Collective Intelligence in Dynamic Networks

TL;DR

This paper analyzes how dynamic network structures influence social learning and collective intelligence, revealing that network evolution can both enhance consensus or cause disagreement depending on the network's connectivity.

Contribution

It provides a new understanding of the effects of network dynamics on social learning, emphasizing the importance of initial structure and proposing a measure of homophily.

Findings

Dynamic networks can foster consensus in sparse structures.

Well-connected networks may experience slower learning and disagreement.

Initial network structure significantly impacts long-term beliefs.

Abstract

We revisit DeGroot learning to examine the robustness of social learning in dynamic networks -- networks that evolve randomly over time. Dynamics have double-edged effects depending on social structure: while they can foster consensus and boost collective intelligence in "sparse" networks, they can have adverse effects such as slowing down the speed of learning and causing long-run disagreement in "well-connected" networks. Collective intelligence arises in dynamic networks when average influence and trust remain balanced as society grows. We also find that the initial social structure of a dynamic network plays a central role in shaping long-run beliefs. We then propose a robust measure of homophily based on the likelihood of the worst network fragmentation.