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AI Persuasion, Bayesian Attribution, and Career Concern of Doctors
Author: Hanzhe Li, Jin Li, Ye Luo and Xiaowei Zhang | Disagreements between doctors and AI can arise from two sources: attention differences (complementarities) and comprehension differences (substitutes). AI’s interpretability influences how doctors attribute these sources and their willingness to change their minds. Surprisingly, uninterpretable AI can be more persuasive by allowing doctors to partially…
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Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity
Author: Jin Li, Ye Luo, Zigan Wang and Xiaowei Zhang | We identify a new type of bias in data analysis, termed reinforcement bias, and develop IV-based reinforcement learning algorithms to correct it. Additionally, we establish their theoretical properties by integrating them into a stochastic approximation framework. Our analysis accommodates iterate-dependent Markovian structures and, therefore,…
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Dynamic Selection in Algorithmic Decision-making
Author: Jin Li, Ye Luo and Xiaowei Zhang | 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 of future data to be collected and analyzed. Our proposed IV-based algorithms correct this bias, obtaining true parameter values and…
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Seesaw Experimentation
Author: Jin Li, Ye Luo and Xiaowei Zhang | We show how a firm’s performance can decline despite consistently implementing successful A/B test innovations—a phenomenon we term “seesaw experimentation.” An improvement in the measured primary dimension can create negative externalities in unmeasured secondary dimensions that exceed the gains. Using a multivariate normal distribution model, we…