Rapid Information Transfer in Networks with Delayed Self Reinforcement
This provides a mechanism for faster information transfer in networks, relevant for understanding and designing networks with rapid response.
The paper shows that self reinforcement, where each agent augments its neighbor-averaged information update with its previous update, increases information-transfer rate without requiring higher individual update rates and captures superfluid-like information transfer observed in nature.
The cohesiveness of response to external stimuli depends on rapid distortion-free information transfer across the network. Aligning with the information from the network has been used to model such information transfer. Nevertheless, the rate of such diffusion-type, neighbor-based information transfer is limited by the update rate at which each individual can sense and process information. Moreover, models of the diffusion-type information transfer do not predict the superfluid-like information transfer observed in nature. The contribution of this article is to show that self reinforcement, where each individual augments its neighbor-averaged information update using its previous update, can (i) increase the information-transfer rate without requiring an increased, individual update-rate; and (ii) capture the observed superfluid-like information transfer. This improvement in the information-transfer rate without modification of the network structure or increase of the bandwidth of each agent can lead to better understanding and design of networks with fast response.