Consensus in complex networks with noisy agents and peer pressure
Consensus in complex networks with noisy agents and peer pressure
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DOI：
10.1016/j.physa.2022.128263
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
C. Griffin;A. Squicciarini;Feiran Jia
中科院分区：
文献类型：
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作者：
C. Griffin;A. Squicciarini;Feiran Jia
在本文中，我们研究了连通图上的离散时间共识模型，该模型具有单调增加的同伴压力和掩盖隐藏状态的噪声扰动输出。我们假设每个智能体都保持恒定的隐藏状态，并且呈现动态输出，该输出受到从均值零分布中抽取的随机噪声的扰动。我们表明，在对等压力项不断增加的某些假设下，随着时间趋于无穷大，在极限内可以确保达成共识，并且还表明，即使模型动态和输出已知，也无法准确恢复隐藏状态。分布的确切性质是针对简单的两顶点图计算的，并且发现的结果可以（根据经验）推广到更复杂的图结构。
In this paper we study a discrete time consensus model on a connected graph with monotonically increasing peer-pressure and noise perturbed outputs masking a hidden state. We assume that each agent maintains a constant hidden state and a presents a dynamic output that is perturbed by random noise drawn from a mean-zero distribution. We show consensus is ensured in the limit as time goes to infinity under certain assumptions on the increasing peer-pressure term and also show that the hidden state cannot be exactly recovered even when model dynamics and outputs are known. The exact nature of the distribution is computed for a simple two vertex graph and results found are shown to generalize (empirically) to more complex graph structures.