用深度聚类估计理性疏忽效用函数用于框架-在YouTube交战动态中的应用
Estimating Rationally Inattentive Utility Functions with Deep Clustering for Framing - Applications in YouTube Engagement Dynamics
摘要：
我们考虑一个涉及行为经济学和机器学习的框架。理性疏忽的贝叶斯主体基于其后验分布、效用函数和信息获取成本（广义Shannon互信息的Renyi散度）进行决策。通过观察这些决策，观察者如何估计效用函数和信息获取成本？使用深度学习，我们估计框架信息（必要的外部特征），决定代理的注意力策略。然后，我们提出了一个基于偏好的逆强化学习算法来测试理性疏忽：代理是效用最大化，注意力最大化，以及是否存在信息成本函数，合理化的数据？该测试施加了Renyi互信息约束，该约束影响代理如何选择注意力策略以最大化其预期效用。该测试提供了建设性的效用函数和信息获取成本的代理估计。我们在一个大规模的YouTube数据集上展示了这些方法，用于表征用户的评论行为。
英文摘要：
We consider a framework involving behavioral economics and machine learning. Rationally inattentive Bayesian agents make decisions based on their posterior distribution, utility function and information acquisition cost Renyi divergence which generalizes Shannon mutual information). By observing these decisions, how can an observer estimate the utility function and information acquisition cost? Using deep learning, we estimate framing information (essential extrinsic features) that determines the agent's attention strategy. Then we present a preference based inverse reinforcement learning algorithm to test for rational inattention: is the agent an utility maximizer, attention maximizer, and does an information cost function exist that rationalizes the data? The test imposes a Renyi mutual information constraint which impacts how the agent can select attention strategies to maximize their expected utility. The test provides constructive estimates of the utility function and information acquisition cost of the agent. We illustrate these methods on a massive YouTube dataset for characterizing the commenting behavior of users.
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