Among the many functions of models, explanation is central to the functioning and aims of science. However, the discussions surrounding modeling and explanation in philosophy have largely remained separate from each other. This chapter seeks to bridge the gap by focusing on the puzzle of model-based explanation, asking how different philosophical accounts answer the following question: if idealizations and fictions introduce falsehoods into models, how can idealized and fictional models provide true explanations? The chapter provides a selective and critical overview (...) of the available strategies for solving this puzzle, mainly focusing on idealized models and how they explain. (shrink)

In the context of astrophysical modeling at the solar system scale, we investigate the modalities implied by taking into account different levels of detail at which phenomena can be considered. In particular, by framing the analysis in terms of the how-possibly/how-actually distinction, we address the debated question as to whether the degree of plausibility is tightly linked to the degree of detail. On the grounds of concrete examples, we argue that, also in the astrophysical context examined, this is not necessarily (...) the case. (shrink)

Sometimes, scientific models are either intended to or plausibly interpreted as representing nonactual but possible targets. Call this “hypothetical modeling”. This paper raises two epistemological challenges concerning hypothetical modeling. To begin with, I observe that given common philosophical assumptions about the scope of objective possibility, hypothetical models are fallible with respect to what is objectively possible. There is thus a need to distinguish between accurate and inaccurate hypothetical modeling. The first epistemological challenge is that no account of the epistemology of (...) hypothetical models seems to cohere with the most characteristic function of scientific modeling in general, i.e., surrogative representation. The second epistemological challenge is a version of “reliability challenges” familiar from other areas. There is a challenge to explain how hypothetical models could be a reliable guide to what is possible, given that they are not and cannot be compared against their nonactual targets and updated accordingly. I close with some brief remarks on possible solutions to these challenges. (shrink)

Several recent accounts of modeling have focused on the modal dimension of scientific inquiry. More precisely, it has been suggested that there are specific models and modeling practices that are best understood as being geared towards possibilities, a view recently dubbed modal modeling. But modalities encompass much more than mere possibility claims. Besides possibilities, modal modeling can also be used to investigate contingencies, necessities or impossibilities. Although these modal concepts are logically connected to the notion of possibility, not all models (...) are equal in their affordances for these richer modal inferences. This paper investigates the modal extent of selected models and argues that analyzing singular model-target pairings by themselves is typically not enough to explain their modal aptness or to identify the kinds of modalities they can be used to reason about. Furthermore, it is argued that some important concepts that are not explicitly modal - like biological robustness - can be understood modally through their relational nature to a background space of possibilities. In conclusion, it is suggested that the strategy of modal modeling is contrastive, situating particular possibilities in larger modal spaces and studying the structural relations within them. (shrink)