Information Horizons in a Complex World

The whole in a complex system is the sum of its parts, plus the interactions between the parts. Understanding social, biological, and economic systems therefore often depends on understanding their patterns of interactions—their networks. In this thesis, the approach is to understand complex systems by making simple network models with nodes and links. It is first of all an attempt to investigate how the communication over the network affects the network structure and, vice versa, how the network structure affects the conditions for communication. To explore the local mechanism behind network organization, we used simplified social systems and modeled the response to communication. Low communication levels resulted in random networks, whereas higher communication levels led to structured networks with most nodes having very few links and a few nodes having very many links. We also explored various models where nodes merge into bigger units, to reduce communication costs, and showed that these merging models give rise to the same kind of structured networks. In addition to this modeling of communication networks, we developed new ways to measure and characterize real-world networks. For example, we found that they in general favor communication on short distance, two-three steps away in the network, within what we call the information horizon.