Chaos in social learning with multiple true states

Fang A.; Wang L.; Zhao J.; Wang X.

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Chaos in social learning with multiple true states

机译：具有多个真实状态的社会学习中的混乱

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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.

机译：现有的大多数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，我们考虑具有多个真实状态的社会学习模型，其中不同组中的代理会接收由其相应的基础真实状态生成的不同信号序列。每个特工通过结合他所接收的外部信号以及邻居根据邻居的影响而进行的理性自我调整来更新自己的信念。我们在信念演化过程中观察到混沌振荡，这意味着通过计算最大的Lyapunov指数和Hurst指数，无法正确学习任何真实状态。

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《Physica, A. Statistical mechanics and its applications》 |2013年第22期|共7页
作者
Fang A.; Wang L.; Zhao J.; Wang X.;
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正文语种 eng
中图分类物理学;
关键词
Chaos; Consensus; Multiple true states; Opinion dynamics; Social learning;

机译：混沌;共识;多个真实状态;意见动态;社会学习;

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Chaos in social learning with multiple true states

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