Bridging the Unavoidable A Priori: A Framework for Comparative Causal Modeling
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908677219287040 |
|---|---|
| author | Hovmand, Peter S. O'Donnell, Kari Ogland-Hand, Callie Biroscak, Brian Gunzler, Douglas D. |
| author_facet | Hovmand, Peter S. O'Donnell, Kari Ogland-Hand, Callie Biroscak, Brian Gunzler, Douglas D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging the Unavoidable A Priori: A Framework for Comparative Causal Modeling Hovmand, Peter S. O'Donnell, Kari Ogland-Hand, Callie Biroscak, Brian Gunzler, Douglas D. Artificial Intelligence Applications Computation Methodology Machine Learning 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. |
| title | Bridging the Unavoidable A Priori: A Framework for Comparative Causal Modeling |
| topic | Artificial Intelligence Applications Computation Methodology Machine Learning |
| url | https://arxiv.org/abs/2511.21636 |