Overcoming Challenges of Interpretive Structural Modeling with Large Language Models
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Computer Science > Machine Learning
Title:Overcoming Challenges of Interpretive Structural Modeling with Large Language Models
Abstract:Interpretive Structural Modeling (ISM) is a well-known process for multi-criteria decision making. The success of ISM over other methodologies is its ability to model causal relationships, the binary scale of factors, and resulting hierarchical representation. Traditionally, the modeling process is performed by repeated interactions with subject matter experts until consensus is reached. This process is tedious, labor-intense, and most importantly limits the ability of ISM to scale to studies with hundreds of variables. Drawing on existing work of causal graph discovery with large language models (LLM) as imperfect experts, this work explores an integrated LLM-ISM approach for ISM. Pairwise, k-wise, rowwise, and full graph discovery methodologies are compared and evaluated. It is shown that causal graph discovery methods for ISM perform best using rowwise (SHD=160, F1-score=0.77) and full graph methods (SHD=135, F1-score=0.73).
| Comments: | This preprint has not undergone peer review or any post-submission improvements or corrections |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.02254 [cs.LG] |
| (or arXiv:2610.02254v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02254
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