Human AI Construction of Bayesian Networks for Operational Decision Support — A Virtual Survey Approach

人机协作构建贝叶斯网络以支持运营决策——一种虚拟调查方法

Abstract: 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.

摘要： 贝叶斯信念网络（BBNs）是处理不确定性决策的强大工具。然而，构建其结构和估计参数的过程十分困难。目前，研究人员必须在依赖专家判断或使用大型数据集来学习网络结构与参数之间做出选择。

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.

我们提出了一种利用大语言模型的新方法，旨在弥合专家意见与数据驱动学习之间的鸿沟。该方法通过一组人工智能代理（AI agents），基于特定的人格设定和背景来估计概率。随后，我们应用截尾平均法（trimmed-mean rule）来消除这些反馈中的噪声。

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.

我们开发了一个六步贝叶斯网络框架，并将其应用于模拟客户在替代医疗系统中咨询医生的意愿。模型显示，尽管自我效能感似乎是一个主要因素，但其真实的因果影响较小。相比之下，主观规范在模拟客户意愿方面具有更强的影响力。最有效的策略是同时提升客户的自信心与社区规范。

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Paper Details:

• Authors: Kumar Rahul, Shovan Chowdhury, et al.
• Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
• arXiv ID: 2607.14141
• Submission Date: 13 Jul 2026

论文详情：

• 作者： Kumar Rahul, Shovan Chowdhury 等
• 学科分类： 人工智能 (cs.AI)；机器学习 (cs.LG)
• arXiv 编号： 2607.14141
• 提交日期： 2026年7月13日