We investigate the role of manipulation in a model of opinion formation. Agents repeatedly communicate with their neighbors in the social network, can exert effort to manipulate the trust of others, and update their opinions about some common issue by taking weighted averages of neighbors'...

The novelty of our model is to combine models of collective action on networks with models of social learning. Agents are connected according to an undirected graph, the social network, and have the choice between two actions: either to adopt a new behavior or technology or stay with the default...

We study a model of social learning in networks where the dynamics of beliefs are driven by conversations of dissonance-minimizing agents. Given their current beliefs, agents make statements, tune them to the statements of their associates, and then revise their beliefs. We characterize the...

In markets with search frictions, consumers can acquire information about goods either through costly search or from friends via word-of-mouth (WOM) communication. How do sellers’ market power react to a very large increase in the number of consumers’ friends with whom they engage in WOM?...

Consumers can acquire information through their own search efforts or through their social network. Information diffusion via word-of-mouth communication leads to some consumers free-riding on their “friends” and less information acquisition via active search. Free-riding also has an...

We develop a model of information exchange through communication and investigate its implications for information aggregation in large societies. An \textit{underlying state} determines payoffs from different actions. Agents decide which others to form a costly \textit{communication link} with,...

We develop a dynamic framework of strategic information transmission through cheap talk in a social network. Privately informed agents have different preferences about the action to be implemented by each agent and repeatedly communicate with their neighbors in the network. We first characterize...

We study the impact of bots on social learning in a social network setting. Regular agents receive independent noisy signals about the true value of a variable and then communicate in a network. They na¨ıvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. Bots...

We study in the laboratory the impact of private information revelation on the selection of partners when forming individual networks. Our experiment combines a "network game" and a "public-good game". In the network game, individuals decide with whom to form a link with, while in the...

We study in the laboratory the impact of private information revelation on the selection of partners when forming individual networks. Our experiment combines a "network game" and a "public-good game". In the network game, individuals decide with whom to form a link with, while in the...