2018/12/23 by William Hoiles, Vikram Krishnamurthy, Hoiles, William +1
Decision Sciences · Physics and Astronomy · Social Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Misinformation and Its Impacts #Opinion Dynamics and Social Influence
paper · pdf · doi:10.48550/arxiv.1812.09640
openalex publication_date 2018/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.