Multi-Issue Social Learning

TL;DR

This paper introduces the 'celebrities graph', a novel social network structure that prevents information cascades and enables effective information aggregation in large, randomly ordered populations facing multiple decision issues.

Contribution

The paper proposes the 'celebrities graph' as a new observability structure that ensures proper social learning and information aggregation in complex, dynamic environments.

Findings

The 'celebrities graph' prevents information cascades.

Proper information aggregation is achieved in large populations.

The graph works under random agent decision orders and multiple issues.

Abstract

We consider social learning where agents can only observe part of the population (modeled as neighbors on an undirected graph), face many decision problems, and arrival order of the agents is unknown. The central question we pose is whether there is a natural observability graph that prevents the information cascade phenomenon. We introduce the `celebrities graph' and prove that indeed it allows for proper information aggregation in large populations even when the order at which agents decide is random and even when different issues are decided in different orders.