Many life-influencing social networks are characterized by considerable informational isolation. People within a community are far more likely to share beliefs than people who are part of different communities. The spread of useful information across communities is impeded by echo chambers (far greater connectivity within than between communities) and filter bubbles (more influence of beliefs by connected neighbors within than between communities). We apply the tools of network analysis to organize our understanding of the spread of beliefs across modularized communities and to predict the effect of individual and group parameters on the dynamics and distribution of beliefs. In our Spread of Beliefs in Modularized Communities (SBMC) framework, a stochastic block model generates social networks with variable degrees of modularity, beliefs have different observable utilities, individuals change their beliefs on the basis of summed or average evidence (or intermediate decision rules), and parameterized stochasticity introduces randomness into decisions. SBMC simulations show surprising patterns; for example, increasing out-group connectivity does not always improve group performance, adding randomness to decisions can promote performance, and decision rules that sum rather than average evidence can improve group performance, as measured by the average utility of beliefs that the agents adopt. Overall, the results suggest that intermediate degrees of belief exploration are beneficial for the spread of useful beliefs in a community, and so parameters that pull in opposite directions on an explore-exploit continuum are usefully paired.

中文翻译：

---

信仰在部分模块化社区中的传播。


许多影响生活的社交网络都具有相当程度的信息隔离的特点。一个社区内的人们比不同社区的人们更有可能分享信仰。有用信息在社区之间的传播受到回声室（社区内部的连通性比社区之间的连通性大得多）和过滤气泡（社区内相连邻居的信仰影响比社区之间更大）的阻碍。我们应用网络分析工具来组织我们对模块化社区中信仰传播的理解，并预测个人和群体参数对信仰动态和分布的影响。在我们的模块化社区信仰传播（SBMC）框架中，随机块模型生成具有不同模块化程度的社交网络，信仰具有不同的可观察效用，个人根据总和或平均证据（或中间决策规则）改变他们的信仰，参数化随机性将随机性引入决策中。 SBMC 模拟显示出令人惊讶的模式；例如，增加外群体连通性并不总是能提高群体绩效，增加决策的随机性可以提高绩效，而总结而不是平均证据的决策规则可以提高群体绩效（通过代理人所采用的信念的平均效用来衡量）。总体而言，结果表明，中等程度的信念探索有利于社区中有用信念的传播，因此在探索-利用连续体上向相反方向拉动的参数可以有效地配对。