A Biased Inferential Naivety learning model for a network of agents

We propose a Biased Inferential Naivety social learning model. In this model, a group of agents tries to determine the true state of the world and make the best possible decisions. The agents have limited computational abilities. They receive noisy private signals about the true state and observe the history of their neighbors' decisions. The proposed model is rooted in the Bayesian method but avoids the complexity of fully Bayesian inference. In our model, the role of knowledge obtained from social observations is separated from the knowledge obtained from private observations. Therefore, the Bayesian inferences on social observations are approximated using inferential naivety assumption, while purely Bayesian inferences are made on private observations. The reduction of herd behavior is another innovation of the proposed model. This advantage is achieved by reducing the effect of social observations on agents' beliefs over time. Therefore, all the agents learn the truth, and the correct consensus is achieved effectively. In this model, using two cognitive biases, there is heterogeneity in agents' behaviors. Therefore, the growth of beliefs and the learning speed can be improved in different situations. Several Monte Carlo simulations confirm the features of the proposed model. The conditions under which the proposed model leads to asymptotic learning are proved.