Consensus and Disagreement: Information Aggregation under (Not So) Naive Learning
中文摘要
我们研究网络中非贝叶斯信息聚合的一个模型。个体在 Friedkin-Johnsen 型聚合规则中非合作地进行选择,以最大化自身收益。当且仅当信息传递无噪声时,均衡中选择的是 DeGroot 规则,并由此达成共识。在信息传递存在噪声时,尽管某些分歧不可避免,最优的规则选择却会放大分歧:即使噪声很小,个体在每一期都会对自己的初始观点赋予相当大的权重,从而加剧分歧。我们借助这一框架来思考规则的均衡选择与社会有效选择之间的对比,及其与群体间观点极化的联系。
Abstract
We explore a model of non-Bayesian information aggregation in networks. Agents noncooperatively choose among Friedkin-Johnsen-type aggregation rules to maximize payoffs. The DeGroot rule is chosen in equilibrium if and only if there is noiseless information transmission, leading to consensus. With noisy transmission, while some disagreement is inevitable, the optimal choice of rule amplifies the disagreement: even with little noise, individuals place substantial weight on their own initial opinion in every period, exacerbating the disagreement. We use this framework to think about equilibrium versus socially efficient choice of rules and its connection to polarization of opinions across groups.