Social Learning in Networks: Theory and Experiments
62 Pages Posted: 21 Sep 2013 Last revised: 1 Aug 2014
Date Written: December 27, 2013
Abstract
This paper presents a non-Bayesian model of social learning in networks in an environment with a finite set of actions. We conduct a laboratory experiment in which participants play an urn-guessing game over several decision rounds while observing the previous choices of the network members to whom they are connected. We identify three properties of individual choice revision: consistency, monotonicity and identity independence. We consider the class of revision functions satisfying such properties and establish that consensus occurs in arbitrary strongly connected networks if and only if the revision functions of all agents are identical and preference-based. Thus, consensus is hard to achieve, which is supported by evidence from our experiment. The theoretical prediction differs sharply from the existing results in the Bayesian and non-Bayesian literature.
Keywords: social learning, networks, experiments, consensus, information aggregation, herding
JEL Classification: C91, C92, D83, D85
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Persuasion Bias, Social Influence, and Uni-Dimensional Opinions
By Peter M. Demarzo, Jeffrey Zwiebel, ...
-
Naive Learning in Social Networks: Convergence, Influence and Wisdom of Crowds
By Matthew O. Jackson and Benjamin Golub
-
Bayesian Learning in Social Networks
By Daron Acemoglu, Munther Dahleh, ...
-
Bayesian Learning in Social Networks
By Ilan Lobel, Munther Dahleh, ...
-
Opinion Dynamics and Learning in Social Networks
By Daron Acemoglu and Asuman E. Ozdaglar
-
Rational Social Learning by Random Sampling
By Lones Smith and Peter Norman Sorensen
-
Information Percolation in Segmented Markets
By Darrell Duffie, Gustavo Manso, ...
-
Information Percolation in Segmented Markets
By Darrell Duffie, Semyon Malamud, ...
-
How Homophily Affects the Speed of Learning and Best Response Dynamics
By Benjamin Golub and Matthew O. Jackson
-
Spread of (Mis)Information in Social Networks
By Daron Acemoglu, Asuman E. Ozdaglar, ...