Dissonance minimization and conversation in social networks
研究了社交网络中个体为减少认知不协调而调整言论的行为，发现这种调整能加速信念传播和收敛，但也会改变长期信念的影响力分布，甚至阻碍共识达成。
We are examining social learning in networks, where agents aim to minimize cognitive dissonance resulting from disagreement by adjusting their statements in conversations to align with those of their associates, rather than truthfully sharing their beliefs. Our analysis investigates the impact of this adjustment, known as audience tuning, on belief revision, limiting beliefs, consensus conditions, and convergence speed. Our findings demonstrate that audience tuning facilitates extensive belief propagation beyond immediate associates, resulting in faster convergence in most of the societies considered. It also leads to a redistribution of influences on long-run beliefs, favoring agents with lower dissonance sensitivity. We also show that endogenous changes in the network, driven by dissonance minimization, can impede society from reaching a consensus.