SIAM Journal on Applied Dynamical Systems, Volume 19, Issue 3, Page 1884-1919, January 2020.
To make decisions we are guided by the evidence we collect and the opinions of friends and neighbors. How do we combine our private beliefs with information we obtain from our social network? To understand the strategies humans use to do so, it is useful to compare them to observers that optimally integrate all evidence. Here we derive network models of rational (Bayes optimal) agents who accumulate private measurements and observe the decisions of their neighbors to make an irreversible choice between two options. The resulting information exchange dynamics has interesting properties: When decision thresholds are asymmetric, the absence of a decision can be increasingly informative over time. In a recurrent network of two agents, the absence of a decision can lead to a sequence of belief updates akin to those in the literature on common knowledge. On the other hand, in larger networks a single decision can trigger a cascade of agreements and disagreements that depend on the private information agents have gathered. Our approach provides a bridge between social decision making models in the economics literature, which largely ignore the temporal dynamics of decisions, and the single-observer evidence accumulator models used widely in neuroscience and psychology.

中文翻译：

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社交网络上的贝叶斯证据积累

SIAM 应用动力系统杂志，第 19 卷，第 3 期，第 1884-1919 页，2020 年 1 月。
为了做出决定，我们以收集到的证据以及朋友和邻居的意见为指导。我们如何将我们的私人信仰与我们从社交网络获得的信息结合起来？要了解人类这样做的策略，将它们与最佳整合所有证据的观察者进行比较是有用的。在这里，我们推导出理性（贝叶斯最优）代理的网络模型，这些代理累积私人测量值并观察其邻居的决策，以便在两个选项之间做出不可逆的选择。由此产生的信息交换动态具有有趣的特性：当决策阈值不对称时，随着时间的推移，缺乏决策可能会提供越来越多的信息。在两个代理的循环网络中，没有做出决定会导致一系列信念更新，类似于关于常识的文献中的更新。另一方面，在更大的网络中，一个单一的决定可能会引发一连串的协议和分歧，这些协议和分歧取决于代理收集的私人信息。我们的方法在经济学文献中的社会决策模型（很大程度上忽略了决策的时间动态）与神经科学和心理学中广泛使用的单一观察者证据累积模型之间架起了一座桥梁。