Temporal Network Epistemology: on Reaching Consensus in Real World Setting

TL;DR

This paper introduces a temporal network epistemology model to simulate learning in dynamic social networks, revealing how network changes influence consensus formation and uncovering new phenomena in collective decision processes.

Contribution

It presents a novel temporal network epistemology framework and demonstrates its impact on understanding consensus dynamics in evolving social networks.

Findings

Network temporal dynamics significantly affect learning outcomes.

Uninformed agents and multiple consensus states can emerge in dynamic networks.

Structural changes can facilitate reaching consensus even without additional information.

Abstract

This work develops the concept of temporal network epistemology model enabling the simulation of the learning process in dynamic networks. The results of the research, conducted on the temporal social network generated using the CogSNet model and on the static topologies as a reference, indicate a significant influence of the network temporal dynamics on the outcome and flow of the learning process. It has been shown that not only the dynamics of reaching consensus is different compared to baseline models but also that previously unobserved phenomena appear, such as uninformed agents or different consensus states for disconnected components. It has been also observed that sometimes only the change of the network structure can contribute to reaching consensus. The introduced approach and the experimental results can be used to better understand the way how human communities collectively solve both complex problems at the scientific level and to inquire into the correctness of less complex but common and equally important beliefs' spreading across entire societies.