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Chaos in social learning with multiple true states

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Listed:
  • Fang, Aili
  • Wang, Lin
  • Zhao, Jiuhua
  • Wang, Xiaofan

Abstract

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.

Suggested Citation

  • Fang, Aili & Wang, Lin & Zhao, Jiuhua & Wang, Xiaofan, 2013. "Chaos in social learning with multiple true states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5786-5792.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:22:p:5786-5792
    DOI: 10.1016/j.physa.2013.07.042
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    References listed on IDEAS

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    Cited by:

    1. Fang, Aili, 2021. "The influence of communication structure on opinion dynamics in social networks with multiple true states," Applied Mathematics and Computation, Elsevier, vol. 406(C).
    2. Fang, Aili & Wang, Lin & Wei, Xinjiang, 2019. "Social learning with multiple true states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 375-386.

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