超越无界信念：社会学习中偏好与信息如何相互作用
Beyond Unbounded Beliefs: How Preferences and Information Interplay in Social Learning
AI总结：
本文研究社交网络序贯学习中社会何时采取正确行动，提出联合偏好与信息的“可排除性”条件，并给出一维和多维状态下的两类满足结构，证明无界信念并非必要。
摘要：
社会何时才能通过观察性学习最终获知真相，或采取正确行动？在一个关于社交网络上序贯学习的一般模型中，我们识别出一个被称为可排除性的简单学习条件。可排除性是主体偏好与其信息的联合性质。我们构建了两类共同满足可排除性的偏好与信息：（i）对于一维状态，具有单交叉差异的偏好以及一个新的信息条件——方向无界信念；（ii）对于多维状态，中间偏好与次指数位置平移信息。这些应用表明，在存在多个状态时，“无界信念”不仅不是学习所必需的，而且与正态信息等常见信息结构不相容。无界信念要求单个主体能够识别正确行动；相反，可排除性只要求单个主体必须能够排除任何错误行动，即使她无法采取正确行动。
英文摘要：
When does society eventually learn the truth, or take the correct action, via observational learning? In a general model of sequential learning over social networks, we identify a simple condition for learning dubbed excludability. Excludability is a joint property of agents' preferences and their information. We develop two classes of preferences and information that jointly satisfy excludability: (i) for a one-dimensional state, preferences with single-crossing differences and a new informational condition, directionally unbounded beliefs; and (ii) for a multi-dimensional state, intermediate preferences and subexponential location-shift information. These applications exemplify that with multiple states "unbounded beliefs" is not only unnecessary for learning, but incompatible with familiar informational structures like normal information. Unbounded beliefs demands that a single agent can identify the correct action. Excludability, on the other hand, only requires that a single agent must be able to displace any wrong action, even if she cannot take the correct action.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
