Bridging the Unavoidable A Priori: A Framework for Comparative Causal Modeling
Abstract
AI/ML models have rapidly gained prominence as innovations for solving previously unsolved problems and their unintended consequences from amplifying human biases. Advocates for responsible AI/ML have sought ways to draw on the richer causal models of system dynamics to better inform the development of responsible AI/ML. However, a major barrier to advancing this work is the difficulty of bringing together methods rooted in different underlying assumptions (i.e., Dana Meadow's "the unavoidable a priori"). This paper brings system dynamics and structural equation modeling together into a common mathematical framework that can be used to generate systems from distributions, develop methods, and compare results to inform the underlying epistemology of system dynamics for data science and AI/ML applications.
Cite
@article{arxiv.2511.21636,
title = {Bridging the Unavoidable A Priori: A Framework for Comparative Causal Modeling},
author = {Peter S. Hovmand and Kari O'Donnell and Callie Ogland-Hand and Brian Biroscak and Douglas D. Gunzler},
journal= {arXiv preprint arXiv:2511.21636},
year = {2025}
}
Comments
Presented at 43rd Conference of the International System Dynamics Society in Boston, United States