Dynamic Opinion Aggregation: Long-Run Stability and Disagreement
提出一个非贝叶斯社会学习模型，用非线性观点聚合器刻画启发式和偏见，分析网络结构如何影响观点收敛、共识形成及信息效率，并揭示有限群体中长期分歧的条件与大规模群体中连接度与非线性之间的权衡。
Abstract This article proposes a model of non-Bayesian social learning in networks that accounts for heuristics and biases in opinion aggregation. The updating rules are represented by non-linear opinion aggregators from which we extract two extreme networks capturing strong and weak links. We provide graph-theoretic conditions for these networks that characterize opinions’ convergence, consensus formation, and efficient or biased information aggregation. Under these updating rules, agents may ignore some of their neighbours’ opinions, reducing the number of effective connections and inducing long-run disagreement for finite populations. For the wisdom of the crowd in large populations, we highlight a trade-off between how connected the society is and the non-linearity of the opinion aggregator. Our framework bridges several models and phenomena in the non-Bayesian social learning literature, thereby providing a unifying approach to the field.