Chaos in social learning with multiple true states

Chaos in social learning with multiple true states
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具有多种真实状态的社会学习中的混沌

DOI:
10.1016/j.physa.2013.07.042
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发表时间:
2013-11
期刊:
Physica A: Statistical Mechanics and Its Applications
影响因子:
--
通讯作者:
Wang, Xiaofan
Wang, Xiaofan
中科院分区:
其他
文献类型:
--
作者:
Fang, Aili;Wang, Lin;Zhao, Jiuhua;Wang, Xiaofan

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大多数现有的社会学习模型都假设只有一种潜在的真实状态。在这项工作中,我们考虑了一个具有多个真实状态的社会学习模型,在该模型中,不同组中的代理接收由其对应的潜在真实状态生成的不同信号序列。每个智能体通过结合他收到的外部信号的理性自我调整和邻居根据他们的交流而产生的影响来更新他的信念。我们观察到信念演化中的混沌振荡,这意味着通过计算最大Lyapunov指数和Hurst指数都不能正确地学习两个真实状态。
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.
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