have our own opinions on various topics of social issues. e opinions are formed by an evolutionary process in social context [1,2,3]
In the individual Bayesian learning process, individuals update their opinions by Bayesian law to achieve asymptotic learning
The individual will continuously adjust his opinion according to the signals received by himself and by his neighbors. erefore, we consider opinion dynamics with Bayesian law in a social network background, which is called Bayesian social learning
Summary
We all have our own opinions on various topics of social issues. e opinions are formed by an evolutionary process in social context [1,2,3]. Non-Bayesian learning refers to individuals updating their opinions by non-Bayesian strategy, such as linear combination of opinions of the neighbors [5, 15, 16] and various game theories in social network [17, 18]. Most of these models try to explore how the society could achieve group consensus. Considering people’s crowd psychology, we put forward a signal update strategy which means that people adjust their observations to meet with the most people Combined with this signal update strategy, we propose a Bayesian social learning model to study the opinion dynamics under social environment.
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