UC Berkeley

This dissertation focuses on human behavior modeling and incentive designs for online platforms, specifically in two application domains: collective intelligence platforms and healthcare intervention platforms. In the last two decades, wide adoption of internet facilitates the emergence of online collective intelligence platforms. In the mean time, the fast developing mobile technology enables real-time healthcare interventions. This dissertation is a step towards implementing human behavior modeling through machine learning and optimization techniques to enhance the efficacy of these online platforms. Case studies show that human behavior modeling combined with smart incentive designs have the potential to improve the performance of such online platforms.

In the first part of this dissertation, I present two collective intelligence platforms: M-CAFE and DebateCAFE. M-CAFE is a mobile-friendly platform that encourages students to check in weekly to numerically assess their course performance, provide textual ideas about how the course might be improved, and rate ideas suggested by other students. For instructors, M-CAFE displays ongoing trends and highlights potentially valuable ideas based on collaborative filtering. M-CAFE is complementary to existing platforms, such as Piazza and stackExchange and allows students to step back and consider their own performance and the performance of their instructors, filling the gap between voluminous transcripts from existing platforms and a one-time-only, end-of-course evaluation. DebateCAFE is an online deliberation platform that introduces a novel incentive mechanism to encourage participants to articulate persuasive arguments on both sides of a complex issue. It uses a combination of uncertainty sampling and collaborative filtering to mitigate bias from selective exposure and highlight/rank the persuasive arguments. Furthermore, DebateCAFE assigns a score to each participant based on the lower of the Wilson scores of the two arguments entered to encourage strong arguments for opposing opinions. Both platforms were built upon the CAFE framework, which is based on Opinion Space.

In the second part of this dissertation, I describe a novel mobile phone application (app) called CalFit and introduce the Discontinuation Prediction Score (DiPS) for non-adherence prediction. CalFit implements important behavior-change features like dynamic goal setting and self-monitoring. Specifically, CalFit uses a reinforcement learning algorithm to generate personalized daily step goals that are challenging but attainable. Two empirical studies with university staff and students indicate that dynamic goal setting is effective in promoting physical activity. Despite the success, I identify that low adherence is a major drawback for mobile-based interventions like ours. Therefore, I further developed the Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse for the upcoming week for each subject. I present two versions of DiPS using logistic regression and support vector machine methods to demonstrate that DiPS has potential to accurately predict exercise relapse and efficiently allocate resources to improve compliance.