Six LLMs show an equivalence-versus-process paradox: some match human surface behaviors but few replicate human decision pathways, so GABMs need dual-level validation before use in logistics research.
semopy: A Python package for Structural Equation Modeling
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abstract
Structural equation modelling (SEM) is a multivariate statistical technique for estimating complex relationships between observed and latent variables. Although numerous SEM packages exist, each of them has limitations. Some packages are not free or open-source; the most popular package not having this disadvantage is $\textbf{lavaan}$, but it is written in R language, which is behind current mainstream tendencies that make it harder to be incorporated into developmental pipelines (i.e. bioinformatical ones). Thus we developed the Python package $\textbf{semopy}$ to satisfy those criteria. The paper provides detailed examples of package usage and explains it's inner clockworks. Moreover, we developed the unique generator of SEM models to extensively test SEM packages and demonstrated that $\textbf{semopy}$ significantly outperforms $\textbf{lavaan}$ in execution time and accuracy.
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cs.MA 1years
2025 1verdicts
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Validating Generative Agent-Based Models for Logistics and Supply Chain Management Research
Six LLMs show an equivalence-versus-process paradox: some match human surface behaviors but few replicate human decision pathways, so GABMs need dual-level validation before use in logistics research.