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AI Persuasion, Bayesian Attribution, and Career Concern of Doctors
Resources › Working Papers Working Paper This version: September 2024JEL: D02, D83, I10, I30, M16 Abstract This paper examines how AI persuades doctors when their diagnoses differ. Disagreements arise from two sources: attention differences, which are objective and play a complementary role to the doctor, and comprehension differences, which are subjective and act as substitutes.…
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Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity
Resources › Working Papers Working Paper This version: December 2024JEL: C10 Abstract In the standard data analysis framework, data is collected (once and for all), and then data analysis is carried out. However, with the advancement of digital technology, decision-makers constantly analyze past data and generate new data through their decisions. We model this as…
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Dynamic Selection in Algorithmic Decision-making
Resources › Working Papers Working Paper This version: September 2023JEL: C13, C26, C44 Abstract This paper identifies and addresses dynamic selection problems in online learning algorithms with endogenous data. In a contextual multi-armed bandit model, a novel bias (self-fulfilling bias) arises because the endogeneity of the data influences the choices of decisions, affecting the distribution…
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Seesaw Experimentation: A/B Tests with Spillovers
Resources › Working Papers Working Paper November 2024JEL: C12, D62, L25 Abstract This paper examines how a firm’s performance can decline despite consistently implementing successful A/B test innovations—a phenomenon we term “seesaw experimentation.” While these innovations improve the measured primary dimension, they create negative externalities in unmeasured secondary dimensions that exceed the gains. Using a…