Human AI Construction of Bayesian Networks for Operational Decision Support -- A Virtual Survey Approach
Researchers propose a novel method using Large Language Models to build Bayesian Belief Networks, employing a panel of AI agents to estimate probabilities based on personas and context, and applying a trimmed-mean rule to reduce noise. A six-step framework is illustrated on customer intention to consult a doctor in an alternative healthcare system, revealing that subjective norms have a much stronger effect than self-efficacy, and the most effective strategy is to improve both confidence and community norms simultaneously.
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[Submitted on 13 Jul 2026]
Title:Human AI Construction of Bayesian Networks for Operational Decision Support -- A Virtual Survey Approach
View a PDF of the paper titled Human AI Construction of Bayesian Networks for Operational Decision Support -- A Virtual Survey Approach, by Kumar Rahul (1) and 6 other authors
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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. 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.
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.14141 [cs.AI]
(or arXiv:2607.14141v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.14141
arXiv-issued DOI via DataCite
Submission history
From: Shovan Chowdhury [view email] [v1] Mon, 13 Jul 2026 06:15:07 UTC (739 KB)
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