统一显示性偏好与显示性理性疏忽
Unifying Revealed Preference and Revealed Rational Inattention
AI总结：
本文通过等价映射统一了经济理论中的显示性理性疏忽与经典显示性偏好，将显示性偏好的拟合优度鲁棒性度量扩展至理性疏忽，并在约14万条YouTube视频数据上验证了用户参与度高度符合理性疏忽模型。
摘要：
本文统一了经济理论中的两个关键结果，即显示性理性疏忽和经典显示性偏好。显示性理性疏忽用于检验贝叶斯决策者信息获取的理性。另一方面，经典显示性偏好用于检验在已知预算约束下的效用最大化。我们的第一个结果是等价性结果——我们通过在两种设置中对决策参数的等价映射和决策空间上收益单调性的偏序，统一了显示性理性疏忽和显示性偏好。其次，我们在计算上利用该统一结果，将文献中显示性偏好检验拟合优度的鲁棒性度量扩展到显示性理性疏忽。这一扩展有助于量化贝叶斯决策者的行为在多大程度上满足理性疏忽。最后，我们在一个真实的 YouTube 数据集上阐明了该统一结果的意义，该数据集包含约 140,000 个视频的缩略图、标题和用户参与度元数据。我们利用从深度自编码器（即学习元数据低维特征的深度神经网络）提取的 YouTube 元数据特征，计算了显示性偏好文献中鲁棒性度量的贝叶斯类比。计算出的鲁棒性值表明，YouTube 用户参与度非常符合理性疏忽模型。我们所有的数值实验均可完全复现。
英文摘要：
This paper unifies two key results from economic theory, namely, revealed rational inattention and classical revealed preference. Revealed rational inattention tests for rationality of information acquisition for Bayesian decision makers. On the other hand, classical revealed preference tests for utility maximization under known budget constraints. Our first result is an equivalence result - we unify revealed rational inattention and revealed preference through an equivalence map over decision parameters and partial order for payoff monotonicity over the decision space in both setups. Second, we exploit the unification result computationally to extend robustness measures for goodness-of-fit of revealed preference tests in the literature to revealed rational inattention. This extension facilitates quantifying how well a Bayesian decision maker's actions satisfy rational inattention. Finally, we illustrate the significance of the unification result on a real-world YouTube dataset comprising thumbnail, title and user engagement metadata from approximately 140,000 videos. We compute the Bayesian analog of robustness measures from revealed preference literature on YouTube metadata features extracted from a deep auto-encoder, i.e., a deep neural network that learns low-dimensional features of the metadata. The computed robustness values show that YouTube user engagement fits the rational inattention model remarkably well. All our numerical experiments are completely reproducible.
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。
