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Model-derived causal explanations are inherently constrained by hidden assumptions and context: The example of Baltic cod dynamics.

Authors :
Banitz, Thomas
Schlüter, Maja
Lindkvist, Emilie
Radosavljevic, Sonja
Johansson, Lars-Göran
Ylikoski, Petri
Martínez-Peña, Rodrigo
Grimm, Volker
Source :
Environmental Modelling & Software. Oct2022, Vol. 156, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

Models are widely used for investigating cause-effect relationships in complex systems. However, often different models yield diverging causal claims about specific phenomena. Therefore, critical reflection is needed on causal insights derived from modeling. As an example, we here compare ecological models dealing with the dynamics and collapse of cod in the Baltic Sea. The models addressed different specific questions, but also vary widely in system conceptualization and complexity. With each model, certain ecological factors and mechanisms were analyzed in detail, while others were included but remained unchanged, or were excluded. Model-based causal analyses of the same system are thus inherently constrained by diverse implicit assumptions about possible determinants of causation. In developing recommendations for human action, awareness is needed of this strong context dependence of causal claims, which is often not entirely clear. Model comparisons can be supplemented by integrating findings from multiple models and confronting models with multiple observed patterns. • We reviewed how ecological models are used to study causes of complex phenomena. • Models for the same system were developed and analyzed in quite different ways. • Model-based causal claims strongly depend on context, which is often not entirely clear. • Awareness of hidden assumptions improves clarity and robustness of causal findings. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13648152
Volume :
156
Database :
Academic Search Index
Journal :
Environmental Modelling & Software
Publication Type :
Academic Journal
Accession number :
158888531
Full Text :
https://doi.org/10.1016/j.envsoft.2022.105489