Model-derived causal explanations are inherently constrained by hidden assumptions and context: The example of Baltic cod dynamics. (October 2022)

• Record Type: Journal Article
• Authors: Banitz, Thomas; Schlüter, Maja; Lindkvist, Emilie; Radosavljevic, Sonja; Johansson, Lars-Göran; Ylikoski, Petri; Martínez-Peña, Rodrigo; Grimm, Volker
• Is Part Of: Environmental modelling & software. Volume 156(2022)
• Publication Date: 2022-10
• Subjects: Causation -- Ecological models -- Social-ecological systems -- Context dependence -- Model comparison; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105489
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: 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.