Truth, understanding, and normativity in scientific models

Abstract Scientific models often contain assumptions known not to be true. Despite being false representations, models provide us with a key understanding of phenomena. What is more, the falsehoods that figure in models are in many cases central to them, and there is no available alternative to their use. If falsehoods play such an irreplaceable role in our understanding of phenomena, it would seem that truth is not a key concern of scientific modeling. In this paper, I assess the prospects and challenges of reconciling truth and understanding in scientific modeling. More specifically, I review a thesis recently emerging in the literature, what I shall call the Derivation Thesis (DT), according to which we use models to derive true information. First, I examine different versions of the thesis and develop what I take to be its most promising formulation (what I call the generalized DT). Second, I discuss a serious challenge to the generalized DT. I consider a thought experiment in which an unreliable astrological model gives true explanations by fluke. This scenario challenges the idea that models can provide genuine understanding by generating truths. In response, I argue that genuine scientific models also fulfill a specific normative role that epistemically lucky models lack (what I call the normative generalized DT). I test this hypothesis by analysing how the Ideal Gas Law advances scientific understanding of real gases.