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摘要翻译：
自然和人为系统中的许多网络表现出无标度特性，是小世界。现在我们证明人们在认知图中对复杂系统的理解也遵循一种无标度拓扑(P_k=k^-λ,λ[1.24,3.03]；r^2<=0.95)。人们把注意力集中在少数属性上，总度的概率分布中的一条肥尾表明了这一点。这几个属性与系统中的许多其他变量有关。更多的属性几乎没有联系。尽管人们的平均距离（Wilcoxon符号秩检验，W=78,P=0.75)与相同大小和连接密度的随机网络没有什么不同，但认知地图中的无标度特性仍然存在。与随机网络相比，无标度特性体现在更高的层次结构上（Wilcoxon符号秩检验，W=12,P=0.03)。人们用相对简短的解释来描述系统。这些发现可能有助于我们更好地理解人们的看法，尤其是在决策、解决冲突、政治和管理方面。
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英文标题：
《Cognitive Maps of Complex Systems Show Hierarchical Structure and
Scale-Free Properties》
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作者：
Uygar Ozesmi and Can Ozan Tan
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最新提交年份：
2006
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Neurons and Cognition 神经元与认知
分类描述：Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触，皮层，神经元动力学，神经网络，感觉运动控制，行为，注意
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一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
Many networks in natural and human-made systems exhibit scale-free properties and are small worlds. Now we show that people's understanding of complex systems in their cognitive maps also follow a scale-free topology (P_k = k^-lambda, lambda [1.24,3.03]; r^2 <= 0.95). People focus on a few attributes, as indicated by a fat tail in the probability distribution of total degree. These few attributes are related with many other variables in the system. Many more attributes have very few connections. The scale-free properties in the cognitive maps of people arise despite the fact that their average distances are not different (Wilcoxon sign-rank test, W=78, p=0.75) than random networks of the same size and connection density. The scale-free property manifests itself in the higher hierarchical structure compared to random networks (Wilcoxon sign-rank test, W=12, p=0.03). People use relatively short explanations to describe systems. These findings may help us to better understand people's perceptions, especially when it comes to decision-making, conflict resolution, politics and management.
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PDF链接：
https://arxiv.org/pdf/q-bio/0612030
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