Human Information Processing in Complex Networks

In this work, we study the structure of real-world communication systems to understand how information can be rapidly and efficiently communicated to humans, for example from swarms of drones or other agents. Humans constantly receive information from systems of interconnected stimuli or concepts — from language and music to literature and science — yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient ways that humans process this information.

Here we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system’s network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly-connected modules — the two defining features of hierarchical organization. Together, these results suggest that rapid and efficient communication is constrained by the structural properties of communication systems, with implications for the design of optimal channels for robot-human and human-human information transmission.

Source: Lynn, C. W., Papadopoulos, L., Kahn, A. E., and Bassett, D. B. “Human information processing in complex networks.” In revision, Nature Physics <>.

Points of contact: Danielle S. Bassett (PI) and Christopher W. Lynn.