# Estimating Rationally Inattentive Utility Functions with Deep Clustering for Framing - Applications in YouTube Engagement Dynamics

## TL;DR

This paper introduces a deep learning framework to estimate utility functions and information costs of rationally inattentive agents, applying it to analyze YouTube user commenting behavior and decision-making processes.

## Contribution

It develops a novel inverse reinforcement learning method incorporating Renyi divergence to estimate attention strategies and utility functions from behavioral data.

## Key findings

- Successfully applied to YouTube data to characterize user commenting behavior.
- Provides a constructive way to estimate utility and information costs from observed decisions.
- Demonstrates the importance of framing and attention strategies in behavioral modeling.

## Abstract

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.