A Dynamic Opinion and Network Formation Model∗

Social networks have a profound influence on opinion formation, and at the same time opinion similarity and diversity can draw individuals together or drive them apart. In light of this, we propose a dynamic model in which individuals, belonging to 2 different groups, sequentially update both their opinions and their connections. We show that with probability one this dynamic process converges to a steady state, a state in which no one wants to change their opinions and add/delete their links. If small trembles occur during link creation and deletion, there are only two types of steady states of opinions. Intuitively, in the dynamic process, it could be either individuals’ opinions get closer with creation of more cross-group connections, or individuals’ opinions get further away with deletion of cross-group connections. Thus, in one type of steady states, the distance between individuals’ opinions is minimized (so a consensus if possible), which is consistent with the predictions of most models on social learning. More interestingly in the other type, the distance between individuals’ opinions is maximized, which is rarely predicted from a weighted-average or Bayesian learning model, but yet it is consistent with evidence on opinion polarization, such as the increasing polarization in ideology between political parties. ∗I thank Matthew O. Jackson and Songzi Du for very helpful discussions, and all mistakes remain mine. This preliminary and incomplete draft is prepared for submissions to summer conferences. Please contact me for an updated version. †Department of Economics, University of Washington. Email: [email protected].