Opinion Polarization by Learning from Social Feedback
This addresses polarization dynamics in social networks, offering an incremental affective-based mechanism compared to existing deliberative models.
The paper tackles the problem of explaining opinion polarization by proposing a model where agents learn from social feedback, leading to stable bi-polarization in modular networks without relying on negative influence or bounded confidence assumptions.
We explore a new mechanism to explain polarization phenomena in opinion dynamics in which agents evaluate alternative views on the basis of the social feedback obtained on expressing them. High support of the favored opinion in the social environment, is treated as a positive feedback which reinforces the value associated to this opinion. In connected networks of sufficiently high modularity, different groups of agents can form strong convictions of competing opinions. Linking the social feedback process to standard equilibrium concepts we analytically characterize sufficient conditions for the stability of bi-polarization. While previous models have emphasized the polarization effects of deliberative argument-based communication, our model highlights an affective experience-based route to polarization, without assumptions about negative influence or bounded confidence.