Modeling Mobile Health Users as Reinforcement Learning Agents

• URL: http://arxiv.org/abs/2212.00863v1
• Date: Thu, 1 Dec 2022 20:52:05 GMT
• Title: Modeling Mobile Health Users as Reinforcement Learning Agents
• Authors: Eura Shin, Siddharth Swaroop, Weiwei Pan, Susan Murphy, Finale Doshi-Velez
• Abstract summary: Mobile health (mHealth) technologies empower patients to adopt/maintain healthy behaviors in their daily lives. Without intervention, human decision making may be impaired. We show that different types of impairments imply different types of optimal intervention.
• Score: 27.300572343559285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile health (mHealth) technologies empower patients to adopt/maintain healthy behaviors in their daily lives, by providing interventions (e.g. push notifications) tailored to the user's needs. In these settings, without intervention, human decision making may be impaired (e.g. valuing near term pleasure over own long term goals). In this work, we formalize this relationship with a framework in which the user optimizes a (potentially impaired) Markov Decision Process (MDP) and the mHealth agent intervenes on the user's MDP parameters. We show that different types of impairments imply different types of optimal intervention. We also provide analytical and empirical explorations of these differences.

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