Network and timing effects in social learning

TL;DR

This paper examines how social network structure influences learning outcomes when agents make irreversible decisions based on private signals and neighbors' actions, revealing that network topology affects convergence to optimal actions.

Contribution

It demonstrates how different network structures impact social learning and decision convergence, highlighting the importance of network topology in collective decision-making.

Findings

On linear networks, agents fail to converge to the optimal action.

On regular directed tree networks, agents successfully learn the optimal action.

Network structure critically influences social learning outcomes.

Abstract

We consider a group of agents who can each take an irreversible costly action whose payoff depends on an unknown state. Agents learn about the state from private signals, as well as from past actions of their social network neighbors, which creates an incentive to postpone taking the action. We show that outcomes depend on network structure: on networks with a linear structure patient agents do not converge to the first-best action, while on regular directed tree networks they do.