Chaos in social learning with multiple true states

• We consider social learning with multiple true states.
• We give simulations of social learning with one true state and multiple true states under the same assumptions.
• Chaos exists in the social learning with multiple true states.
• Communications between groups with different underlying true states hinder asymptotic learning.

Most existing social learning models assume that there is only one underlying true state. In this work, we consider a social learning model with multiple true states, in which agents in different groups receive different signal sequences generated by their corresponding underlying true states. Each agent updates his belief by combining his rational self-adjustment based on the external signals he received and the influence of his neighbors according to their communication. We observe chaotic oscillation in the belief evolution, which implies that neither true state could be learnt correctly by calculating the largest Lyapunov exponents and Hurst exponents.