Chaos in social learning with multiple true states

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...

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Bibliographic Details
Published in:Physica A 2013-11, Vol.392 (22), p.5786-5792
Main Authors: Fang, Aili, Wang, Lin, Zhao, Jiuhua, Wang, Xiaofan
Format: Article
Language:English
Subjects:
Chaos
Chaos theory
Consensus
Evolution
Exponents
Learning
Lyapunov exponents
Mathematical models
Multiple true states
Opinion dynamics
Oscillations
Social learning
Statistical mechanics
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Summary: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. •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.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2013.07.042