Computer simulations (CSs) are frequently used in scientific practice to explain complex phenomena across disciplines. Despite their widespread use, the legitimacy of CS-based explanations remains a matter of critical debate in philosophy. This is due, among other things, to the differences (e.g., material differences) between computer programs and real-world target systems. Besides the question of whether CSs can provide scientific explanations at all, it still needs to be examined how they are integrated into scientific explanation.

This project aims to investigate the explanatory role of CSs and to develop a basis for their explanatory power. The project is divided into three steps:

1. Previous studies mostly focused on the analogy between CSs and target systems, which may not be necessary when considering explanations. The project therefore proposes that CSs and explanatory models share the same epistemic status, while emphasizing that CSs as such are not complete explanations but rather provide “raw explanations” that contain both explanatory and non-explanatory components.
2. I then systematically analyze how CSs as “raw explanations” constitute scientific explanations in practice and identify which components contribute to the explanation and how they are situated within existing explanatory frameworks. This will further our understanding of scientific explanatory practice.
3. Third, CSs are powerful at expanding the scope of explanatory research by identifying relations and relevant structures, which are involved in both causal and non-causal modes of explanation. As non-causal explanations have not received enough attention to date, and their nature remains unclear. To close this gap, the project introduces “explanatory grounding” as a candidate account to capture the dependence patterns in non-causal explanations and to open up an additional dimension of scientific explanation.

This project is carried out in close collaboration with the scientists of the “RTG 2450 – Tailored Scale-Bridging Approaches to Computational Nanoscience” research training group and draws on its case studies. It is both philosophically and scientifically relevant. Philosophically, it enriches the debate on scientific explanations by introducing the concept of “raw explanations” and developing the notion of “explanatory grounding. ”The project thus goes beyond purely causal approaches and enables a systematic presentation of non-causal explanations. From a scientific perspective, it examines the explanatory function of computer simulations, legitimizes simulation-based explanations, and strengthens the collaboration between philosophers and scientists. Furthermore, it contributes to a better understanding of how simulations generate knowledge across disciplines and provides conceptual tools for analyzing complex systems where causal explanations alone are insufficient.

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