用AI代理模拟专家判断，构建更可信的决策网络
Human AI Construction of Bayesian Networks for Operational Decision Support -- A Virtual Survey Approach
- 让多个AI代理扮演不同角色，基于情境估算概率
- 剔除异常值后，发现主观规范比自我效能影响更大
- 适合需要融合专家经验与数据的医疗决策场景
贝叶斯信念网络（BBNs）是不确定性下决策的强大工具，但结构构建与参数估计仍具挑战。当前研究需在依赖专家判断或使用大数据学习之间二选一。本文提出一种新方法：利用大语言模型生成一组扮演特定角色的AI代理，根据上下文估算概率，并采用截尾均值规则去除噪声。我们构建了六步BBN框架，用于建模替代医疗系统中患者咨询医生的意愿。结果表明，虽然自我效能看似关键，但其实际因果影响较小；相比之下，主观规范对行为意向的影响更强。最有效的策略是同时提升患者信心与社会规范影响力。
原文摘要 · Abstract (English)
Bayesian Belief Networks (BBNs) are powerful tools for decision-making under uncertainty. However, building their structures and estimating parameters are difficult. Currently, researchers must choose between relying on expert judgement or using large datasets to learn the structure and parameters of the network. We propose a new methodology using Large Language Models to bridge the gap between expert opinion and data-driven learning. This approach uses a panel of AI agents to estimate probabilities based on specific personas and context. We then apply a trimmed-mean rule to remove noise from these responses. We develop a six step BBN framework and illustrate it to model customer intention to consult a doctor in an alternative healthcare system. The model reveals that while self efficacy appears to be a major factor, its actual causal impact is small. In contrast, subjective norms have a much stronger effect in modelling customers' intention. The most effective strategy is to improve both confidence and community norms simultaneously.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。