用于运营决策支持的贝叶斯网络的人工AI构建——一种虚拟调查方法
Human AI Construction of Bayesian Networks for Operational Decision Support -- A Virtual Survey Approach
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中文总结 AI 辅助
研究如何构建贝叶斯网络用于运营决策支持，提出利用大语言模型的新方法，通过人工智能代理估计概率并去噪，开发六步框架，以客户咨询医生意图建模为例，揭示因素影响，得出有效策略。
中文摘要 AI 辅助
贝叶斯信念网络（BBNs）是不确定性决策的有力工具，但构建其结构和估计参数具有难度。目前研究者需在依赖专家判断或使用大型数据集学习网络结构和参数之间抉择。本文提出一种利用大语言模型弥合专家意见与数据驱动学习之间差距的新方法。该方法使用一组人工智能代理基于特定角色和背景估计概率，再应用截尾均值规则去除这些响应中的噪声。还开发了一个六步BBN框架，并通过对替代医疗系统中客户咨询医生意图建模进行说明。模型显示自我效能虽看似是主要因素，但其实际因果影响较小，相比之下，主观规范对建模客户意图有更强影响，最有效的策略是同时提高信心和社区规范。
英文摘要
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.
