We present an account of how idealised models provide scientific understanding that is based on the notion of stability: a model provides understanding of a target behaviour when both the model and the target’s perfect model are in a class of models over which that behaviour is stable. The class is characterised in terms of what we call the model’s noetic core, which contains the features that are indispensable to both the model’s and the target’s behaviour. The account is factivist because it insists that models must get those aspects of the target that it aims to understand right, but it disagrees with extant factivist accounts about how models achieve this.

Epidemiological models of the transmission of SARS-CoV-2 played an important role in guiding the decisions of policy-makers during the pandemic. Such models provide output projections, in the form of time -series of infections, hospitalisations, and deaths, under various different parameter and scenario assumptions. In this paper I caution against handling these outputs uncritically: raw model-outputs should not be presented as direct projections in contexts where modelling results are required to support policy -decisions. I argue that model uncertainty should be handled and communicated transparently. Drawing on methods used by climate scientists in the fifth IPCC report I suggest that this can be done by: attaching confidence judgements to projections based on model results; being transparent about how multi-model ensembles are supposed to deal with such uncertainty; and using expert judgement to 'translate' model-outputs into projections about the actual world. In a slogan: tell me what you think (and why), not (just) what your models say. I then diffuse the worry that this approach infects model-based policy advice with some undesirably subjective elements, and explore how my discussion fares if one thinks the role of a scientific advisor is to prompt action, rather than communicate information.

James Nguyen argues that certain works of literary fiction hold cognitive value about the actual world. This chapter discusses two ways of understanding how we can learn from such works. First, just like experiments (both physical and in thought), works of literary fiction can describe appropriately constrained systems where an explanatory factor can be observed to act in isolation, shielded from extraneous influence. Through this, we can come to better understand the contribution this factor makes in actual systems. Second, through works of literary fiction, we can investigate how explanations that we suspect hold in the actual world fare in novel systems. So when faced with competing hypotheses about what explains some set of observations in the actual world, learning that one of them (and not the others) acts as an explanation in an appropriate world of literary fiction, serves to confirm that hypothesis at the expense of its competitors. These suggestions add (some) detail and precision to the way in which we can learn from literary fiction in a manner that encourages further dialogue between the philosophy of art and the philosophy of science.

This white paper outlines the plans of the History Philosophy Culture Working Group of the Next Generation Event Horizon Telescope Collaboration.

This entry explores how scientific models represent what is possible; how they are justified; and their functions. Two different approaches to these issues are discussed: the first concerns models “embedded” in a well-confirmed theory; the second concerns models not so embedded. When the former models represent possibilities, they provide us with information about what is possible according to the theory in which they are embedded. They are justified to the extent that the theory is actually confirmed. Models of the second kind cannot rely on theoretical support in the same way. Yet, scientists regularly extract modal information from such models, and they hold some epistemic merit. What could underwrite their justification is explored. Finally, various functions played by scientific models that represent possibilities are considered.

This Element presents a philosophical exploration of the notion of scientific representation. It does so by focussing on an important class of scientific representations, namely scientific models. Models are important in the scientific process because scientists can study a model to discover features of reality. But what does it mean for something to represent something else? This is the question discussed in this Element. The authors begin by disentangling different aspects of the problem of representation and then discuss the dominant accounts in the philosophical literature: the resemblance view and inferentialism. They find them both wanting and submit that their own preferred option, the so-called DEKI account, not only eschews the problems that beset these conceptions, but further provides a comprehensive answer to the question of how scientific representation works.

What is the primary intellectual aim of philosophy? The standard view is that philosophy aims to provide true answers to philosophical questions. But if our aim is to settle controversy by answering such questions, our discipline is an embarrassing failure. Moreover, taking philosophy to aim at providing true answers to these questions leads to a variety of puzzles: How do we account for philosophical expertise? How is philosophical progress possible? Why do job search committees not care about the truth or falsity of a candidate’s philosophical views? We argue that philosophy does not aim at discovering true answers to philosophical questions. Instead, its primary intellectual aim is understanding. We argue that many familiar aspects of philosophy become intelligible once we accept this hypothesis.