Psychological Insights into Decisions Relevant to Public Policy

This dissertation explores the integration of cognitive and behavioral sciences insights into policy-relevant domains, focusing on labor market discrimination, online teaching habits, and digital math education. The research comprises three studies: 1. An application of reinforcement learning models to analyze teachers' decision-making processes on the Zearn online math-teaching platform. This study demonstrates how computational models derived from computational psychology can capture complex, adaptive teaching behaviors and their impact on student outcomes. 2. A two-phase study combining data exploration with a large-scale field experiment to design and evaluate behavioral interventions for improving student learning outcomes on the Zearn platform. This research showcases the potential of data-driven approaches in developing effective educational interventions. 3. A meta-analysis of experimental correspondence studies investigating discrimination in North American labor markets. This study examines how perceptions of warmth and competence impact callback rates, providing insights into the mechanisms underlying discrimination. This work demonstrates the feasibility and value of bridging cognitive and behavioral sciences with policy-making through innovative models and methodologies. The findings presented in this dissertation contribute to a more comprehensive understanding of how these fields can collaboratively tackle complex challenges in discrimination, education, and digital learning environments. Additionally, this research establishes a groundwork for future studies at the intersection of cognition, behavior, and public policy.