Opinions and Networks: How Do They Effect Each Other
The topic of this study is two-fold and two models are presented. For the first part, I propose a non-linear learning algorithm that takes into account both proximity of opinions and network effects. Agents reach consensus of a final opinion that can be estimated given initial conditions under star and small-world networks. However, when the network structure is scale-free, simulation results show rather chaotic patterns. In the second half of this paper, a two-stage endogenous network formation mechanism is introduced. Opinion closeness is critical in establishing links. Existing neighbors also play an important role in connecting to new neighbors, which, combined with a growing population, contributes to a power-law degree distribution with coefficients that fit empirical findings extremely well. The correlation between opinion and degree is illustrated and formalized as well.
KeywordsSocial learning Consensus Complex network Network dynamics Simulation
JEL ClassificationD83 D85 C63
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