Common Knowledge on Networks

TL;DR

This paper investigates how network properties like clustering and betweenness centrality influence the ability to achieve common knowledge in social networks, revealing signals that distinguish true coordination from contagion and highlighting inequalities in success.

Contribution

It demonstrates that specific network metrics can predict successful common knowledge tasks and differentiate them from simple contagion processes.

Findings

Network properties signal success in common knowledge tasks.

Distinct network signals differentiate true coordination from contagion.

Common knowledge tasks can create inequalities in success.

Abstract

Common knowledge of intentions is crucial to basic social tasks ranging from cooperative hunting to oligopoly collusion, riots, revolutions, and the evolution of social norms and human culture. Yet little is known about how common knowledge leaves a trace on the dynamics of a social network. Here we show how an individual's network properties---primarily local clustering and betweenness centrality---provide strong signals of the ability to successfully participate in common knowledge tasks. These signals are distinct from those expected when practices are contagious, or when people use less-sophisticated heuristics that do not yield true coordination. This makes it possible to infer decision rules from observation. We also find that tasks that require common knowledge can yield significant inequalities in success, in contrast to the relative equality that results when practices spread by contagion alone.