Cognitive Maps of Complex Systems Show Hierarchical Structure and Scale-Free Properties

Abstract

Many networks in natural and human-made systems exhibit scale-free properties and are small worlds. Now we show that people&#39;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&#39;s perceptions, especially when it comes to decision-making, conflict resolution, politics and management.