Social Learning in a World of Friends Versus Connected Strangers: A Theoretical Model with Experimental Evidence

Abstract

Networks and the relationships embedded in them are critical determinants of how people communicate, form beliefs, and behave. E-commerce platforms such as Amazon and eBay have made actions of "strangers" more observable to others. More recently, social media websites such as Facebook and Google Plus have created networks of "friends", and the actions of these friends have become more visible than ever before to consumers. This dissertation develops an analytical model to examine how social learning occurs in different types of networks. Specifically, I examine the pure-strategy perfect Bayesian equilibrium of observational learning in a friend-network vs. a stranger-network. In this model, each individual makes an adopt-or-reject decision about a product after receiving a private signal regarding the underlying quality of the product and observing past actions of other individuals in the network. Grounded on the homophily theory in sociology, the degree of network heterogeneity defines the key difference between a friend-network and a stranger-network. I show a threshold effect of network size regarding which network carries more valuable information: when the network size is small, a friend-network carries more valuable information than a stranger-network does. But when the network size gets larger, a stranger-network dominates a friend-network. This suggests two competing effects of network homogeneity on social learning: individual preference effects and social conforming effects. I also test key implications from theoretical results using experiments to demonstrate internal validity and enhance insights on social learning in networks. I found that experimental results are in line with predictions from the theoretical model.