We present a new framework in which agents with limited and heterogeneous cognitive ability—modeled as finite depths of reasoning—learn from their neighbors in social networks. Each agent tracks old information using Bayes-like formulas, and uses a shortcut when reasoning on behalf of multiple neighbors exceeds her cognitive ability. Surprisingly, agents with moderate cognitive ability are capable of partialing out old information and learn correctly in social quilts, a tree-like union of cliques (fully-connected subnetworks). Agents with low cognitive ability may fail to learn in any network, even when they receive a large number of signals. We also identify a critical cutoff level of cognitive ability, determined by the network structure, above which an agent's learning outcome remains the same even when her cognitive ability increases.

我们提出了一个新框架，其中具有有限和异构认知能力的代理（建模为有限的推理深度）向社交网络中的邻居学习。每个代理使用类似贝叶斯的公式跟踪旧信息，并在代表多个邻居进行推理超出其认知能力时使用捷径。令人惊讶的是，具有中等认知能力的智能体能够偏出旧信息并在社交被子中正确学习，一个类的树状联合（完全连接的子网络）。认知能力低的代理可能无法在任何网络中学习，即使他们收到大量信号。我们还确定了认知能力的临界临界水平，由网络结构决定，高于该临界水平，即使智能体的认知能力增加，其学习结果也保持不变。