{"ID":2836539,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21636","arxiv_id":"2511.21636","title":"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.","short_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/M...","url_abs":"https://arxiv.org/abs/2511.21636","url_pdf":"https://arxiv.org/pdf/2511.21636v1","authors":"[\"Peter S. Hovmand\",\"Kari O'Donnell\",\"Callie Ogland-Hand\",\"Brian Biroscak\",\"Douglas D. Gunzler\"]","published":"2025-11-26T18:08:20Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"stat.AP\",\"stat.CO\",\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}