Robustness Reexamined: Unpacking the Limits of Model-Based Robustness Analysis as Explanatory Reasoning

Abstract In science, obtaining the same result through different means is often taken to support a hypothesis. Bayesian approaches have long aimed to clarify the logic behind this practice, commonly known as robustness analysis (RA), typically by appealing to probabilistic independence to capture evidential diversity. Schupbach’s (2018. “Robustness Analysis as Explanatory Reasoning.” The British Journal for the Philosophy of Science 69 (1): 275–300.) explanatory account offers a novel alternative, particularly suited to empirically driven cases. However, this paper questions whether the account can be successfully extended to model-based RAs. I argue that its application in such contexts is more problematic than often acknowledged and that it fails to accommodate a broader class of model-based RAs than previously assumed.