人机协作的贝叶斯框架：互补性与相关性忽视
A Bayesian Framework for Human-AI Collaboration: Complementarity and Correlation Neglect
AI总结：
本文提出一个贝叶斯框架，研究人类与AI协作中信息重叠和相关性忽视对决策的影响，分析了增强、损害、互补性和自动化等交互模式。
摘要：
我们开发了一个决策理论模型，研究AI辅助何时会提高或损害人类决策质量。人类决策者观察到私人信息并接收AI系统的建议，但可能不完美地结合这些信号。我们证明AI辅助的影响可以分解为两个主要因素：AI提供的信息价值，以及人类如何使用AI建议带来的行为扭曲。我们分析的核心是人类与AI知识之间信息重叠的微观基础度量。我们研究了一种实际相关的不完美决策形式——相关性忽视，即人类将AI建议视为与自身信息独立，尽管有共同证据。在该模型中，我们刻画了信息重叠和AI能力如何塑造人类与AI交互的领域，包括增强、损害、互补性和自动化，并得出关键见解。
英文摘要：
We develop a decision-theoretic model of human-AI interaction to study when AI assistance improves or impairs human decision-making. A human decision-maker observes private information and receives a recommendation from an AI system, but may combine these signals imperfectly. We show that the effect of AI assistance decomposes into two main forces: the marginal informational value of the AI beyond what the human already knows, and a behavioral distortion arising from how the human uses the AI's recommendation. Central to our analysis is a micro-founded measure of informational overlap between human and AI knowledge. We study an empirically relevant form of imperfect decision-making -- correlation neglect -- whereby humans treat AI recommendations as independent of their own information despite shared evidence. Under this model, we characterize how overlap and AI capabilities shape the Human-AI interaction regime between augmentation, impairment, complementarity, and automation, and draw key insights.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
