Opinion Dynamics with Local Interactions
During the last century many researchers investigated the way individuals form their opinions. The rapid growth of social networks in the recent years (Facebook,Twitter e.t.c) has further intensified this interest. To this day, a lot of models, on how our opinions evolve, have been proposed. In the huge majority of these models, each agent has to learn a large amount of opinions of other agents in order to update her opinion. In this thesis, we investigate the well studied Hegelsmann-Krause and Freidkin-Johson Model, under the constraint that each agent can learn a small amount of opinions of other agents. We propose three vatiations of these models, namely Network Hegelsmann-Krause, Random Hegelsmann-Krause and Limited Information Friedkin-Johson Model and we investigate their convergence properties.