Try out the following online demos for our Polarization Model. There is one small version with 200 agents and a big one with 10000 agents.
They relate to our recent paper:
Opinion Polarization by Learning from Social Feedback, S. Banisch & E. Olbrich Submitted to the Social Simulation Conference, Dublin (2017).
The paper explores a new mechanism to explain polarization phenomena in opinion dynamics. The model is based on the idea that agents evaluate alternative views on the basis of the social feedback obtained on expressing them. A high support of the favored and therefore expressed opinion in the social environment, is treated as a positive social feedback which reinforces the value associated to this opinion. In this paper we concentrate on the model with dyadic communication and encounter probabilities defined by an unweighted, time-homogeneous network. The model captures polarization dynamics more plausibly compared to bounded confidence opinion models and avoids extensive opinion flipping usually present in binary opinion dynamics. We perform systematic simulation experiments to understand the role of network connectivity for the emergence of polarization.