We study a dynamic model of opinion formation in social networks. In our model, boundedly rational agents update opinions by averaging over their neighbors' expressed opinions, but may misrepresent their own opinion by conforming or counter-conforming with their neighbors. We show that an...

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'...

We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents naïvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. The weights are fixed in the sense of representing average frequency and intensity of...

We investigate how the selection process of a leader affects team performance with respect to social learning. We use a lab experiment in which an incentivized guessing task is repeated in a star network with the leader at the center. Leader selection is either based on competence, on...